esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Unlike other researchers, surcharge stresses were used as a component of the equation proposed for the arching theory. In addition, the lateral earth pressure coefficient (K) was described as the ratio of horizontal to vertical stresses measured at different points in the region where arching occurred. The soil arching exists not only in buried pipes but also in various engineering constructions such as culverts, tunnels, piled embankments, retaining walls, and foundations. Researchers have studied the formation, stages, boundaries, and configuration of arching within the soil prism under different testing conditions [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. Arching and deflection of the pipe change the stresses, giving rise to new stress distributions [8; 13; 41]. This situation results in a significant variation in the lateral earth pressure coefficient, K. In the designing of deep buried flexible pipes, the Iowa equation developed by [PERSON] [42] and German standards [43] are widely used to calculate uniformly distributed stresses acting on the pipe, assuming equal deflection in both vertical and horizontal directions. Similar approaches are required when considering the distribution of stresses in shallow buried flexible pipes exposed to localised surface loading. However, there is still a lack of studies on this subject. To develop similar approaches, it is critical to understand the deflection behaviour of the pipe and its effect on the stress distribution, the deformed shape of the pipe and the changes in lateral earth pressure coefficients within the central soil prism due to arching effects. In this study, two main cases are investigated by full-scale tests and numerical analyses for a shallow buried HDPE (High Density Polyethylene) pipe subjected to localised loading, considering crown and springline stresses and pipe deflections. Firstly, the study explored the applicability of [PERSON]'s arching theory to calculate stresses acting on the crown. This involved considering variations in the lateral earth pressure coefficient (K) within the central soil prism, dependent on the form of pipe deflection. In the second case, the changes in springline stresses due to horizontal pipe deflection were studied. Finally, the research investigated the numerical relationship between stresses at the springline and the arching deflection of the pipe through Finite Element (FE) models. This comprehensive approach aimed to provide insights into the complex interaction between soil behaviour, pipe deflection, and stress distributions under localised loading conditions.
# Bioactive Compounds from Herbal Medicine Targeting Multiple Myeloma
## 3 Flavonoids in Multiple Myeloma
### Butein
#### Isobavachalcone  
Isobavachalcone is a prenylated chalcone of the flavonoids (Figure 16). The compound was first isolated in 1968 from _Psonalea corylifolia_ L. [389] ([PERSON], 2012). It is found in medicinal plants, such as _Broussonetia papyrifera_, _Angelica keiskei_, _Psonalea corylifolia_, and _Maclura tinctoria_[390; 391].  
Figure 15: The chemical structure of cardamonin.  
This naturally occurring chalcone compound has been used in traditional Chinese medicine as an anthelmintic, antibacterial, aphrodisiac, astringent and antiplatelet agent [347, 392, 393]. Isobavachalcone showed antioxidant, antifungal, antibacterial, and anticancer activity [347].  
Isobavachalcone has been shown to have anticancer activity [394, 395]. It inhibited skin tumors in vivo [396], induced apoptosis in multiple myeloma, neuroblastoma, prostate cancer, breast cancer, ovarian cancer, and lung cancer [394, 397, 398, 399].  
Isobavachalcone showed reduced toxicity to normal cells. Isobavachalcone inhibited the proliferation of MM H929 cells by inducing apoptosis and autophagy. Mitochondrial cell death pathway was involved in its anti-proliferative activity. Protelytic activation of PKC8 was involved in chloroquine plus isobavachalcone-induced cell death. The combination of chloroquine with isobavachalcone showed low toxicity for normal PBMCs [397].
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Next-Hop Selection
#### 3.8.5 Opportunistic  
Opportunistic routing (OR) algorithms differ by the chosen metric applied to the forwarder selection and prioritization. Hence, several opportunistic routing protocols have been presented depending on several metrics such as the real-time opportunistic routing protocol (ORTR) [111], which adjust the transmission power to follow the delay constrains and the energy-aware opportunistic routing protocol (EAOR) [112], including crucial energy information exchange during the well-know RTS/CTS handshake of opportunistic routing protocol [113]. The objective is to define a new OR metric, which reduces energy consumption in WSNs.
## 3 Results
### Quantitative Analysis of Sample Size in Reaching Feasible Model Performance  
To quantitatively determine the effects of the sample number on the model prediction, the BGSA algorithm was used to test the R\({}^{2}\) curves of XGBoost in Figure 4e-h for mutation. In Figure 5, the mutation points ranged from two to four for different sites (US-MMS, DBF, with 4; NL-Loo, ENF, with 3; US-Var, GRA, with 2; and DE-Geb, CRO, with 3). Except for the NL-Loo site, the first mutation position of the other three sites is less than 500. At the left of the first mutation, each curve of R\({}^{2}\) fluctuates dramatically and a distinct  
Figure 4: (**a–d**) Effect of increasing the training sample on the prediction results (take four sites as examples). Increasing training samples in terms of annual sample size (x axis) and the determination (R\({}^{2}\)) from each test sample and predicted values (y axis). (**e–h**) Increase the total samples (70% of which are training samples and 30% are test samples) in steps of 100 (x axis), and the determination (R\({}^{2}\)) as in (**a–d**).  
trough appears. This is likely because when the total sample size is less than 500, then the NTS is less than 350 (approximately one complete observation year), so the NTS is not representative enough. Since both US-MMS (DBF) and NL-Loo (ENF) sites (Figure 5a,b) belong to the forest biome, their mutations are basically the same, at 1300, 2200-2400, and 4100-4500, respectively. The R\({}^{2}\) values increase after each mutation. The x-axis in Figure 5 refers to the total number of samples (70% of which are training samples and 30% are test samples), so the NTS corresponding to each mutation is 910, 1540-1680 (the median is about 1600), and 2870-3150 (the median is about 3000). This means for DBF and ENF sites, the training sample number of 910 (approximately 2.5 years daily samples, named YDS hereafter) can stabilize the model, and 3000 (approximately 8.2 YDS) can lead the model to reach the best performance. Compared with the above two sites, US-Var (GRA) and DE-Geb (CRO) sites (Figure 5c,d) reached the stability mutation at delayed positions between 1900 to 2400 (the median is equal to 2150) and the last mutation at an earlier position between 2400 to 3200 (the median is equal to 2800); correspondingly, the NTS are 1505 (approximately 4.1 YDS) and 1960 (approximately 5.4 YDS), respectively. The same trend was observed in the RF analysis for both four sites, detailed in Figure S3.  
Figure 5: Mutation detections for the evaluation results of XGBoost in Figure 4e-h by the BG segmentation algorithm. The black vertical dash lines represent mutation points.
## 4 Results
### Generalization  
To assess the applicability and generalizability of the proposed approach across different domains, we applied our methodology to the GoEmotions dataset [43, 44]. This dataset comprises Reddit comments, which are manually annotated with 27 distinct emotion labels. Similarly to the hacker's posts dataset, GoEmotions contains noisy, user-generated content with heterogeneous lengths and formats. To simulate comparable conditions, we selected a subset of the GoEmotions dataset [44] comprising four emotion categories--joy, anger, sadness, and neutral--and ensured a balanced distribution of samples across these classes. For our generalization experiments, we employed the Mistral LLM, which previously demonstrated the highest accuracy in the hacker's posts classification task.  
We investigated two experimental settings: (a) prompts constructed using examples that are representative of each target category and (b) prompts constructed using randomly selected examples. Model performance in both settings was evaluated based on the accuracy metrics reported in Table 4.  
As shown in Table 4, the accuracy achieved by the proposed method in the emotion recognition domain exhibits a pattern consistent with that observed in the hacker's posts experiment. More specifically, fine-tuning yielded the highest performance, followed by three-shot prompting, which slightly outperformed the one-shot approach, both of which surpassed zero-shot performance. When prompt examples are selected randomly, accuracy declines across all configurations, falling below the few-shot and zero-shot baseline in both domains. These observations provide a strong indication of the applicability and robustness of the proposed methodology across diverse domains and varying levels of data noise.
# Seismic Behaviour of CFST Space Intersecting Nodes in an Oblique Mesh
## 6 Discussion
### Space Intersecting Angle
As shown in Figure 6a-e, the maximum Mises stress values of all specimens reached the ultimate strength of the steel tube, for specimens with space angles \(\leq\) 3\({}^{\circ}\), the maximum stress value was mainly located in the middle of the elliptical connecting plate near the concave side and the non-nodal area far from the end. For specimens with space angles \(>\) 3\({}^{\circ}\), the maximum stress value was mainly located on the concave side of the elliptical connecting plate, and the positive \(y\)-axis side of the loaded end steel tube, and the reason for the difference was mainly due to the different failure modes of the specimens. The failure mode of specimens with space angles \(\leq\) 3\({}^{\circ}\)was mainly bending failure of the steel tube concrete column, while the failure mode of specimens with space angles \(>\)3\({}^{\circ}\)was mainly out-of-plane instability failure of the node. According to Figure 6f-h,  
Figure 5: Relevant mechanical property parameter curves of components with different space angles. (**a**) Displacement load curve. (**b**) Skeleton curve. (**c**) Stiffness degradation curve. (**d**) Axial displacement-out of plane displacement curve. (**e**) Energy consumption curve. the area of compression damage of the specimen with a space angle of 1\({}^{\circ}\) was relatively large, and the main damage area was the interval (105.6 mm, 1054.5 mm) from the starting point of the intersection. In the interval (\(-\)105.6 mm, 105.6 mm), the average compression damage was only 0.49, mainly due to the reinforcement ring setting in this area, which improved the radial constraint of the core concrete. For specimens with space angles of 4\({}^{\circ}\) and 5\({}^{\circ}\), the compression damage was mainly concentrated on the concave side of the node area and the loaded end. The maximum compression damage value was observed in the specimen with a space angle of 5\({}^{\circ}\), while the concrete damage in the non-nodal area was relatively small. This difference was mainly due to the maximum lateral instability degree of the component, and the bending moment on the steel tube concrete at the end was larger during failure.
## 5 Experimental Results
### RQ2. How Effective Is the Proposed Approach in Test Case Prioritization in Testing CAD Software? We evaluate whether the ranking of test cases can trigger the defects early. During testing the CAD software, given the same set of test cases, if a sequence of test cases (i.e., a ranked list of test cases) can trigger defects earlier than another sequence of test cases, we consider the earlier sequence is better. The effectiveness of such sequences of test cases can be evaluated with the pre-defined APFD metric [46]. A high value of APFD indicates an effective sequence of test case ranked. We designed RQ2 to evaluate the test case sequences. **Method**. In the comparison, we employed the random ranking of test cases as a baseline. We validated the test cases, respectively, for the CART, the random forest, the SVM, the Bayesian networks, the CNN, and the RNN. In our study, the number of triggered defects in top-10, top-20, and top-30 test cases of each test case sequence is reported as a metric to evaluate the capability of the ranking model. A high value of the number of defects detected in top-\(k\) indicates that the test case ranking performs well. **Result and analysis**. Table 5 shows the evaluation results of ranking test cases for the CAD software. Table 5 demonstrates that among the top-10 test cases. The Bayesian networks can find the least number of defects and the random forest detects the most defects from dataset one. In the top-20 test cases, the random ranking finds the least number of defects and the random forest model detects the most defects for most modules, but the values are the same in module procedure, module math, and part 2. The reason is that the number of defective functions is less than 20. In the top-30 test cases, the random ranking model detects the least number of defects and the random forest model detected the most defects for most modules, but the values are equal in module procedure, module math, and Fold 2. The number of defective functions is less than 30. Among the five models in comparison, the random forest model shows the largest APFD value and the random ranking model shows the smallest value. To show details, we use Table 6 to directly present one example of the top-10 results of ranking test cases by the random forest model. As shown in Table 6, point_outside_influence_radius_is_0 and point_inside_radius_is_greater_than_1 have the highest value (0.4605) among the top-10 test cases. This means that these two test cases are more likely to find defects than the other test cases.
# Globally Scheduling Volunteer Computing
## 4 The global Volunteer Computing Scheduling Problem
### The Project Assignment Algorithm  
BOINC clients using Science United periodically (once per day) issue an AM RPC. The request message includes a list of currently attached projects, with account and scheduler RPC failure information for each one as described in Section 4.5.  
The reply message includes a list of projects. Some of these are currently attached projects flagged as \"outgoing\": this instructs the client to finish existing jobs for the project, then detach from it. For each of the other projects, the reply includes a list \(R\)(\(P\)) of processing resources that the client should use for \(P\).  
With these factors in mind, here is a sketch of the project assignment algorithm currently used by Science United:  
First, we discard projects \(P\) that cannot be used because either (a) \(P\) has a keyword for which the volunteer has a \"no\" preference; or (b) _Usable_(\(P\), \(D\), \(R\)) is false for all processing resources, i.e., \(P\) does not have app versions that can use the device.  
For each remaining project we compute a \"score\" \(S(P)\) which is the weighted sum of several components:  
\[S(P)=C_{1}K-C_{2}E(P)+C_{3}V \tag{4}\]  
where* \(K\) is the number of project keywords for which the volunteer has a \"yes\" preference. Projects with such keywords will be preferred over projects with only \"maybe\" preferences.
* \(E(P)\) is the project's allocation balance see Section 4.4.
* \(V\) is one or zero depending on whether the device has VirtualBox installed and the project has a VirtualBox app version that can run on the device. This preferentially assigns devices with virtualization capability (which are relatively scarce) to projects that require it.  
C\({}_{1}\), C\({}_{2}\) and C\({}_{3}\) weight these components, reflecting a balance between conflicting goals. They have been chosen empirically; currently C\({}_{2}\) is \(01\) and the others are one.  
We then compute a set \(A\) of projects to include in the reply This is carried out as follows. For each processing resource \(R\), we find the highest-scoring project \(P\) for which _Usable_now_(\(P\), \(D\), \(R\)) is true. We add \(P\) to \(A\), and include \(R\) in the resource list for \(P\). If there are projects that have a higher score than \(P\), and for which _Usable_(\(P\), \(D\), \(R\)) is true, we similarly add them to \(A\).  
The ability to specify the processing resources used by a project is important in some cases. For example, suppose the highest-scoring project \(P\) can only use the CPU, and the device also has a GPU. The scheduler can assign a lower-scoring project and have it use only the GPU, giving \(P\) full use of the CPU.  
If the client is currently attached to a project not in \(A\), we add it to \(A\) and set the \"outgoing\" flag, telling the client to finish existing jobs and detach from the project.
# Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
## 5 Experiments
### Ablation Experiments  
The experimental findings underscore the substantial merits of the proposed EPEM, delineated across three pivotal dimensions: (1) The individual feature projector aims to capture the type-specific features of different types of nodes and links, which can discover the distinguished correlations between different types of nodes and links. (2) The predictive expert constructor furnishes enhancing predictive experts adept at leveraging the learned type-priori features of diverse link types for unobserved link prediction. (3) The trustworthiness investor orchestrates the amalgamation of prediction outputs from enhancing predictive experts based on their respective trustworthiness values.  
To affirm the effectiveness of components of the EPEM, we devised three model variants, namely, EPEM-i, EPEM-p and EPEM-t. The EPEM-i model excludes the individual feature projector, the EPEM-p model inputs feature representations of disparate link types to a single enhancing predictive expert to distill their shared characteristics, and the EPEM-t model eschews the trustworthiness investor. Instead, it employs an average trustworthiness distribution for comparison against other methods in our experiments.  
In this section, we elucidate the experimental outcomes of the proposed EPEM model and its variants, with results exhibited in Figure 3. The performance comparison between EPEM-i and EPEM demonstrates the importance of capture the type-specific features of different nodes for obtaining the type-priori representations of them. The empirical observations endorse the efficacy of both the predictive expert constructor and the trustworthiness investor within our model. Even with a singular enhancing predictive expert, EPEM-p consistently outperforms the majority of compared methods, underscoring the predictive expert constructor's efficacy. Furthermore, the trustworthiness investor's adeptness in assigning higher trustworthiness to enhancing predictive experts of superior reliability empowers the model to deliver more targeted unobserved link predictions. Therefore, we establish both the individual feature projector, the predictive expert constructors, and the trustworthiness investor as indispensable components of the proposed EPEM model.  
Figure 2: Performance comparison of EPEM by setting different ratio \(\delta\) on datasets. (**a**) Facebook; (**b**) DBLP; (**c**) MovieLens.
## 3 Proposed Methods
### Hard Information Flipping (HIF)  
We first examine three mutual data bits (hard information) produced by 1D SOVA detectors, represented as a 3 \(\times\) 1 column vector, as shown in Figure 6a. These bits were produced from the LLRs generated by SOVA detectors through a threshold detector. The first and third rows correspond to the data from H1 and H3, respectively, while the second row is from H2. Practically, these three estimated bits should have the same sign as the recorded bit being detected.  
Figure 5: Readback signal waveforms from a staggered medium at AD = 3.0 Tb/in2.  
Therefore, disagreement among the signs indicates the presence of errors, and to maximize accuracy, a simple scheme is to select the most common sign among the three. For instance, in the first and last columns of Figure 6a, when all three bits are \"+1 s\" or \"\(-\)1 s\", the estimated recorded bits will be \"+1\" and \"\(-\)1\", respectively, as depicted in the first and last columns of Figure 6b. Referring to the second column in Figure 6a, there are two \"+1 s\" and one \"\(-\)1\". In this case, the most common sign, \"+1\", is selected as the estimated recorded bit. Similarly, in the fifth column, there are two \"\(-\)1 s\" and one \"+1\". Therefore, the estimated recorded bit for this column is \"\(-\)1\", as depicted in Figure 6b.
## 3 Results and Discussion
### Singlet Oxygen Generation Study  
To assess the potential application of the new tribenzoporphyrazines in photodynamic therapy, the quantum yields of singlet oxygen generation in DMSO were measured spectrophotometrically using an indirect method with 1,3-diphenylisobenzofuran (DPBF). DPBF is a compound that easily reacts with singlet oxygen, with a breakage of tran ring, and its peak at about 400 nm decreases over time (Figures 7 and 8) as it decomposes. The changes in absorption were than calculated to give singlet oxygen quantum yields (\(\Phi_{\Delta}\)). As a reference, zinc(II) phthalocyanine was used. Compound **6** generated oxygen with a quantum yield value of 0.24, which indicates the moderate photosensitizing potential, whereas 7 presented an even higher quantum yield, reaching the value of 0.59. The measured singlet oxygen generation values can be considered as high compared to those previously obtained for the studied magnesium(II) **3** and **4** analogs and other porphyrazine derivatives presented in the literature (Table 4). It was found by [PERSON] et al. [38] that porphyazines with eight peripheral isophtaloxylkylsulfonyl substituents generate singlet oxygen with \(\Phi_{\Delta}\) values between 0.01 and 0.04 in DMF and that incorporating Mg\({}^{2+}\) ions into the Pzs improves their capability to generate single oxygen. The exchange of Mg\({}^{2+}\) (**3**, **6**) to Zn\({}^{2}\) (**4**, **7**) in the tribenzoporphyrazine ring influenced the singlet oxygen generation  
Figure 5: The CV (**a**) and DPV (**b**) voltammograms of **7** in DCM/0.1M TBAP. DPV parameters: modulation amplitude 20 mV, step rate 5 mV \(\times\) s\({}^{-1}\).
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 2 Related Work
### SoftCast Video Transmission
[31] developed resource allocation algorithms for 3D video. In distortion-resource optimization, the DR algorithm exhaustively searches all possible discrete power and channel resources. For resource-distortion optimization, the optimization problem is divided into power and channel optimization subproblems. Then, considering fixing one resource each time given a desired distortion, the RD algorithm looks for the optimal solution after searching in an exhaustive manner. Nonetheless, it combines texture and depth using 5D-DCT without considering the characteristic distinction. Unlike 2D video pseudo-analog transmission systems, which resort to discarding chunks with the smallest variation to meet the constrained resource requirement without significantly degrading quality, 3D video pseudo-analog transmission systems can face some complications. Due to the involvement of synthesis distortion, the source data that can be discarded from both texture and depth frames must significantly degrade neither the virtual view quality nor the reference view quality, rather than only the latter. Hence, this work aims to perform resource allocation while jointly balancing the power assigned to texture and depth maps to avoid causing geometric distortion in virtual views or additional distortions in reference views, to improve resource allocation in 3D video transmission.