esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Internal and External Temperature  
Ground tests revealed that the RF components dissipate a considerable amount of heat, so that no internal heater was needed for the other subsystems. During all five ISM-HAB flights, the temperature inside the payload stayed between 30 and 52 \({}^{\circ}\)C, while the outside temperature reached a minimum of 46 \({}^{\circ}\)C (Figure 11), with polystyrene proving to be quite effective as a thermal insulator. Therefore, all the electronic components inside the payload stayed well inside their operative temperature range.  
Figure 10: (**a**) Trajectories of the five missions. (**b**) Picture taken during ISM-HAB #3 at 30 km altitude. The curvature of the Earth is accentuated by the fisheye lens.  
Throughout the flights, the recorded external temperatures were consistently higher than those of the standard atmosphere (Figure 12). It remains to be assessed whether this corresponds to reality or it is due to a bias of some kind in the sensors.
## 5 Experimental Results and Evaluation
### Security and Privacy Analysis
#### 5.4.3 DDoS Attack  
In conventional cloud-based systems, an adversary can accessmultiple compromised computers, such as using bots, to send huge volumes of TCP request traffic to target cloud servers in a short period of time. As a result, unexpected traffic jams by the DDoS attack overwhelm centralized servers such that service and networking functions become unavailable. Our solution adopts a DDS to achieve efficient and robust meta-data storage and distribution. As the DDS uses a DHT-based protocol to coordinate and maintain meta-data access service sites over a P2P network, it is hard for an adversary to disrupt the meta-data service by launching DDoS attacks to target service sites. Moreover, our data authentication framework relies on SCs deployed on Ethereum to ensure decentralization. Therefore, our approach can mitigate the impact of DDoS attacks better than centralized data auditing methods.
# High-Temporal-Resolution High-Spatial-Resolution Spaceborne SAR Based on Continuously Varying PRF
## 1 Introduction
In Section 5, the relevant imaging algorithm for high-resolution high-squint spaceborne SAR, based on CVPRF, is developed. In Section 6, simulation results are provided, therein showing the accuracy of the resampling processing and the high-order imaging algorithm for the CVPRF concept. Conclusions are drawn in Section 7. Figure 4: Timing diagram (slant range versus PRF) (**left**) and basic geometry (**right**) of CVPRF conception. Pulses of different squint angle is represented with different color (varying from red to gray). For transmitted pulse on the right of the figure, its suitable PRF is shown on the left with the same color.
# Melanoma Brain Metastases: Immunotherapy or Targeted Therapy? A Systematic Review and Meta-Analyses
## 3 Results
### Methodological Quality  
The full data regarding quality assessment according to the Cochrane Collaboration risk-of-ias tool (CCRBT) are described in Supplementary material Table S2. Overall, we found a low risk of bias, and this is not surprising, since only three studies were phase III randomised trials.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **N** & **Proportion (95\% CI)** & **I\({}^{2}\)** & \(p\)-Value** \\ \hline
**Treatments** & & & & 0.21 \\ \hline Immunotherapy + TT/TT & 6 & 0.47 (0.28–0.66) & 86.8\% & \\ \hline Immunotherapy & 7 & 0.33 (0.24–0.44) & 73\% & \\ \hline
**Type immuno** & & & & 0.002 \\ \hline Combo immuno & 3 & 0.45 (0.36–0.54) & 55.1\% & \\ \hline Mono immuno & 5 & 0.26 (0.19–0.34) & 11.9\% & \\ \hline No immuno & 5 & & & \\ \hline
**Local therapy** & & & 0.78 \\ \hline Yes & 10 & 0.39 (0.30–0.48) & 74.3\% & \\ \hline No & 3 & 0.45 (0.13–0.82) & 85.3\% & \\ \hline
**Previous Immunotherapy** & & & 0.98 \\ \hline Yes & 7 & 0.41 (0.32–0.51) & 67\% & \\ \hline No & 4 & 0.41 (0.17–0.70) & 79.6\% & \\ \hline Missing & 2 & - & - & \\ \hline
**Previous TT** & & & 0.79 \\ \hline Yes & 8 & 0.38 (0.26–0.52) & 74.40\% & \\ \hline No & 3 & 0.41 (0.29–0.54) & 83.90\% & \\ \hline
**ECOG PS** & & & 0.01 \\ \hline
2 & 5 & 0.52 (0.38–0.67) & 63\% & \\ \hline
1 & 8 & 0.30 (0.21–0.40) & 81.90\% & \\ \hline \end{tabular}
\end{table}
Table 5: Subgroup analysis for treatment responses of asymptomatic brain metastases in melanoma patients.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **N** & **OR (95\% CI)** & **l2** & \(p\)**Value** \\ \hline
**Treatment** & & & & \\ \hline Immuno TT/TT & 5 & 0.98 [0.65–1.47] & 0.00\% & 0.001 \\ \hline Immuno & 3 & 0.17 [0.06–0.46] & 0.00\% & \\ \hline
**Type immuno** & & & & \\ \hline Combo immuno & 3 & 0.34 [0.07–1.64] & 53.54\% & 0.819 \\ \hline Mono immuno & 1 & 0.27 [0.03–2.30] & - & \\ \hline No immuno & 4 & - & - & \\ \hline
**Local therapy** & & & & \\ \hline Yes & 7 & 0.61 [0.32–1.17] & 47.30\% & 0.47 \\ \hline No & 1 & 1.0 [0.42–2.40] & & \\ \hline
**Previous Immuno** & & & & \\ \hline Yes & 4 & 0.74 [0.35–1.54] & 66.80\% & 0.05 \\ \hline No & 2 & 0.17 [0.04–0.75] & 0\% & \\ \hline NA & 2 & - & - & \\ \hline \end{tabular}
\end{table}
Table 6: Subgroup analysis for treatment responses of symptomatic vs. asymptomatic brain metastases in melanoma patients.  
Figure 7: Odds ratios for treatment responses of symptomatic vs. asymptomatic brain metastases in melanoma patients. Reported measures of heterogeneity: Tau2 (prespecified value for the square root of the between-study variance, restricted maximum-likelihood estimator).
## 3 Results  
To ensure that the sample size achieved adequate power, a power analysis was conducted using the \"pwr package\" in RStudio (2023.06.2 + 561) [53; 54]. The analysis indicated that the achieved power was 0.969, with \(\alpha\) set at 0.05, and a large effect size was assumed (i.e., \(\eta_{\mathrm{p}}{}^{2}\) = 0.14). The experimental results included response time (RT), proportion correct and Cowan's K. We analyzed response time by linear mixed-effects regression, and we analyzed accuracy data via logistic mixed-effects regressions [51; 55]. For the RT analysis of the change detection task, we only considered correct trials (21.8% error). Then, we excluded from the analyzed cases RTs greater than three median absolute deviations above or below the median RT, calculated separately for each participant and condition (9.1%).
# Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review
## 2 Mechanism of Extracellular Electron Transport in Microorganisms
### Indirect Electron Transfer (IET)  
In IET, microorganisms can generate and secrete diminutive molecules that can undergo redox cycling, thereby acting as an ES (Figure 1). They function as the final electron acceptor and, upon reduction, are capable of transferring electrons to iron oxides, subsequently undergoing reoxidation. This type of shuttle offers a means for an indirect reduction process. The repetitive cycling of a solitary shuttle molecule can significantly influence the turnover rate of the terminal oxidant, such as iron, within a specific situation. Compounds such as humic compounds, quinones, phenazines, and molecules containing thiols have the potential to fulfil this function [26; 27; 28; 29]. Electron shuttles can be classified into two distinct categories, specifically exogenous and endogenous, depending on their presence in the environment or the inherent ability of the cell to produce them. Thionine is the predominant exogenous mediator utilized, while other compounds such as quinine, phenoxazine, phenazine, and phenothiazazine have also been employed in this capacity. On the other hand, endogenous mediators are released from cells in a reduced state and undergo oxidation to enable electron transfer at the electrode. The mediators under consideration in this category consist of flavin compounds produced by _S. oneidensis_ MR-1 and 2-amino-3-carboxy-1/4-naphthoquinone, which is excreted by _S. putrefaciens_[24].
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 6 Emerging Trends and Future Directions
### Current Research Trends in DVC  
The field of DVC is rapidly evolving, with researchers exploring various techniques to improve event recognition, localization, and description generation. One notable trend is the increasing use of Transformer-based architectures. Unlike traditional LSTM or encoder-decoder models, Transformers can capture long-range dependencies in video data, leading  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**NO** & **Method** & **Method’s Category** & **Dataset** & **BLEU-4** & **METEOR** & **CIDEr** & **ROUGE-L** & **Year** \\ \hline
1 & KEANeft [130] & Encoder-decoder & VC\_NBA\_2022 & 32.4 & 28.0 & 138.5 & 54.9 & 2025 \\
2 & Instance-VideoLLM [61] & Attention & WTS & 0.121 & 0.409 & 0.389 & 0.417 & 2025 \\
3 & UCF-SST-NLP [131] & Attention & WTS & 0.2005 & 0.4115 & 0.5573 & 0.4416 & 2024 \\  
oundary-aware [140] & Encoder-decoder & MPII-MD & 0.8 & 7.0 & 10.8 & 16.7 & 2017 \\
24 & VL-LSTM [141] & Encoder-decoder & MPII-MD & 0.80 & 7.03 & 9.98 & 16.02 & 2015 \\
25 & SMT [51] & Encoder-decoder & MPII-MD & 0.47 & 5.59 & 13.21 & 8.14 & 2015 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of video captioning methods on nine other benchmark datasets, showing BLEU-4, METEOR, CIDEr, and ROUGE-L scores for each sentence in short descriptions.  
to more accurate and coherent captions. For instance, the PDVC framework [17] employs a deformable Transformer with parallel decoding heads for localization and captioning, achieving state-of-the-art performance.  
Another trend is the integration of multimodal information. DVC systems are no longer limited to visual features but also incorporate audio, language, and other modalities to enhance the robustness and descriptive capabilities of the system. Multimodal fusion techniques, such as those proposed in [PERSON] et al. [22], leverage the natural kinship between events and their human-provided descriptions to generate semantically meaningful event proposals.
## 5 Analysis of Roughness Detection Method by Kernel
### Analysis Results
All three samples with 100,000 points have an error of less than 0.6% compared to the results of the original sample. When the number of points is reduced to 10,000, the histogram is similar to the original point cloud (see Figure 12(b)). In the grouting, sand and gravel samples, the roughness at which the number of points reached 95% was 5.76, 6.05, and 1.19 mm. The roughness of the sand and gravel samples showed an error of about 2% compared to the results of the original point cloud. The fluctuation between the roughness classes in the histogram becomes larger if the number of points is decreased to 5000 (see Figure 12(c)). Therefore, the distribution of points in individual roughness classes of the histogram is unstable. The roughness at which the number of points in each sample reached 95% has an error of about 2% compared to the result of the original point cloud. In the results of 1000 points, the fluctuation of histogram is larger than the others and the slope of the histogram is steeper within a roughness class of 5 mm (see Figure 12(d)). The roughness at which the number of points in the gravel sample reached 95% is increased to about 4% compared to the result of the original point cloud. The histograms of grouting and sand samples cannot be distinguished as well. Figure 13: Effect of point cloud density on roughness. Figure 14 shows the result of the point cloud roughness in color scale for each point. In the case of 100,000 points, it is similar to the color scale result of the original point cloud, as shown in Figure 14a. If the number of points is reduced to 10,000, the locations with high roughness are still same as the results of the original point clouds (see Figure 14b). When the number of points is decreased to 5000, the distribution of points becomes sparser. In the failure surface, the intensity of the color scale representing the roughness of the protrusion of the gravel in the gravel sample, and the angled failure surface in the grouting sample is decreased with the decrease of points (see Figure 14c). The roughness is reduced compared to the result of the original point cloud because the kernel is redefined by the reduced points. Figure 14d shows the results of 1000 points, and the color scale of roughness is further reduced. The location of the kernel for detecting the roughness is changed by the sparse point cloud. The elevation between the kernel and points is more reduced by the changed kernel. Therefore, the roughness scale is decreased with the decrease of the points, which means that the roughness cannot be expressed in detail under low density conditions.
# FGM: Noisy Label Learning via Fine-Grained Confidence Modeling
## 4 Experiments
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**CIFAR-10**} \\ \hline Noise type & \multicolumn{4}{c}{Asym} \\ \hline Method/Noise ratio & 10\% & 20\% & 30\% & 40\% \\ \hline Cross-Entropy & 88.8 & 86.1 & 81.7 & 76.0 \\ \hline GCE [24] & 89.5 & 85.6 & 80.6 & 76.0 \\ \hline Pcorr [38] & 93.1 & 92.9 & 92.6 & 91.6 \\ \hline Mcorr [8] & 89.6 & 91.8 & 92.2 & 91.2 \\ \hline DivideMix [19] & 93.8 & 93.2 & 92.5 & 91.4 \\ \hline ELR [15] & 94.4 & 93.3 & 91.5 & 85.3 \\ \hline MOIT+ [28] & 94.2 & 94.3 & 94.3 & 93.3 \\ \hline Sel-CL+ [12] & 95.6 & 95.2 & 94.5 & 93.4 \\ \hline FGCM & 95.6 & 95.4 & 94.7 & 93.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison with state-of-the-art methods on CIFAR-10 with different ratios of asymmetric noise as PreAct-18 resnet being the backbone. Figure 3 demonstrates the effectiveness of FGCM in filtering and rectified noisy labels by modeling the confidence of samples in a fine-grained manner. The outstanding performance on both synthetic and real-world datasets demonstrates that FGCM can effectively alleviate the bad memorization effect of DNNs and still maintain good performance for the trained model even when there is a high number of false-labeled training samples.
# The Limitations of 5f Delocalization and Dispersion
## 5 X-ray Absorption Spectroscopy: Angular Momentum Coupling and Perturbations
### Prepeak Structures in 4f and 5f Systems  
The observation and explanation of the pre-peak structure is not new. In fact, it goes back to 1971, with the theoretical analysis of the pre-peak structure in Ce by [PERSON] et al [39], a copy of which is shown in Figure 8. These results will be utilized further in the discussion that follows. The important point for Pu is that in the XAS final state, n increases from 5 to 6, filling the 5f\({}_{5/2}\) manifold, eliminating the angular momentum coupling in the final state and thus also preventing any pre-peak structure [6; 40].  
It is also important to note that the electric dipole selection rules that govern these transitions are founded upon the utilization of spherically symmetric states, i.e., the spherical harmonics [6; 7; 8; 9]. The high level of agreement in Figure 1 between the experimental results, derived from the Branching ratio measurements, and the Intermediate Coupling Theory requires strongly spherically symmetric wavefunctions, for both the core states and the 5f states. Again, the symmetry of the potential determines the symmetry of the solution. The non-spherical perturbations of the 5f states must be weak compared to the spherical potential terms. (Regarding Figure 7: The pre-peak has also been observed for U but the situation for U is complicated by the delocalization. For \(n=3\), U should have a bigger branching ratio than is observed. This is discussed in detail elsewhere [6; 7; 8; 32]).  
Figure 7: The N\({}_{4,5}\) (4d\({}_{5/2}\) and 4d\({}_{3/2}\)) and O\({}_{4,5}\) (5d\({}_{5/2}\) and 5d\({}_{3/2}\)) XAS of UO\({}_{2}\) (localized, \(n=2\)) and Pu (localized, \(n=5\)) are shown here. XAS is X-ray absorption spectroscopy. The relative photon energy is relative to the maximum of the 4d\({}_{5/2}\) peaks. See text for details. The figure has been taken from [38], with data from [6; 7; 8; 32].
# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 3 Materials and Experiments
### Evaluation Indicators  
We used a general target detection evaluation system to evaluate the effectiveness of various models in ship detection tasks. The definitions of precision and recall are given by Equations (6) and (7).  
\[\text{Precision }=\frac{TP}{TP+FP} \tag{6}\]  
\[\text{Recall }=\frac{TP}{TP+FN} \tag{7}\]  
where TP stands for true positives, FP for false positives, and FN for false negatives.  
When assessing model performance, individual precision and recall metrics may not provide sufficient information, as a model could exhibit a situation with extremely high precision and very low recall. To comprehensively consider the model's performance, we used the F1-score as one of the model evaluation indicators. Specifically, the definition of the F1-score is provided in Equation (8).  
\[\text{F1-Score }=\frac{2\times\text{ Precision }\times\text{ Recall}}{\text{ Precision }+\text{ Recall}} \tag{8}\]  
In addition, the mean Average Precision (mAP) is a key indicator for evaluating the effectiveness of object detection algorithms. The definition is given in Equations (9) and (10). In Equation (9), P represents precision and R represents recall. The curve obtained by plotting precision with recall is denoted as P(R), where recall is on the horizontal axis and precision on the vertical axis. In Equation (10), N represents the number of label types in the downstream task, and AP(i) represents the AP value of the i-th label.  
\[AP=\int_{0}^{1}P(R)*dR \tag{9}\]  
Figure 8: Statistical data plots for different ship sizes in the HRSID and SSDD dataset. (**a**) shows the number and percentage of small, medium, and large ships in the HRSID dataset. (**b**) shows the same statistics in the SSDD dataset. The number of small-sized ships is the highest.  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP(i) \tag{10}\]
# Malicious Office Macro Detection: Combined Features with Obfuscation and Suspicious Keywords
## 2 Background
### Macro in Office Documents  
MS Office macros, developed in the Visual Basic for Applications (VBA) programming language, have been widely applied in Microsoft Office documents such as Word, Excel, PowerPoint, and Outlook. Users can develop customized macro codes to interact with Office applications. These codes can be used to complete various tasks, such as manipulating data, formatting documents, creating charts, sending emails, etc. The automation of repetitive tasks is one of the main advantages of utilizing macros. For example, when users need to perform formatting actions multiple times in Microsoft Word, such as applying a specific font, setting paragraph alignment, or inserting headers and footers, they can use macros to record these actions once and execute them whenever necessary. This helps to save time and effort. Macros also help users accomplish complex tasks that may otherwise be challenging or time-consuming. For example, Microsoft Excel enables its users to create macros. These macros can analyze large datasets, perform calculations, generate reports, and automatically create charts when triggered by a specific event, such as workbook opening or button clicking. Figure 1 shows the VBA macros of two benign documents.  
Figure 1a demonstrates how to modify the font format in a Word document, while Figure 1b shows a macro snippet that automatically sends an email with the content of the document.  
While bringing convenience to users, macros are also exploited by attackers to carry out various malicious activities. As depicted in Figure 2, a straightforward yet malicious macro, comprising only four lines, can still execute a series of malicious actions. It obtains a running instance of Windows Explorer using the VBA function \"GetObject\". The method \"ShellExecute\" is then called to initiate PowerShell, which downloads and executes a malicious script. There are various techniques that can be employed to execute commands, as we will further detail in the following sections.
## 2 Grout-Injection Materials and Test Specim Products  
### Installation Methods and Exposure Conditions of Grout-Injection Materials  
Conventionally used grout-injection materials for concrete crack repairs include acrylic resin, epoxy resin, polyurethane foam, and synthetic polymer rubberized gel (SPRG). In this paper, these four types of grout-injection repair materials were tested for their performance in compliance to the ISO TS 16774 _Test Methods for Repair Materials for Water-Leakage Cracks in Underground Concrete Structures_. Injection methods can be broadly categorized into two types: (1) a direct injection for leakage crack sealing (installation into the crack) and (2) a backfill injection for the repair/reformation of a damaged existing waterproofing layer (drilling at the adjacent side of the crack and injection) [8]. In the case of a direct injection method, a low-pressure injection is required to ensure localized cracking near the crack is prevented. Refer to Figure 3 below for an illustrated concept of a grout-injection repair of waterproofing membranes.  
The level of exposure to the different types of the six degradation factors with the types of grout-injection materials can differ based on the methods (or locations) of installation and the material property at the initial stages of installation (prior to curing). Most cases of a direct injection are subject to a high influence from a continuous hydrostatic pressure even after complete installation but are not commonly affected by thermal stress (as is shown in Table 1). Due to the difference in the below-grade structure environment and the material types in Korea, the expected exposure level of degradation factors in leakage cracks only takes into account the study results obtained for the analytic hierarchy process (AHP) statistics data in Korea [9]. Refer to Table 1 for details.  
Figure 3: Grout injection in a below-grade concrete structure for the repair of a waterproofing layer: (**a**) the direct injection method and (**b**) the backfill injection method.  
Based on the above three types of expected exposure levels (\(\bullet\): High, \(\blackblackblack\): Moderate, and \(\odot\): Low) outlined in Table 1, the following grout-injection repair materials were selected as specimens for assessing the physical properties change (waterproofing performance). The grout-injection material properties and specifications are introduced in the following Section 2.2.