esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemicals Screening  
#### 3.1.1 Aqueous Stability Results  
An aqueous stability test was conducted for a total of 42 surfactant solutions at reservoir conditions as the first stage of the screening process. The surfactant solutions were checked for transparency after 7 days of aging at reservoir temperature of 63 \({}^{\circ}\)C. The 1S stability test showed that all concentrations except 0.1 wt% were stable (Figure 3a) and, thus, the 0.1 wt% concentration was discarded. As demonstrated in Figure 3b, the 2S solutions were unstable at surfactant concentrations higher than 0.1 wt%, as indicated by milky appearance and precipitation. Thus, only 0.1 wt% 2S was selected for the phase behavior study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test ID** & **Test Type** & **Injection Sequence** \\ \hline
4 & Surfactant Flooding (SF) & FW \(\rightarrow\) Oil \(\rightarrow\) CSW \(\rightarrow\) SF \\
5 & Alkali—Surfactant (AS) Flooding & FW \(\rightarrow\) Oil \(\rightarrow\) CSW \(\rightarrow\) ASF 1  \\
6 & NPs—Surfactant (NS) Flooding & FW \(\rightarrow\) Oil \(\rightarrow\) CSW \(\rightarrow\) NSF 2  \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sequence of oil displacement experiments.  
After standalone 2S was tested for aqueous stability, the aqueous stability tests were conducted with co-surfactant 2A to evaluate stable surfactant/co-surfactant ratios. The preliminary tests showed that higher co-surfactant percentages resulted in better stability. Hence, 2S/2A ratios of 10/90, 20/80, 30/70, 40/60, and 50/50 were selected at overall concentrations of 0.5, 1.0, 1.5, and 2.0 wt%. All samples remained stable at reservoir temperature and thus were selected for further tests. Figure 4 shows the aqueous stability performance on the 7 th day for 1.5 wt% and 2.0 wt% concentrations.  
For the aqueous stability analysis of six 3S surfactants, the samples were diluted 10 times and 20 times with CSW. However, none of these dilutions were stable with seawater, as suggested by the high precipitation after the 7 th day in Figure 5. Consequently, all 3S solutions were discarded from the study.  
Figure 4: Aqueous stability results for several 2S/2A ratios at overall concentration of (**a**) 1.5 wt% and (**b**) 2.0 wt%.  
Figure 3: Aqueous stability results for (**a**) 1S and (**b**) 2S at 7 th day of aging at 63 \({}^{\circ}\)C.
## 3 Datasets
### Reference LiDAR Data  
The LiDAR data used as reference in this study were the same as the data used by [PERSON] et al. [19]. They were a point cloud in LAS binary file format v. 1.2 [39] collected on 23 September 2015 with orthometric elevations using the EGM2008 geoid. The registered point density of the test area was 0.97 points/m\({}^{2}\) considering the four returns. The nominal horizontal (RMSE\({}_{\text{2D}}\)) and vertical (RMSE\({}_{\text{Z}}\)) accuracies had values lower than 0.3 m and 0.2 m, respectively [40].  
The LiDAR data for the nine selected subareas were carefully edited by manually removing the points that did not belong to the DSM [19]. Finally, a spatially oriented data thinning was carried out by sub-sampling the edited point cloud using a minimum distance between points of 2 m. This LiDAR was finally used for satellite-based DSM validation. It is important to note that the number of points in the LiDAR data after processing ranged from 20,588 to 58,909 for each of the nine selected subareas shown in Figure 1. The greenhouse subareas (GH1, GH2 and GH3) presented 58,909, 51,920 and 56,000 points, respectively. The urban subareas (UR1, UR2 and UR3) presented 46,677, 50,679 and 25,794 points, respectively, while the bare soil subareas (BS1, BS2 and BS3) had 45,087, 20,588 and 42,815 points, respectively.
# UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
## 3 SFM Preliminaries  
We obtain a video stream from the AUV's camera, although we need to extract key frames from the video stream as the image sequence for the 3D reconstruction. However, building the structure from all of the frames is not realistic; therefore, we develop the process shown in Figure 2 to convert the video stream to an image sequence that describes the preliminaries for the SFM system. Firstly, we filter out the blurred frames from the video stream and obtain the initial frames. Next, a key frame extraction method based on clustering is used to extract key frames from initial frames. At last, we perform the image enhancement step on the key frames to obtain the final image sequence.
# Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection
###### Abstract  
Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model's enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.  
expert system; smart city; artificial intelligence; real-time application; violence detection; image-to-image stable diffusion; edge computing; MediaPipe; YOLO v7; LSTM +
Footnote †: journal:  
Article
# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
###### Abstract  
In this study, we proposed a YOLOv8-based Multi-Level Multi-Head Attention mechanism utilizing EO and IR cameras to enable rapid and accurate detection of vessels of various sizes in maritime environments. The proposed method integrates the Scale-Sensitive Cross Attention module and the Self-Attention module, with a particular focus on enhancing small object detection performance in low-resolution IR imagery. By leveraging a multi-level attention mechanism, the model effectively improves detection performance for both small and large objects, outperforming the baseline YOLOv8 model. To further optimize the performance of IR cameras, we introduced a color palette preprocessing technique and identified the optimal palette through a comparative analysis. Experimental results demonstrated that the Average Precision increased from 85.3 to 88.2 in EO camera images and from 68.2 to 73 in IR camera images when the Black Hot palette was applied. The Black Hot palette, in particular, provided high luminance contrast, effectively addressing the single-channel and low-resolution limitations of IR imagery, and significantly improved small object detection performance. The proposed technique shows strong potential for enhancing vessel detection performance under diverse environmental conditions and is anticipated to make a practical contribution to real-time maritime monitoring systems. Furthermore, by delivering high reliability and efficiency in data-constrained environments, this method demonstrates promising scalability for applications in various object detection domains.  
EO/IR camera; maritime monitoring; real-time detection; vessel detection; Scale-Sensitive Cross Attention; self-attention; multi-level multi-head attention; small object detection; color palette +
Footnote †: journal: Applied sciences
## 2 Design Specifications  
In this section, we present the design specifications and the structure of the proposed antenna. Table 1 summarizes the design goal of our multi-GNSS antenna. The first six parameters, from frequency range to axial ratio, are essential performance criteria required of a GNSS antenna. Antenna size is typical of a single antenna that can meet design specifications in Table 1. GNSS RF signals are transmitted in the RHCP (right-hand circular polarization). The frequency range of 1.16-1.61 GHz is set to cover the worldwide GNSS currently in operation (1.164-1.300 GHz and 1.559-1.610 GHz).  
For impedance-matched performance, a reflection coefficient of less than \(-10\) dB is specified. For wide beamwidth operation, the minimum antenna gain at zenith (\(\theta=0^{\circ}\)) needs to be relaxed to 3 dBic. The minimum antenna gain at the horizon (\(\theta=90^{\circ}\)) is set to \(-4\) dBic, reflecting the physical realizability in a given dimension. An omnidirectional gain of 3 dBic over the hemispherical space and no radiation (minus infinite gain) below the horizon can be obtained only from an ideal imaginary antenna, which occupies an infinitely large space. The axial ratio of less than 6 dB is specified over the upper hemispherical angles (\(-90^{\circ}\leq\theta\leq 90^{\circ}\) and \(-90^{\circ}\leq\varphi\leq+90^{\circ}\)), not just on the principal planes. This value is also selected, reflecting the realizability. The normal axial ratio of 3 dB is difficult to achieve at all angles in the upper hemisphere so that it is relaxed to 6 dB. Assuming a 1 dB axial ratio for the GNSS transmitting antenna onboard a satellite, the former corresponds to 0.23 dB in the maximum polarization mismatch loss, while the latter corresponds to 0.64 dB [27].  
Antenna types capable of meeting design specifications in Table 1 include crossed dipoles [21; 22; 23; 24; 25; 26], four-point fed patches [28], four horizontal arc monopoles [29], and quadrifilar helices [30]. In this work, we choose wideband crossed-dipoles with parasitic monopoles fed by a broadband quadrature hybrid coupler realized on the dipole ground plane of optimum size. This structure requires only three printed circuit boards (PCBs) that can be easily assembled by inserting PCBs in slots and using soldering PCBs at  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Specification** \\ \hline Frequency range (GHz) & 1.16–1.61 \\ \hline Input reflection coefficient (dB) & \(<\)\(-\)10 \\ \hline Polarization & RHCP \\ \hline Gain at zenith (dBic) & \(>\)3 \\ \hline Horizontal gain (dBic) & \(>\)\(-\)4 \\ \hline Axial ratio (dB) & \(<\)6 (in the upper hemisphere) \\ \hline Size (\(\lambda\) at center freq.) & \(<\)0.5 \(\times\) 0.5 \(\times\) 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design specifications of the proposed antenna.  
suitable locations. The proposed antenna with a wide beamwidth is amenable to low-cost production and, for example, can be attractive for small fishing boats that can constantly undergo rolling and pitching.
## 3 Smart Sensor Monitoring Approach
### Tool Wear Estimation Based on Recurrent Neural Networks
Following the graph in Figure 9, the hidden state of the cell is computed following Equation (10):  
\[h_{t}=tanh(W_{h}x_{t}+r_{t}\otimes U_{h}h_{t-1}) \tag{10}\]  
The hidden state of the cell is a nonlinear combination of the linear combination of the input sequence at time t, \(x_{t}\), given by the matrix multiplication between the input vector at the time step t and a matrix, \(W_{h}\), plus the Hadamard product (8) between the linear combination of the reset gate output \(r_{t}\) and the hidden state at the previous time step, \(h_{t-1}\), using the matrix \(U_{h}\); in this way, what to remove from previous time steps is determined. Finally, the z gate updates the hidden state value at time t, \(h_{t}\), considering which information of the past needed to be conserved. From a mathematical perspective, this is obtained using Equation (11). \[h_{t}=z_{t}\otimes h_{t-1}+(1-z_{t})\otimes h_{t} \tag{11}\]  
From a computational perspective, the GRU architectures can store and filter the information using their update and reset gates in a simpler way compared to LSTM architectures, providing similar or even better performance depending on the applications.
# Address Space Layout Randomization Next Generation
## 3 ASLR Limitations
### Stacks  
There are two different kinds of stacks, namely the stack of the main process, and the stack of the threads or clones (since both thread and cloned entities handle stacks in a similar way, in what follows we will refer to them jointly as 'thread stacks'). The main stack is still considered and handled as a growable object.  
Initially, thread stacks were'set up' to be growable. Flags MAP_GROWSDOWN and MAP_GROWSUP were added to the mmap() request, to tell the kernel about expected behavior. Inevitably, these flags were removed [29] because of security problems and intrinsic logical limitations. Nowadays, thread stacks are treated as regular (non-growable) objects, reserved with the maximum estimated size when the threads are created. By default, the thread stack size is set to 8 Mb (in Debian and Ubuntu), but it can be changed by using the RLIMIT_STACK resource with the setrlimit() system call. Note that the RLIMIT_STACK value is used as the default thread stack size rather than an upper limit.  
A summary of the facilities provided by the system (compiler and standard library), to deal with growable stack issues, is presented below:  
* One or more protected pages (page guards) are placed at the end of the thread stack. If the stack overflows, then the process receives a SIGSEGV signal. This guard is further enforced by the GCC flag-fstack-check, which emits extra code to access sequentially all the pages of the stack, thus preventing overflowing by jumping over the page guard.
* The split stack feature (GCC flag-fsplit-stack) generates code to automatically continue the stack in another object (created via mmap()), before it overflows. As a result, the process has discontinuous stacks which will only run out of memory if the program is unable to allocate more memory. This is an interesting feature for threaded programs, as it is no longer necessary to calculate the maximum stack size for each thread. This is currently only implemented in the i386 and x86_64 back-ends running in GNU/Linux.  
Figure 9: Classic memory layout.  
* It is possible to ask the compiler to print stack usage information for the program, on a per-function basis, using the -fstack-usage flag and making an estimation of stack size.  
Although these facilities are very helpful when dealing with stacks, in practice most applications work with the default 8 Mb sequential stack (Google Chrome(r), LibreOffice, Firefox, etc.). Only very demanding applications have stack size issues, which are typically handled by slightly increasing the RLMIT_STACK limit value. For example, Oracle(r) advises to set it to 10 MB when running its database.
## 4 Experimental Results
### Performance of DOA Estimation Model
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **50 Snapshots** & **80 Snapshots** & **100 Snapshots** & **200 Snapshots** & **500 Snapshots** \\ \hline MUSIC & 63\% & 68\% & 70\% & 84\% & 95\% \\ ASMUSIC & 66\% & 70\% & 73\% & 88\% & 96\% \\ DeepMUSIC & 76\% & 78\% & 79\% & 89\% & 96\% \\ ILSSE & 76\% & 79\% & 80\% & 89\% & 96\% \\ CNN & 76\% & 80\% & 83\% & 92\% & 97\% \\ LSTM & 70\% & 74\% & 75\% & 91\% & 97\% \\ CNN-LSTM & 81\% & 85\% & 87\% & 94\% & 98\% \\
**CNN-LSTM+SAM** & **86\%** & **89\%** & **90\%** & **96\%** & **100\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction accuracy of DOA estimates as snapshots increase and with SNR being 5 dB. Figure 11: _Cont_. Figure 10: DOA estimation performance when the first DOA is set to \(\theta_{1}\in[-90^{\circ},89^{\circ}]\) with SNR being 0 dB and the number of snapshots being T = 500. (a) DOA estimate of MUSIC. (b) DOA estimate of ASMUSIC. (c) DOA estimate of DeepMUSIC. (d) DOA estimate of ILSSE. (e) DOA estimate of CNN. (f) DOA estimate of LSTM. (g) DOA estimate of CNN-LSTM. (h) DOA estimate of CNN-LSTM+SAM.
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 3 Adaptive Beamforming Model Based on CNN
### Network Model Architecture
Unlike ReLU, which produces unbounded outputs, Softmax inherently generates probabilistic weights by pushing higher values toward 1 and lower values toward 0. This property not only enforces the MVDR optimization objective but also suppresses contributions from noise-corrupted channels during beamforming. The proposed CNN architecture is a novel design tailored for Lamb wave beamforming, integrating time-space mapping, anti-rectifier activation, and Softmax-based weight normalization to address the challenges of adaptive imaging. In this framework, the input to the CNN beamformer is the Time of Flight Corrected (ToFC) data, while the output of the CNN beamformer is the corresponding apodization weights of each channel for different imaging points in the imaging region. These apodization weights are then multiplied with the ToFC data from the input layer and summed along the channel axes to produce the final beamforming image. For clarity, the pseudocode of the proposed CNN-based adaptive beamforming work  
Figure 2: The proposed CNN network architecture. Algorithm: CNN-based adaptive beamforming workflow. Input: Multi-channel Lamb wave signals \(\mathbf{z}\). Output: Damage image \(I_{CNN}\). (1) Preprocess input signals:  
- Apply Time-of-Flight Correction (ToFC) to align scattered signals. - Extract real and imaginary parts (channel dimension: 16 for 8 receivers). (2) Feed ToFC data into the CNN:  
- Input dimensions: 201 \(\times\) 101 \(\times\) 16. - Process through four convolutional layers (7 \(\times\) 7 kernels, anti-rectifier activation). - Apply batch normalization and L2 regularization. (3) Predict apodization weights \(\mathbf{w}_{CNN}\):  
- Softmax activation ensures \(\sum\mathbf{w}_{CNN}=\mathbf{1}\). (4) Compute pixel values:  
- \(I_{CNN}=\mathbf{w}_{CNN}^{\mathbf{T}}\cdot\mathbf{z}\). (5) Generate the final image by aggregating pixel values across the grid.
## 3 Result and Discussion
### Effect of the Adiabatic Section Length on the Performance of the PHP
#### 3.3.2 Effect of the Adiabatic Section Length on the Thermal Performance of the PHP  
To compare the heat transfer performance of the PHP with different lengths of the adiabatic section, the thermal resistances of the PHP with different lengths of the adiabatic section are presented in Figure 13. It can be seen that when the heat input was low, the PHP with shorter adiabatic length showed lower thermal resistance. As the heat input increased, the thermal resistance of the PHP with longer adiabatic length became smaller than that of the PHP with shorter adiabatic length. The reason for this was as follows: When the length of the adiabatic section was small, the start-up performance of the PHP with shorter adiabatic section length was significantly better than that of the PHP with longer adiabatic section length. Thus, the thermal resistance of the PHP with 60 mm adiabatic section length showed the lowest thermal resistance at the heat input of 20 W. However, when the heat input increased, the start-up performance of the PHP was improved, and the effect of the adiabatic length on the start-up performance reduced. In addition, when the adiabatic length of the PHP increased, the mass of liquid slugs increased at the same filling ratio. With more liquid slugs in the PHP, the evaporation section of the PHP was more easily wetted by the liquid slugs, and the convective heat transfer was enhanced. Thus, in the calculation range of this work, the heat transfer performance of the PHP with longer adiabatic section length was better at high heat input.  
Figure 12: The temperature fluctuation of the PHP at 40 W with different lengths of the adiabatic section: (**a**) 60 mm, (**b**) 120 mm, (**c**) 180 mm, and (**d**) 240 mm.  
Figure 13: The thermal resistance of the PHP varied with the heat input.
## 1 Introduction
A relevant issue, therefore, is to investigate what performance can be expected from such services, i.e., what latency, resource consumption, and reliability (i.e., percentage of reached destinations within the broadcast domain) are in order. This paper extends [10] by presenting a thorough discussion and evaluation of the various factors that determine the performance of MDB. Moreover, we discuss the impact on the performance of MDB of different network conditions, such as varying UE density, presence of selfish users, or the occurrence of near-simultaneous broadcasts related to the same event. Last, but not least, we assess the performance of a real-life service, i.e., the diffusion of alerts in a vehicular network scenario, run on MDB. Our results confirm that MDB consumes few resources, that it is reliable, i.e., is able to reach most of the UEs, and that the latency involved is tolerable, even when the target area is quite large. The rest of the paper is organized as follows: Section 2 reports background information. Section 3 reviews the related work. Section 4 discusses the role of UEs and eNBs in MDB. Section 5 reports performance evaluation results, and Section 6 concludes the paper.
# Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer
## 5 Conclusions  
FOXM1 is widely overexpressed in human cancer, where its expression is associated with genomic instability. Three major mechanisms driving increased FOXM1 expression in cancer are FOXM1 gene amplification, Tp53 mutation, and Rb-E2F deregulation. FOXM1c is the highest expressed isoform in both normal tissues and cancer. FOXM1b and FOXM1c are transcriptionally active, while FOXM1a is not.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2072-6694/11/2/251/s1](http://www.mdpi.com/2072-6694/11/2/251/s1)]([http://www.mdpi.com/2072-6694/11/2/251/s1](http://www.mdpi.com/2072-6694/11/2/251/s1)), Figure S1: FOXM1 amplifications + copy number gains in TCGA cancer types, Figure S2: FOXM1 copy number correlations with FOXM1 mRNA and protein expression in CCLE cancer cell lines, Figure S3: FOXM1 expression in TCGA aneuploidy clusters, Figure S4: FOXM1 isoform expression in human cancer cell lines, Figure S5: FOXM1 isoform transcriptional activity in FT282 cells, Figure S6: FOXM1 promoter activity and endogenous mRNA expression following FOXM1 knockout or exogenous FOXM1 overexpression in human cell lines, Table S1: Summary of TCGA cancer types, Table S2: FOXM1 copy number, mRNA, and protein expression in CCLE cell lines, Table S3: RNA-seq differential expression analysis related to Figure 9C-F, Table S4: Lists of genes in Venn Diagram related to Figure 9E, Table S5: Lists of genes in Venn Diagram related to Figure 9F, Table S6: Lists of primers and sequences.  
[PERSON] designed experiments, generated data, analyzed/interpreted data, and prepared the manuscript; [PERSON] and L.C. generated data; [PERSON] supervised the project, interpreted data, and prepared the manuscript.  
This work was supported by The Fred & Pamela Buffett Cancer Center, NCI P30 CA036727, NIH T32 CA009476, NIH/NCI F99 CA212470, and a UNMC Program of Excellence Assistantship.  
The authors thank the UNMC Epigenomics, Genomics and Flow Cytometry Core Facilities. We thank [PERSON] for assistance with RNA-seq data and data deposits, and [PERSON] for assistance with CCLE data. We thank [PERSON] for assistance with RT-qPCR. We thank [PERSON] (University of Illinois at Chicago) for the 6X-FOXM1 promoter reporter construct, [PERSON] for the PGL3-FOXM1 promoter reporter construct and [PERSON] (Netherlands Cancer Institute) for the pFash-FOXM1 promoter reporter construct.  
The authors declare there are no conflicts of interest.