esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Description and Mineralogy  
The selected outcrop (Figure 2A) is a section with a depth of four meters with a thin coated cemented sandy level at the bottom, and a quaternary colluvial deposit or artificial (man-made) ground deposits at the top (Figure 2B). Its top is at an altitude of 730 m.a.s.l, whereas the top of the tertiary facies reaches 850 m.a.s.l. Therefore, erosion of no less than 120 m can be deduced. In the lower part of the section, two clayey and silty levels can be identified: level 8 and level 6 (Figure 2C,D). They are green-colored, plastic, stiff clays, up to one meter thick, with a coarse sand level between them (level 7). At the top, high-competence and continuous layers appear at level 1, level 3, and level 5 (Figure 2B). They are ten to thirty centimeters thick, showing among them two silty sand levels--level 2 and level 4--on which some discontinuities can be observed.  
The XRD analysis indicates the presence of phyllosilicates, quartz, calcite, dolomite, and alkali feldspar. On the oriented aggregates, smectite, illite, and palygorskite were identified in the clay fraction (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Level** & **Quartz** & **Calcite** & **Dolomite** & **K-Feldspar** & **Smectite** & **Illite** & **Palygorskite** \\ \hline
6 & 16 & 1 & 9 & 2 & 14 & 29 & 29 \\
8 & 11 & 9 & 3 & 3 & 8 & 33 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mineralogy of the clay levels identified within the DC Formation. The mineralogical semi-quantification was also performed using the oriented aggregates (OA) and after applying the method of reflective powers.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Discussion-Attention Based Models  
Attention-based approaches have proven to be highly effective in Video Captioning, enabling models to focus on the most relevant visual and temporal information within videos. From traditional Encoder-Decoder frameworks with single-head attention to advanced Transformer models with multi-head attention, the field has witnessed significant progress. Furthermore, the integration of semantic information and object relationships has further enriched the generated captions, pushing the state-of-the-art in this exciting domain. As we continue to explore more sophisticated attention mechanisms and their applications, the potential for creating truly intelligent video description systems remains vast and promising.  
Figure 14: The proposed method, aLSTMs (redrawn by the authors, adapted from [44]), includes: (1) an LSTM visual encoder for video frame processing, (2) an attention-based LSTM for word generation with key feature focus, and (3) a cross-view module aligning sentences with visual content. For demonstration, words are color-coded to indicate their most relevant video frames.
## 10 Future Directions
### Federated Learning Protocol Standardization  
As FL continues to grow, there is a need for standardized protocols and frameworks to ensure interoperability between different systems and devices. Developing common standards can facilitate better collaboration and integration across various platforms, enhancing the overall security and efficiency of FL implementations.
# Virtual Simulation and Experiment of Quality Inspection Robot Workstation
## 5 Defect Detection Experiments in the Virtual Environment  
### Defect Detection Algorithm Model  
The industrial quality inspection robot utilizes machine vision to recognize defect types on workpieces and employs the improved YOLOv7 algorithm for part defect detection. YOLO series object detection algorithms have been widely used in the field of computer vision because of their high efficiency and real-time performance. YOLOv7 is the most advanced algorithm in the YOLO series, which has been improved in accuracy  
Figure 4: Quality inspection robot workstation workflow simulation.  
and speed [20; 21]. In the aspect of defect detection, the YOLOv7 algorithm still has the disadvantage that it is difficult to detect because the detected target is small and the features are not obvious. On the foundation of the YOLOv7 algorithm, the improved YOLOv7 algorithm incorporates the Convolutional Block Attention Module (CBAM) attention module into the feature fusion network. The CBAM incorporates both spatial attention and channel attention mechanisms, gradually refining feature maps. This approach emphasizes important features while suppressing less relevant information. The model architecture is illustrated in Figure 5. The improved YOLOv7 algorithm effectively solves the problems of small defect areas on the metal surface and significant distribution changes in the image and greatly improves the accuracy of defect detection.  
After incorporating the Convolutional Block Attention Module (CBAM) into the feature fusion network of the improved YOLOv7, the structure of the defect detection algorithm is depicted in Figure 6.
# Evaluating the Effectiveness of Designs for Low-Cost Digital Manufacturing Systems
## 5 Evaluation of Designs
### Quantifying the Effectiveness of Reference Architecture Based Designs
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & & & & \\ Size of architecture elements & 56.7\% & 55.3\% & 50.2\% & 56.5\% & 74.9\% & 17.6\% \\ Pct. of architecture elements overlap & 41.7\% & 41.7\% & 41.7\% & 41.7\% & 41.7\% & 44.3\% \\ No. architecture elements & 3 & 3 & 3 & 3 & 11 \\ No. additional components & 0 & 0 & 0 & 1 & 0 \\ No. connected components & 10 & 10 & 10 & 10 & 54 \\ \hline No. system components & 7 & 7 & 7 & 7 & 23 \\ No. auxiliary and interface components & 0 & 0 & 0 & 0 & 4 \\ Size of auxiliary and interface components & 9.1\% & 8.9\% & 8.1\% & 9.1\% & 12.6\% & 5.6\% \\ \hline \end{tabular}
\end{table} 
Table 5: Effectiveness evaluation of the designs based on Shoestring, WoT and PROSA/Erlang. gramming language as the two integrated solutions, which vastly simplifies connecting architecture elements to individual components. Whereas PROSA/Erlang requires designers to use Erlang/OTP for the development of its architecture elements, a WoT design could also be created using the same programming language as Shoestring and the two digital solutions, which would reduce the number of required auxiliary and interface components. However, this feature of WoT is outdated, and as a result, developers risk a longer design process. Similar to the size of architecture elements, WoT outperforms Shoestring and PROSA/Erlang because its software framework allows for a minimal interface. Specifically, two LOCs are needed for sending an HTTP request and processing its response. By comparison, the PROSA/Erlang relies on TCP/IP sockets for its designs, while Shoestring uses an additional communication layer to send messages from the component to the network level. Therefore, the size of the interface components is much larger for these two candidates. An exception forms the integration scenario because the additional interface components required for the WoT and PROSA/Erlang designs carries over to the overall size of the interface components. The overall design effectiveness can now be determined by comparing the columns (i.e., the metric values for each design scenario) in Figure 2 and Table 5. In summary, the designs based on Shoestring are more effective compared to those based on PROSA/Erlang and WoT, since they require the least design effort for the considered low-cost digital design scenarios. However, compared to WoT, Shoestring suffers from a more complex design in some scenarios due to its focus on reusability. Additionally, due to its software framework, WoT requires the least effort when designing the interfaces for connecting the system components to the architecture elements.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## Conflicts of Interest: The authors declare no conflict of interest. Abbreviations  
The following abbreviations are used in this manuscript:  
**Acronym** **Description**  
AATSR Advanced Along-Track Scanning Radiometer  
ADEOS Advanced Earth Observing Satellite  
ADIOS Aircraft Deployable Ice Observation System  
AIRS Atmospheric Infrared Sounder  
ALOS Advanced Land Observing Satellite  
AMM Arithmetic Mean Model  
AMSR-E Advanced Microwave Scanning Radiometer for EOS  
AMSR2 Advanced Microwave Scanning Radiometer 2  
ANN Artificial Neural Network  
ARGO Array for Real-time Geostrophic Oceanography  
ASCAT Advanced SCATerometer  
ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer  
ATLAS Advanced Topographic Laser Altimeter System  
AUV Autonomous Underwater Vehicle  
AV Aquatic Vegetation  
AVIRIS Airborne Visible / Infrared Imaging Spectrometer  
AVHRR Advanced Very High Resolution Radiometer  
BT Brightness Temperatures  
CDOM Colored Dissolved Organic Matter  
Chl Chlorophyll  
Chl-a Chlorophyll-a  
CNN Convolutional Neural Networks  
CONAE Comision Nacional de Actividades Espaciales  
CR Coral Reef  
DEM Digital Elevation Model  
DL Deep Learning  
DOC Dissolved Organic Carbon  
ECOSTRESS ECosystem Spaceborne Thermal Radiometer Experiment on Space Station  
EKE Eddy Kinetic Energy  
ENVISAT ENVironmental SATellite  
ERS European Remote Sensing  
ESA European Space Agency  
EUMETSAT European Organization for the Exploitation of Meteorological Satellites  
GAM Generalized Additive Model  
GBM Gradient Boosting Method  
GEO Geostationary Orbiters  
GF GaoFen-2  
GHRSST Group for High Resolution Sea Surface Temperature  
GLCM Gray Level Co-occurrence Matrix  
GMI GPM Microwave Imager  
GNSS Global Navigation Satellite Systems  
GOCI Geostationary Ocean Color Imager  
GOES Geostationary Operational Environmental Satellite  
GPS Global Positioning System  
HF High Frequency  
HH Horizontal transmit and Horizontal receive  
HSI Habitat Suitability Index  
IASI Infrared Atmospheric Sounding Interferometer  
ICESat Ice, Cloud, and land Elevation Satellite  
IOPs Inherent Optical Properties  
JASON Joint Altimetry Satellite Oceanography Network  
LEO Low-Earth Orbiters  
LiDAR Light Detection and Ranging
## 4 Discussion
### Limitations and Suggestions for Further Work  
The pelvic bone is a complex structure and a pivotal one; it acts as the stress-bearing unit in the body and is central to various movements. That is why there is a plethora of features that could be analyzed for reconstruction success. So far, the parameters analyzed were chosen for their direct impacts on critical functional outcomes. Through discussions with expert orthopedic surgeons and biomedical engineering scientists, it was concluded that a supplementary parameter, the pelvic ring continuity, identified for its stress-distribution influence [25], would provide additional insight into the surgical outcomes. Given the static and dynamic forces acting on the pelvis, an ideal reconstruction would ensure a shape that uniformly distributes the generated stress and shear. Otherwise, incomplete pelvic ring reconstruction may lead to degeneration of the sacroiliac joint or endoprosthetic breakage [84,85]. That is why, in the future, more understanding of the biomechanics of the pelvis under daily living activities will be exploited for improved custom 3D-printed implant design [86]. From the theoretical and technical knowledge gleaned from this work, a proposed method for analysis is a distance map analysis. Since the healthy pelvis anatomy is shaped for ideal stress distribution, measuring the superimposition of the reconstructed bones from both procedures would give a strong indication of which is better.  
The number of patients involved was sufficient to yield statistical significance. To further uncover the advantages of 3D-printed customized implants, a bigger cohort would suffice, given that other aspects of this study are satisfactory.  
Every aspect of both surgical procedures were uniform to limit the impacting factors. This comparison, with the presence of an expert surgeon, provided clear evidence that 3D-printed customized implants can match the output level of a senior surgeon. It would thus follow to conduct this comparison with a junior surgeon to understand whether the accuracy and precision of the 3D-printed customized implant is dependent on the expertise of a senior surgeon during its placement.  
Additionally, the customizable nature of 3D-printed designs means that there are various patterns and textures that are available. An important part of implant success is the integration of the implant material with the hosting bone. Thus, it would prove insightful to analyze the most effective surface designs in avoiding foreign body rejection.  
In the future, bio-printing can be incorporated and tested with this procedure to investigate the possibility of promoting the repair process of the reconstructed bone [87,88] by pre-printing the implant with bio-inks containing growth factors, cells, and bio-materials.
## 4 Results and Discussions
### Emerging Digital Technologies
#### 4.1.3 Immersive Technologies
By blending digital and physical realities, XR enables full immersion (VR), contextual overlays (AR), and the interactive coexistence of virtual and real elements (MR), thereby enhancing knowledge retention, motivation, and overall training effectiveness. In short, immersive technologies such as VR, AR, and XR are transforming training by enabling immersive, hands-on learning and realistic hazard scenario simulations, as supported by the reviewed literature. These technologies facilitate experiential safety training and environmental simulation. Moreover, they offer safer and more engaging alternatives to conventional training methods, improving hazard awareness, decision-making, and skill transfer. Their use enhances risk awareness, skill retention, and cognitive learning, especially in hazardous or confined environments. Recent studies have confirmed that VR scenarios improve hazard recall and learning retention, while AR enhances cognitive support and task accuracy in real-time settings. Additionally, their interactive and scalable nature makes them highly effective for safety training in diverse industrial settings.
## Results
### Elevation vs. Phenology Parameters from Satellite NDVI Data  
The correspondences between elevation and phenological parameters are shown in Figure 9 and Table 5. Positive \(\rho\) values for SOS means that SOS occurs later with increasing elevation, whereas negative \(\rho\) values for EOS, LOS and SI means that EOS occurs earlier, LOS is shorter, and SI is smaller with increasing elevation. DL produced the highest absolute maximum and median \(\rho\) values (Table 5 and Table S4). The smallest ranges of \(\rho\) were generated with AG and DL. The ranges of \(\rho\) produced by AG and DL were much smaller than those produced by SG, LO and SP. CV was able to generate similar or higher absolute \(\rho\) for LO and SP in most cases, and for SP results from
## 5 Experiment
### Results  
The first aim of the experiment is to verify the efficiency of high-quality sample generation. The tests are performed 10 times on 12 software programs, each time for 10 h. We observe the change in the average number of high-quality samples that are generated over time. The results are presented in Figure 5. In general, the result of PSOFuzzer is better. For libjpeg, binutils and ngiflib, the number of high-quality samples generated by PSOFuzzer is substantially greater than that generated by AFLGo. For tar, binutils, libping and ffipeg, the number of high-quality samples generated by PSOFuzzer is substantially greater than that generated by Sidewinder.  
The second aim of the experiment is to verify the effectiveness of PSOFuzzer in triggering vulnerability. We analyzed whether fuzzers can trigger vulnerabilities each time. When a vulnerability is triggered multiple times in a single test, the trigger is recorded only once. The results are shown in Table 3. PSOFuzzer triggered vulnerabilities more than 4 times for each software. AFLGo triggered vulnerabilities fewer times, and Sidewinder triggered the least vulnerabilities. For binutils and libjpeg, AFLGo did not trigger any vulnerabilities. Sidewinder only triggered vulnerabilities in gzip, miniftp and ngiflib.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Software** & **Type** & **Version Number** & **CVE ID** & **Vulnerability Position** \\ \hline gzip & compressor & 1.2.4 & 2009–2624 & inflate.c line 763 \\ tar & compressor & 1.14 & 2007–4476 & safer\_name\_suffix.c line 1046 \\ pdfresurrect & pdf analyzer & 0.15 & 2019–14,267 & pdf.c line 237 \\ miniftp & file transfer & 1.0 & - & parseconf.c line 62 \\ binutils & binary utilities & 2.32 & 2019–14,444 & readf.c line 13347 \\ libming & flash library & 0.4.8 & 2019–9113 & decompiler.c line 381 \\ libjpeg & jpeg library & 9a & 2018–11,213 & rdppm.c line 153 \\ pngquant & png compressor & 2.7.0 & 2016–5735 & rwppng\_read\_image24\_libpng.c line 238 \\ ffjpeg & encoder & 24 February 2020 & 2020–13,438 & jfif.c line 748 \\ zziplib & decoder & 0.13.67 & 2018–6381 & mmapped.c line 685 \\ exiv2 & metadata library & 0.26 & 2018–5772 & image.c line 468 \\ ngiflib & gif decoder & 0.4 & 2018–10,677 & ngiflib.c line 808 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Software information.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **Gzip** & **Tar** & **Minittp** & **Pdfresurrect** & **Binutils** & **Libjpeg** & **Libming** & **Pngquant** & **Ffjpeg** & **Zziplib** & **Exiv2** & **Negiflib** \\ \hline AFLGo & 5 & 8 & 5 & 3 & 0 & 0 & 2 & 4 & 1 & 6 & 10 & 2 \\ PSOFuzzer & 8 & 10 & 6 & 7 & 4 & 6 & 5 & 7 & 4 & 8 & 10 & 6 \\ Sidewinder & 7 & 0 & 6 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of times(out of 10) that a vulnerability is triggered.
## 4 Experimental Results
### Embedding Capacity and Visual Quality Analysis  
In this section, the performance of embedding capacity and visual quality is compared in three ways. First, the performance of the proposed method with [PERSON] et al. [7], [PERSON] et al. [11], and [PERSON] et al. [15] are shown in Table 4. Secondly, the embedding capacity vs visual quality performance over the complete USC-SIPI dataset is shown in Table 5. Finally, the performance of the proposed method is compared with the most recent LSB-based methods (in Figure 4). The embedding capacity is calculated as the maximum number of secret data bits that can be embedded into the cover image. Bits per pixel are determined using Equation (1), where \(W\) and \(H\) are the width and height of the cover image respectively. PSNR is adopted to evaluate the visual quality or evaluate the distortion of the cover images after embedding secret data. The PSNR is computed as shown in Equation (2). A high value of PSNR illustrates a low degree of image distortion. Conversely, a small PSNR value indicates a larger distortion between the cover image and the stego image. \[\text{bpp}=\frac{\text{Embedding capacity}}{W\times H} \tag{1}\]  
\[\text{PSNR}=10\ \log_{10}\frac{255^{2}}{\text{MSE}} \tag{2}\]  
Mean square error (MSE) is the measure of an average of the squares of the difference between the pixel intensities of the stego and the cover images. That is defined in Equation (3), where \({p^{\prime}}_{i}\) and \(p_{i}\) are the pixel values of the stego and cover images, respectively. The smaller the MSE value, the better the image quality; the higher the MSE value, the greater the distortion. MSE is inversely proportional to the PSNR, which demonstrates that the higher the value of PSNR, the lower the MSE value produced. So, a higher PSNR would be considered as good or an indication of low image distortion. \[\text{MSE}=\sum_{i=1}^{W\times H}\frac{{({p^{\prime}}_{i}-p_{i})}^{2}}{W \times H} \tag{3}\]  
Figure 4: Performance comparison of proposed and recent data embedding methods.
## 3 Results and Discussion
### Kinetic Model for Water Color Changes
\[\mathrm{d}[\mathrm{Color}]/\mathrm{dt}=\mathrm{k}_{\mathrm{f,color}}\alpha_{ \bullet}[\mathrm{SMX}]-\mathrm{k}_{\mathrm{d,color}}([\mathrm{Color}]-[ \mathrm{Color}]_{\mathrm{final}}) \tag{13}\]  
being,  
\([\mathrm{Color}]_{0}\): initial color of aqueous solution of \(\mathrm{SMX}\) (AU). \([\mathrm{Color}]\): color of water (AU). \([\mathrm{Color}]_{\mathrm{final}}\): color in the water at the end of the treatment (AU). \(\mathrm{k}_{\mathrm{f,color}}\): first-order kinetic constant for the color formation in water (1/min). \(\mathrm{k}_{\mathrm{d,color}}\): first-order kinetic constant for the color degradation in water (1/min). Integrating Equation (13), the kinetic equation for the water color changes of the aqueous SMX solutions oxidized by photo-Fenton treatment is shown in Equation (14). \[[\mathrm{Color}]=[\mathrm{Color}]_{\mathrm{final}}+\left[\frac{\mathrm{k}_{ \mathrm{f,color}}[\mathrm{SMX}]_{0}}{\mathrm{k}_{\mathrm{d,color}}-\mathrm{k}_{ \mathrm{SMX}}}\right]\left[\alpha\mathrm{exp}(-\mathrm{k}_{\mathrm{SMX}}\, \mathrm{t})-\mathrm{exp}(-\mathrm{k}_{\mathrm{d,color}}\,\mathrm{t})\right] \tag{14}\]  
From the experimental results obtained, the kinetic parameters of the proposed equation have been adjusted, obtaining the values shown in Table 2. The estimates made allow us to verify that as the Fe(II) concentration increases, the rate of color formation and removal also increases, suggesting a greater generation of hydroxyl radicals that attack the compounds responsible for the coloration (Equations (15) and (16)). The dependence of the \(\alpha\) parameter (Equation (17)) and the initial and final color of the treated water (Equation (18)) on the catalyst concentration used has also been estimated. \[\mathrm{k}_{\mathrm{f,color}}=0.00005[\mathrm{Fe}(\mathrm{II})]_{0}^{2}+0.0008 [\mathrm{Fe}(\mathrm{II})]_{0}+0.0007 \tag{15}\]  
\[\mathrm{k}_{\mathrm{d,color}}=0.0022[\mathrm{Fe}(\mathrm{II})]_{0}^{2}-0.0013[ \mathrm{Fe}(\mathrm{II})]_{0}+0.0495 \tag{16}\]  
\[\alpha=-0.0085[\mathrm{Fe}(\mathrm{II})]_{0}^{2}+0.008[\mathrm{Fe}(\mathrm{II })]_{0}+1.0971 \tag{17}\]  
\[[\mathrm{Color}]_{\mathrm{final}}/[\mathrm{Color}]_{0}=-0.0289[\mathrm{Fe}( \mathrm{II})]_{0}^{2}-0.4856[\mathrm{Fe}(\mathrm{II})]_{0}+3.2503 \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**[Fe(II)]\({}_{0}\)** & \(\mathrm{k}_{\mathrm{f,color}}\) & \(\mathrm{k}_{\mathrm{d,color}}\) & **[Color]\({}_{0}\)** & **[Color]\({}_{\mathrm{final}}\)** & \(\alpha\) \\
**(mg/L)** & **(1/min)** & **(1/min)** & **(AU)** & **(AU)** & **(AU L/mg)** \\ \hline
0.5 & 0.0010 & 0.045 & 0.007 & 0.021 & 1.090 \\
1.0 & 0.0015 & 0.055 & 0.007 & 0.020 & 1.100 \\
2.0 & 0.0019 & 0.060 & 0.014 & 0.031 & 1.105 \\
3.0 & 0.0035 & 0.060 & 0.010 & 0.014 & 1.020 \\
4.0 & 0.0027 & 0.0