esa-sceva/satcom-chunk-collection huggingface dataset:
# Emotion Recognition from Videos Using Multimodal Large Language Models
## 5 Experimental Results
### Quantitative Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Mean Pearson** \\  & **Correlation** \\ \hline Baseline\({}_{FAU}\) & 0.2840 \\ Baseline\({}_{VGGFac72}\) & 0.2488 \\ ViPER-V & 0.2712 \\ ViPER-VATF\({}_{CLIP}\) & **0.3025** \\ \hline Video-LLaVA\({}_{Quarying}\) & 0.0937 \\ Video-LLaVA\({}_{ probing1}\) & 0.2333 \\ Video-LLaVA\({}_{ probing2}\) & 0.2351 \\ ViPER-VATF\({}_{Video-LLaVA_{1}}\) & 0.3004 \\ ViPER-VATF\({}_{Video-LLaVA_{2}}\) & 0.3011 \\ ViPER-VATF\({}_{LLaVA}\) & 0.2895 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean Pearson correlations achieved by proposed methods. The higher result is highlighted in boldface. \begin{table}
\begin{tabular}{c c} \hline \hline
**Activation** & **Mean Pearson** \\
**Function** & **Correlation** \\ \hline Linear & 0.2338 \\ ReLU & 0.2351 \\ Leaky ReLU & **0.2353** \\ Tanh & 0.2332 \\ Sigmoid & 0.2343 \\ GELU & 0.2315 \\ ELU & 0.2326 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Activation function impact in the probing network. Higher result is highlighted in boldface. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Textual** & & & & & & & \\
**Features** & **Adapation** & **Amusement** & **Anxiety** & **Disgust** & **Empathic Pain** & **Fear** & **Surprise** & **Average** \\ \hline CLIP & 0.2575 & **0.3651** & 0.3294 & **0.2755** & 0.2824 & **0.3190** & 0.2890 & **0.3025** \\ Video-LLaVA\({}_{1}\) & 0.2575 & 0.3646 & **0.3303** & 0.2632 & **0.2886** & 0.3122 & 0.2865 & 0.3004 \\ Video-LLaVA\({}_{2}\) & **0.2624** & 0.3631 & 0.3297 & 0.2708 & 0.2763 & 0.3155 & **0.2918** & 0.3011 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Single-emotion Pearson correlation depending on the textual features employed in the ViPER-VATF [9] approach. The higher result, separately for each emotion, is highlighted in boldface.
## 5 D-MSR Management Phases  
In this section we discuss the different management phases, which guide the new node from startup to the moment the node starts to publish/subscribe the periodic sensor data in the network. The node operation state machine is shown in Figure 11.  
After a new node startup, in Phase-1 the node receives the activation command from the neighboring advertiser and starts to send the advertisement. In Phase-2 the initial communication resources between the node and its adjacent neighbor are allocated, by which the routing layer in Phase-3 can establish the routing graph. In Phase-4, the required communication resources should be allocated in the network to exchange the management messages in the routing layer. After construction of the routing graph and allocation of management resources, the end-to-end connection can be established between the sensors and actuators/gateway to publish the sensor data toward the destination(s) which is done in Phase-5. At the network setup stage each node goes through these phases. This procedure continues until all the devices have joined the network and started the operation. During normal operation of the network, in Phase-6, the D-MSR maintains the end-to-end connections by coping with dynamicity, by handling the resources reservation conflict, and  
Figure 11: Different states of a node operation in the network.  
by coping with internal and external interference. The following sections discuss these phases in more detail.
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 5 Review on ECS Methods on WBAN
### Forward Error Correction (FEC)
Hence, the results do not take the code rate in to consideration i.e., do not consider the overhead that is caused by ECS. The decoding algorithm is performed by the BP algorithm, the cost that is associated with BP decoding is not presented, and how this decoding algorithm is suitable for energy-constrained WBAN scenarios is not clear. Furthermore, it is also not clear what type of networks are considered during the simulation. The paper proposed a low complexity version of bit flipping algorithm, called modified implementation efficient reliability ration WBF for LDPC code for implant to implant communication channel in WBAN [42]. The authors presented a decoding process in the proposed algorithm. Subsequently, the performance of the proposed decoder is compared with various versions of LDPC decoders, including the min-sum, WBF, and various versions of WBF in terms of BER and the required number of iterations required to converge against \(E_{b}/N_{0}\). The performance of the proposed code is better than other versions of WBF decoding algorithms in both factors. Additionally, the paper presented required the decoding time against \(E_{b}/N_{0}\) for different rates LDPC codes with several version of WBF, min-sum, and the proposed algorithm. The decoding time for the proposed code lays medium in range when compared with others. The work that is presented in [42] is limited to implant to implant node communication channel only and there is only comparison with different versions of LDPC decoders. However, there is a lack comparison with other types of codes that has similar complexity and is considered for WBAN. The authors introduce a framework to connect two WBANs using UMTS in [43] that supports various rates according to QoS requirements. The authors combined convolutional code in WBAN with UMTS codes of cellular in the proposed framework. In the proposed framework, the medical data uses the adaptive convolutional code and UMTS code, whereas non-medical data only use the UMTScode. The proposed code rates for convolutional code are half, one-third, and a quarter for higher QoS, medium QoS, and lower QoS medical data, respectively. The soft-decision decoder Viterbi algorithm is used in the decoding operation in the proposed system. However, the complexity that is introduced by the use of soft-decision decoder is not analyzed. The theoretical upper bound bit error probability for up and downlink channels when considering AWGN and Rayleigh channels are presented without comparing with other results. The result is a standalone result without taking any consideration of other WBAN and interferences. A summary of FECs based ECS is presented on Table 5. From the literature review, we observed that all of the proposed ECS were considering only a single WBAN without considering interference from other WBAN that is not realistic or practical setting. More research has to be conducted when considering multiple WBANs or considering the interference from other existing WBANs.
## 4 Related Works and Contemporary VSN Projects
### Sensor Network Virtualization Projects
#### 4.1.10 WebDust  
WebDust [56] deals with a generic and modular environment that develops applications for wireless sensor networks. It allows the application implementor to create a customized environment that will provide a wide range of services for wireless sensor networks. This section presents open architecture of WebDust, the most important design decisions, and discusses its distinct features and functionalities. WebDust stores the information extracted from the wireless sensor network in a database. It offers extendable statistics and provides a set of web-based user interfaces that allow the designer to present the information in different ways based on the needs of the application. This environment allows automatic registration of motes and the devices technical characteristics in heterogeneous, hierarchical sensor networks. The main strengths behind WebDust are: (i) the administrator can easily setup and control the network as motes register themselves automatically. (ii) multiple, heterogeneous, wireless sensor networks can be controlled as a single, unified, virtual sensor network. (iii) many users can simultaneously query, monitor, and visualize the execution of the wireless sensor network through a Web-based user interface.  
In most of the project, emphasizes on the following aspects of the sensor network have been encountered in connection with the virtualization of sensor networks:  
* Networking Technology: A few sensor network virtualization prototypes have been developed for specific sensor networking technologies with an aim to exploit unique characteristics of these sensor networks to enable virtualization.
* Layer of Virtualization: Considering the present wireless sensor network, researchers have naturally approached network virtualization in a hierarchical manner. For this reason different projects have attempted to virtualize different layers of sensor network protocol stack. It is found as the design of an abstract layer.
* Architectural domain: Initially most of the researchers have focused on the WSN architecture domains, which dictate the design choices made in the construction of architectures and services that can be offered on those platforms.
* Level of virtualization: To enable virtualization, it is a must to virtualize the tiny sensor nodes, links, and other resources in the sensor network. The level of virtualization refers to the granularity at which each virtual node can administer itself.
## 4 Discussion
### Potential Applications of Observation Space Divergence Analysis  
The observation space divergence analysis presented in this study extends beyond understanding the problem of performance variability in DRL; it can be directly utilized to devise solutions for improving evaluation performance in practical applications. Specifically, the findings of this research can be applied to ensemble methods--a widely used technique for surpassing the performance of single agents [14]--from two perspectives.  
First, for improving evaluation performance via ensemble methods. When ensembling N agents to boost performance, a novel approach can be explored beyond conventional techniques, such as majority voting, simple averaging, or weighted sums based on rewards.  
Specifically, one could employ a weighted sum based on the similarity between the representation of the current observation and the observation space representation of each agent in the ensemble. This method would give priority to the actions of agents whose experiences are most similar to the current situation, thereby potentially leading to more sophisticated and higher performance.  
Second, for analyzing why ensemble effectiveness varies across environments. Our findings provide insight into predicting whether an ensemble will be effective in a given environment. For instance, in an environment like Freeway, where all agents explore a highly similar observation space, we can predict that the benefit of ensembling will be minimal because the individual agent policies are already convergent. In contrast, in an environment like Boxing, where agents learn distinct strategies (e.g., aggressive vs. defensive) resulting in clearly separated observation spaces, we can anticipate a significant performance boost from ensembling, as combining their different specializations would be highly complementary. This provides a practical guideline for determining where to focus limited computational resources when constructing ensembles.
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 7 Hadron Beam Lines
### Particle Identification Instrumentation  
The particle identification instrumentation in the transfer line needs to take into account the hadrons rate, the background and the momentum range of the particles. In the case of low energy beams, the material budget imposed by the instrumentation needs to be taken into consideration, too.  
For hadron momenta lower than \(\leq\)10 GeV/c, particle identification can be achieved employing threshold Cherenkov counters and Time-Of-Flight (TOF) systems. The threshold pressure curves for a typical Cherenkov counter used at the CERN experimental areas filled with CO\({}_{2}\) can be seen in Figure 8. A standard technique employed at the CERN North Area to identify all charged particles above 4 GeV/c makes use of two threshold Cherenkov counters, one filled with CO\({}_{2}\) and another one with R218 or R134a gases [129; 130] and is described in Ref. [131].  
For hadron momenta below 4 GeV/c, a TOF measurement ensures better performance. As an illustration, the separation capability among hadrons is shown in Figure 9. Below 4 GeV/c, kaons are generally missing in the beam due to their short decay length, while the protons can be identified clearly from kaons and pions, and the separation power is inversely proportional to the time resolution of the system.  
In all cases above, the rate of the hadrons in the beamline and the material budget seen by the secondaries are crucial parameters. The aforementioned particle-identification (PID) techniques are effective for intensities significantly lower than \(O(10^{11})\) particles per spill, limited by the pile-up in the scintillators as well as the TOF resolution. At the same time, these instruments contribute very little to the material budget--about 0.2 radiation lengths (\(X_{0}\))--thus they do not contaminate or attenuate the hadron beam. Their use for reliable particle identification was sustainable in early neutrino beams, but these devices cannot stand the power of modern short and long-baseline facilities. However, even in very high intensity facilities, important information on the effectiveness of the focusing, the size of the background (e.g., from interactions in the slits or the magnet's apertures), the momentum  
Figure 8: Threshold pressures of a threshold Cherenkov counter typically used in the CERN experimental areas. For energies above 4 GeV/c and using two counters, one at 5 bar and another e.g., at 2 bar, protons can be separated by pions. Above 6 GeV/c kaons can be tagged, too. Electrons are always ultra-relativistic and can be identified at any momentum.  
Figure 9: Separation of particles (expressed in standard deviations) for a time-of-flight resolution of 900 ps. Below 4 GeV/c TOFs achieve an excellent separation for protons, kaons and pions.  
spread and the beam composition can be gained employing the instrumentation described above in dedicated low-intensity runs.
###### Abstract  
Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. This study presents a novel algorithm to retrieve day and night CBH using infrared observations of the geostationary Advanced Himawari Imager (AHI). The algorithm is featured by integrating deep learning techniques with a physical model. The algorithm first utilizes a convolutional neural network-based model to extract cloud top height (CTH) and cloud water path (CWP) from the AHI infrared observations. Then, a physical model is introduced to relate cloud geometric thickness (CGT) to CWP by constructing a look-up table of effective cloud water content (ECWC). Thus, the CBH can be obtained by subtracting CGT from CTH. The results demonstrate good agreement between our AHI CBH retrievals and the spaceborne active remote sensing measurements, with a mean bias of \(-0.14\)\(\pm\)\(1.26\) km for CloudSat-CALIPSO observations at daytime and \(-0.35\)\(\pm\)\(1.84\) km for EarthCARE measurements at nighttime. Additional validation against ground-based millimeter wave cloud radar (MMCR) measurements further confirms the effectiveness and reliability of the proposed algorithm across varying atmospheric conditions and temporal scales.  
cloud base height; passive radiometer; active radar and lidar; remote sensing; deep learning 2025172469  
[PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] On the Hybrid Algorithm for Retrieving Day and Night Cloud Base Height from Geostationary Satellite Observations. _Remot Sens._**2025**, 17, 2469. [[https://doi.org/10.3390/rs1742469](https://doi.org/10.3390/rs1742469)]([https://doi.org/10.3390/rs1742469](https://doi.org/10.3390/rs1742469))
## 5 In-Line Application of LIBS in Industrial Production
### Rubber  
Rubber has outstanding material properties such as mechanical elasticity, viscoelasticity, dielectric strength, thermal stability, resisting power against chemicals, morphological flexibility, and durability, and is used in a wide range of applications. About 70% of the annual global production of rubber is used for tire production and retreading [462]. A key parameter for the fabrication of rubber is the concentration of the vulcanization agents Sulfur and Zinc oxide (ZnO). The properties of the material (e.g., elasticity, stiffness, wear) depend on the amount of S and ZnO, and for the production of rubber of high quality the concentrations have to be controlled precisely in the process.  
XRF and PGNAA cannot be employed for in-line measurements in the rubber production due to radiation hazards and other techniques were not available. Recently, LIBS has been employed for the first time to quantify ZnO and S directly in the tire rubber  
Figure 15: LIBS on-site measurement of spent refractory materials. (**a**) Demonstrator of a LIBS-based sorting machine (Photo: Orbix). Adapted from [461]. (**b**) End-of-life refractory brick with a modified surface layer. LIBS measurement spots marked with white rectangle (Photo: Fraunhofer IIT). Adapted from [208].  
production [357]. The system was optimized to measure the optical emission of S and Zn from the rubber plasma in air (Figure 16a). Plasma excitation in collinear double-pulse geometry and detection of plasma emission with time-gated detectors was employed to resolve the weak sulfur lines in the near-infrared range.  
The element S and ZnO were quantified in three different sample materials (natural rubber NR, styrene-butadiene rubber SBR, and butadiene rubber BR) that were prepared from the most important polymers used in production (Figure 16b). The mean error of the prediction of concentrations RMSEP is \(\leq\)0.07 wt% for S and \(\leq\)0.33 wt% for ZnO for all polymer types. The results demonstrated that the vulcanizing system of rubber can be quantified under ambient conditions with a LIBS in-line sensor. Earlier attempts on tire rubber analysis by LIBS in the production are reported in [363,364].
# Effects of Caprolactam Wastewater on Algal Growth and Nutrients Removal by _Arthrospira platensis_
## 3 Results and Discussion
### Investigation of Factors Inhibiting the Growth of _A. platensis_  
The results presented in Section 3.1 showed that all three WCP concentrations had toxic effects on the growth and photosynthetic performance of _A. platensis_. To establish an efficient culture process to remove N and P from WCP, it is helpful to understand which  
Figure 3: Changes in growth inhibition with cultivation time.  
Figure 2: Effects of different caprolactam wastewater concentrations on the growth and maximum quantum yield (Fv/Fm) of _Arthospira platensis_. (**A**) Growth curves of _A. platensis_, (**B**) final biomass concentration, (**C**) growth inhibition rate (%), and (**D**) Fv/Fm. Error bars represent the standard deviation (\(n=3\)). Data with different letters indicate significant differences (\(p<0.05\)) between the control and treatments.  
chemical components in WCP are the major factors affecting the growth of _A. platensis_. According to [PERSON] et al. (2020) [11] and [PERSON] et al. (2017) [14], there are two types of inhibitory factors in WCP. The first type is organic compounds, including raw materials for CPL production, such as CHN, CHL, intermediate products such as cyclohexanoneix (CHO), and end products such as CPL. The second type is peroxides. Therefore, the effects of organic matter and peroxides on the growth of _A. platensis_ were investigated.
# Research on Clustering-Based Fault Diagnosis during ROV Hovering Control
###### Abstract  
The objective of this study was to perform fault diagnosis (FD) specific to various faults that can occur in the thrusters of remotely operated vehicles (ROVs) during hovering control. Underwater thrusters are predominantly utilized as propulsion systems in the majority of ROVs and are essential components for implementing motions such as trajectory tracking and hovering. Faults in the underwater thrusters can limit the operational capabilities of ROVs, leading to permanent damage. Therefore, this study focused on the FD for faults frequently caused by external factors such as entanglement with floating debris and propeller breakage. For diagnosing faults, a data-based technique that identifies patterns according to data characteristics was utilized. In imitation of the fault situations, data for normal, breakage and entangled conditions were acquired, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was employed to differentiate between these fault conditions. The proposed methodology was validated by configuring an ROV and conducting experiments in an engineering water tank to verify the performance of the FD.  
ROV; underwater thruster; hovering control; fault diagnosis; DBSCAN +
Footnote †: journal: selected  
_articles_
## 3 Results and Discussion
### Electrochemical Corrosion Results  
The results obtained for the two types of HEA coatings (I8-6 with 5% Cu and I8-7 with 10% Cu) compared to the uncoated substrate (304L stainless steel) and to HEA ingots are  
Figure 8: Scratch test results on I8-6 coating deposited on stainless steel 304L at (**a**) 200 W, (**b**) 250 W, and (**c**) 300 W; I8-7 coating deposited at (**d**) 200 W, (**e**) 250 W, and (**f**) 300 W.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **HV** \\ \hline Stainless steel 304L uncoated & 318.8 \\ Stainless steel 304L with I8-6 coating & 331.8 \\ Stainless steel 304L with I8-7 coating & 331.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Values of Vickers hardness.  
presented in Table 6 and Figure 9. As shown in Table 6, HEA-based coatings with 10% Cu content improve the corrosion resistance of 304L steel in NaCl solutions at a concentration of 3.5 g/L. The sample I8-7 S300, produced from HEA ingots with 10% Cu at 300 W power, demonstrates the lowest corrosion rate (9.67 nm/year) and the highest polarization resistance (31.31 M\(\Omega\)). It is also evident that the HEA coatings on 304L steel exhibit greater corrosion resistance compared to the alloys from which they were derived. The coating films deposited at 300 W achieved the best values, suggesting that the deposition power level has a bigger influence on the corrosion resistance than the difference in the Cu content of the coatings. However, at 300 W power a higher Cu content significantly improves the corrosion resistance compared to 5% Cu.