esa-sceva/satcom-chunk-collection huggingface dataset:
Impact Resistance and Flexural Performance Properties of Hybrid Fiber-Reinforced Cement Mortar Containing Steel and Carbon Fibers  
Jong-Gun Park  
1 Public Safety Research Center (PSRC), Konyang University, 121, Daehak-ro, Nonsan-si  
they are classified into carbon fiber (CF), glass fiber (GF), and basalt fiber (BF) [17; 18; 19; 20]. On the other hand, macro-fibers have a length of 25 to 60 mm and a diameter of 0.2 to 0.8 mm, in such types as steel fiber (SF) and polyvinyl alcohol fiber (PVAF) [21; 22; 23].  
Figure 1 shows the action of the hybrid fibers of different sizes in relation to crack bridging occurring in the cement matrix. As seen in Figure 1, the micro-fibers are cross-linked at the stage of micro-cracks, and at the stage where these micro-cracks develop into macro-cracks, the macro-fibers cross-linked in the cracks, which is expected to improve flexural performance and impact resistance. In other words, short micro-fibers more effectively control micro-cracks, while relatively long macro-fibers more effectively control large cracks [24; 25; 26].  
Although MFRCM has been used only for a single fiber, various studies have been conducted recently on HyFRCM, which can maximize the effect that a single fiber cannot exert if two or more fibers with different material properties are used in an appropriate ratio. Even in the case of HyFRCM containing different combinations of SF and CF, it is considered to be very effective in terms of improving mechanical properties, such as impact resistance, flexural performance, and compressive strength, but the problem is that information on fiber combinations is too limited. In consideration of constructability and economic feasibility particularly, fibers with a fiber combination of 1.0% or less are widely applied to the construction of concrete structures, but research on this is necessary.  
Therefore, it is intended in this study to evaluate such properties as flexural performance, compressive strength, and impact resistance of HyFRCM containing SF and CF with the goal of developing FRCM containing fibers with different material properties to obtain a synergistic effect that cannot be achieved with single fibers alone. In addition, their performance is compared and reviewed with plain mortar (PM). For this objective, the optimal fiber combination of HyFRCM incorporated with SF and CF was examined.
## 1 Introduction  
Lung ultrasound (LUS) is used to quickly and precisely differentiate between the most common causes of respiratory problems. It has been extensively studied as a bedside diagnostic tool and is now universally included in point-of-care ultrasound (PoCUS) guidelines with high-quality supporting evidence [1]. LUS has the potential to refashion healthcare delivery as it enables an augmented clinical interpretation of a patient's status in real time, which could have an immediate impact on clinical decisions, and even be used to monitor response to therapy and evolution [2; 3; 4]. Moreover, LUS imaging is typically less expensive than conventional chest X-ray or computed tomography (CT), making it convenient for locations with limited access to these resources [5; 6].  
LUS has demonstrated an ability to provide immediate information on the condition of COVID-19 patients [4; 7]. There are multiple pulmonary manifestations of COVID-19 that can be observed with LUS, such as the presence of pleural effusion, B-line artifacts, or consolidations [8; 9; 10].  
LUS allows physicians to perform a complete bedside chest exam on both mild and severe COVID-19 patients. It is a useful imaging technique for detecting and monitoring the lung involvement as well as prognosis of the disease, and predicting admission to an intensive care unit (ICU) and mortality [3; 4; 7; 11; 12]. Furthermore, LUS reduces the risk of environment cross-infection compared to other imaging modalities as these devices can be more easily cleaned and disinfected after use [6; 13].  
However, LUS is an operator-dependent imaging technique, and its utility depends on accurate acquisition and interpretation by bedside physicians [14; 15; 16; 17]. Poor image acquisition and incorrect identification and interpretation of artifacts are potential sources of error in its clinical application [1]. In previous studies (most of which were conducted before the pandemic), LUS findings showed moderate to fair inter-rater agreement. However, as the observed agreement in the interpretation of frequently occurring events may be due to some extent to chance, more studies with a controlled environment are required to determine the accuracy with which physicians can interpret LUS acquisitions.  
Furthermore, to the best of our knowledge, there are no published studies to date that specifically evaluate the best orientation of the transducer (i.e., transverse or longitudinal) in an LUS acquisition in COVID-19 patients (see Figure 1).  
Our aim was to first characterize the inter-rater agreement of LUS experts when evaluating the main findings for COVID-19. Our hypothesis was that kappa agreement in ultrasound artifacts and diagnostic interpretation would be substantial, based on the high agreement in other clinical scenarios. We also evaluated the impact of the transducer orientation in LUS acquisitions in COVID-19 patients on the observed findings.
## 4 Analysis and Interpretation of Results
### Dependence of the Occurrence of Negative Prices on RES Generation
#### 4.2.6 Estonia: Small Market but Sensitive Response  
The graph for Estonia (Figure 7) shows a clear dependence of the share of negative prices on the combined production of wind and solar resources. Although the range of capacities is considerably smaller than in the other countries analysed (maximum ~1000 MW), negative prices appear relatively frequently in certain bands. The share of negative prices increases significantly between 400 and 700 MW, peaking in the 700-800 MW band, where negative prices accounted for more than 7% of the cases. This increase may be due to a combination of low demand, limited system flexibility and occasional surpluses at times of low load. In the bands above 800 MW, there is a sudden drop in the share of negative prices to zero, which is probably not related to market stabilisation, but rather to the low data sample (specifically, only 9 times)--i.e., such generation values occurred rarely or not at all during the period analysed.  
Estonia has limited generation and transmission capacity, which means that even a modest increase in RES production may hit system limits. Low export volumes and less connectivity to larger markets mean that surpluses from RES have to be mostly consumed in Estonia. Even for a small market like Estonia, an increase in RES generation may be associated with an increased risk of negative prices. The results show that already at 700-800 MW there is significant price pressure, indicating low system flexibility and the absence of effective mechanisms to smooth out surpluses. This case highlights the importance of local flexibilities, cross-border interconnections and active demand management to absorb fluctuations in RES generation without price extremes.  
Figure 6: Share of negative price in % in the Czech Republic.  
The research results show that the extent of negative electricity prices is strongly correlated with the extent of generation from renewable sources, namely wind and solar. In large systems with high absolute RES production, such as Germany and Poland, there is a significant increase in the probability of negative prices when a certain capacity threshold is exceeded. In the case of Germany, this threshold is situated at around 65,000 MW, while above 195,000 MW the share of negative prices is already as high as 100%. A similar effect, albeit with different dynamics, is observed in Poland. Conversely, in smaller systems such as Slovakia or Estonia, negative prices arise even at significantly lower capacities (as low as 300 MW). Another important observation is the shape of the relationship between generation and the occurrence of negative prices. In the case of Germany, we observe an exponential increase, indicating a strong non-linear load on the system in times of high surplus. In contrast, Slovakia and Estonia, as small and less complex systems, exhibit either a monotonically increasing trend (Slovakia) or a steep increase (Estonia).
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 1 Introduction
### Contributions  
In this work, we designed FIDO2 CAP: FIDO2 Captive-portal Authentication Protocol. Our proposal is to adapt FIDO2 authentication to the network authentication performed via a captive portal. Then, based on our previous work [12], we present a prototype of the OpenNDS captive portal using FIDO2 CAP. Our system makes the authentication in captive portals resistant to common attacks for which password-based systems are vulnerable.  
For validation, we installed our prototype in the networking hardware. We validated the compatibility with different operating systems and web browsers, and we conducted a usability experiment with 15 users.  
Therefore, our contributions can be summarised as follows:  
* **A novel protocol that integrates FIDO2 with captive-portal authentication: FIDO2 CAP.** We provide a design of the FIDO2 Captive-portal Authentication Protocol (FIDO2 CAP). We based our architecture design in the captive portal specification RFC 8952 [8], and then we specified the authentication and registration protocols.
* **Prototype of our protocol with OpenNDS**. We developed, tested and validated with 15 real users a prototype implementation of our protocol. For this, we used existing captive portal software known as OpenNDS.
## 2 Results  
### Free Amino Acids in Watermelon Rind, Flesh, and Rind-Flesh Blends  
A total of 16 free amino acids were identified and quantified in the five watermelon samples. The identified amino acids included seven essential amino acids (Phe, Ile, Val, Leu, Met, His, and Lys), while His and Lys only had trace levels. Two other essential amino acids (Thr and Trp) were not detected. The remaining nine amino acids were Arg, Cit (citrulline), Ala, Ser, Pro, Gly, Asp, Asn, and Orn. Other non-essential amino acids such as Glu, Gln, Thr, Tyr, and Trp were also analyzed, but they were not detected in the five samples. The sum of all amino acids in each sample ranged 127-165 mg/100 g fresh weight (0.13-0.17%), as shown in Figure 1. The rind alone contained the significantly (\(p\leq 0.001\)) highest amount of total free amino acids (165 mg/100 g fresh weight), while flesh alone had 146 mg/100 g fresh weight. The concentration of each free amino acid had a wide range in the five watermelon samples, as shown in Table 1. Cit had the highest concentration, ranging between 39.7 mg/100 g (0.04%) fresh weight in the 0% rind sample (entire flesh) to 61.4 mg/100 g (0.06%) fresh weight in 100% rind, approximately a 1.5-folddifference. The rind-flesh blends had Cit concentrations in between the samples with flesh only and rind only. A similar trend was observed for Arg. The 0% rind sample contained 36.7 mg/100 g fresh weight, while the 100% rind sample had 53.8 mg/100 g fresh weight, which was again found to be about a 1.5-fold difference. Among the identified seven essential amino acids, Phe, Ile, Val, and Leu had concentrations above 1.0 mg/100 g fresh weight (Table 1). The 0% rind sample (flesh only) contained the highest amount. As rind increased in the samples, these essential amino acids decreased; the lowest concentrations were in the 100% rind sample. The flesh only sample had about 4.1 times higher total essential amino acids than the rind only sample. Additionally, five other amino acids with concentrations higher than 1.0 mg/100 g fresh weight were Ala, Ser, Pro, Asp, and Asn (Table 1). The concentration for Ser and Asp increased along with the rind percentage in the samples; 100% rind had 2.4-3.5 times the content found in flesh alone. The concentrations for Ala, Pro, and Asn decreased when rind potency increased; the difference between only rind and only flesh samples was 1.2-2.1 times. Amino acids are not only associated with nutritional value, but also have the potential to contribute bitter, sweet, and umami tastes, although some are tasteless. The most dominant amino acids in the five samples, Arg and Cit, are associated with bitterness
# Exploring Inner Speech Recognition via Cross-Perception Approach in EEG and fMRI
## 5 Results
### Extended Study
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**CMU-MOSEI**} & \multicolumn{4}{c}{**CMU-MOSI**} \\ \cline{2-9}  & **Acc-7 \(\uparrow\) (\%)** & **Acc-5 \(\uparrow\) (\%)** & **Acc-2 \(\uparrow\) (\%)** & **MAE \(\downarrow\)** & **Acc-7 \(\uparrow\) (\%)** & **Acc-5 \(\uparrow\) (\%)** & **Acc-2 \(\uparrow\) (\%)** & **MAE \(\downarrow\)** \\ \hline TFN (2018) [28] & 50.2 & - & 82.5 & 0.593 & 34.9 & - & 80.8 & 0.901 \\ LMF (2018) [29] & 48.0 & - & 82.0 & 0.623 & 33.2 & - & 82.5 & 0.917 \\ Mult (2019) [30] & 52.6 & 54.1 & 83.5 & 0.564 & 40.4 & 46.7 & 83.4 & 0.846 \\ Self-MM (2021) [31] & 53.6 & 55.4 & 85.0 & 0.533 & 46.4 & 52.8 & 84.6 & 0.717 \\ MMIM (2021) [32] & 53.2 & 55.0 & 85.0 & 0.536 & 46.9 & 53.0 & 85.3 & 0.712 \\ TFR-Net (2021) [33] & 52.3 & 54.3 & 83.5 & 0.551 & 46.1 & 53.2 & 84.0 & 0.721 \\ AMM (2022) [34] & 52.4 & - & 85.3 & 0.614 & 46.3 & - & 84.9 & 0.723 \\ LGCCT (2022) [35] & 47.5 & - & 81.1 & - & - & - & - & - \\ EMT (2023) [36] & 54.5 & 56.3 & 86.0 & 0.527 & 47.4 & 54.1 & 85.0 & 0.705 \\ LMR-CBT (2024) [37] & 51.9 & - & 82.7 & - & 41.4 & - & 83.1 & 0.774 \\ CMFH (2024) [38] & 52.8 & 54.4 & 84.5 & 0.548 & 37.2 & 42.4 & 81.7 & 0.907 \\ CRNet (2024) [39] & 53.8 & - & 86.4 & 0.541 & 47.4 & - & 86.4 & 0.712 \\ \hline Ours & **62.6** & **64.7** & **88.0** & **0.457** & **60.3** & **60.5** & **86.5** & **0.614** \\ \hline \hline \end{tabular} \(\uparrow\) indicates that higher values represent better performance for the corresponding metric. \(\downarrow\) indicates that lower values represent better performance for the corresponding metric. \(\uparrow\): Many models did not provide Acc-5 results in their original papers. The Acc-5 data used in this comparison were obtained from [36]. Bold values indicate the best performance for each metric.  
\end{table} 
Table 7: Results on the CMU-MOSEI and CMU-MOSI datasets. structure leverages both modality-specific and shared representations, allowing the model to adapt to the varying information requirements of different emotion recognition tasks. These findings demonstrate that our Cross-Perception Model, initially designed for inner speech recognition, can be effectively applied to other multimodal tasks such as sentiment analysis. This highlights the versatility and robustness of our proposed architecture in capturing and integrating information from multiple modalities across different domains.
## Nomenclature  
\begin{tabular}{l l} CHP & Combined Heat and Power \\ Coeffpressure & Pressure loss coefficient \\ C\({}_{\text{p}}\) & Inlet burner air specific heat capacity \\ D\({}_{\text{H}_{\text{B}}-\text{H}_{\text{O}}}^{\text{eff}(1)}\) & Effective binary diffusivity through the anode support \\ D\({}_{\text{H}_{\text{B}}-\text{H}_{\text{O}}}^{\text{eff}(1)}\) & Effective binary diffusivity through the anode functional layer \\ D\({}_{\text{O}_{\text{O}_{\text{-N}_{\text{2}}}}-\text{H}_{\text{O}}}^{\text{eff}(1)}\) & Effective binary diffusivity through the cathode current collector \\ D\({}_{\text{O}_{\text{2}}-\text{N}_{\text{2}}}^{\text{eff}(2)}\) & Effective binary diffusivity through the cathode functional layer \\ Dm\({}_{\text{A,in}}\) & Inlet air mass flow \\ Dm\({}_{\text{A,out}}\) & Outlet air mass flow \\ Dm\({}_{\text{eject}}\) & Ejected air mass flow \\ Dm\({}_{\text{H,in}}\) & Inlet hydrogen mass flow \\ Dm\({}_{\text{H,out}}\) & Outlet hydrogen mass flow \\ Dm\({}_{\text{O,in}}\) & Inlet oxygen mass flow \\ Dm\({}_{\text{O,out}}\) & Outlet oxygen mass flow \\ En\({}_{\text{Nerst}}\) & Nerst potential \\ F & Faraday constant \\ FCV & Fuel cell vehicle \\ I & Current \\ ICE & Internal combustion engine \\ i & Current density \\ i\({}_{\text{0,anode}}\) & Anode limiting current density \\ i\({}_{\text{0,cathode}}\) & Cathode limiting current density \\ i\({}_{\text{L}}\) & Limiting current density \\ l\({}_{\text{a}(1)}\) & Anode support thickness \\ l\({}_{\text{a}(2)}\) & Anode functional layer thickness \\ l\({}_{\text{c}(1)}\) & Cathode current collector thickness \\ l\({}_{\text{c}(2)}\) & Cathode functional layer thickness \\ M\({}_{\text{H}}\) & Hydrogen molar mass \\ N & Number of cells \\ P & Stack output power \\ P\({}_{\text{A,in}}\) & Inlet air pressure \\ P\({}_{\text{A,out}}\) & Outlet air pressure \\ P\({}_{\text{comp}}\) & Compressor required power \\ P\({}_{\text{generator}}\) & Produced power by the generator \\ P\({}_{\text{H,in}}\) & Inlet hydrogen pressure \\ P\({}_{\text{H2}}\) & Average hydrogen pressure \\ P\({}_{\text{H2}}^{\prime}\) & Hydrogen pressure on the electrolyte surface \\ \end{tabular}
## 3 Performance Verification and Results
### Performance Verification
#### 3.1.2 Bite EMG  
The setup proposed in this research uses three input channels to implement four directional controls. In this case, the bite EMG was further processed to signal a change in direction during movement. The mechanism used is similar to a double click in computer terminology. The bite EMG was scanned for the event, shown in Figure 6, as a double-click. If no event occurs, the included EMG is used as a standard input signal to go up or down, depending on the selected direction.  
A double-click event registers if a first threshold (\(T1\)) is crossed, a rest (\(T2\)), and a corresponding crossing of the second threshold (\(T3\)) within the specified time. In this case, we use a duration of 30 s to complete one cycle. These are shown in Figure 5(b) as \(T1\), \(T2\), and \(T3\).
## 4 User-Centric Multiple Incentive Mechanism
### Participation Assessment Algorithm
#### 4.2.2 The Behavior of Purchasing Content  
Users within the NexoNet may purchase content by sending transactions to unlock desired content, with the paid amount flowing into the content's revenue pool, serving as direct financial remuneration for the creators. The acquisition of content represents a pivotal avenue through which the NexoNet actualizes its value. Purchasers gain access to the desired content, thereby fueling the creative zeal of authors and augmenting the vibrancy of user participation in the system. Consequently, the act of purchasing content is also regarded as a method of contributing to the system through participatory behavior. Upon confirmation of a purchase transaction, users are eligible to receive the system's Purchase Participation (PP) reward.  
\[\left\{\begin{array}{l}\tau_{2}=\frac{\Delta T}{\omega_{2}}\\ \tanh\tau_{2}=(e^{\tau_{2}}-e^{-\tau_{2}})/(e^{\tau_{2}}+e^{-\tau_{2}})\\ PP=\sum_{1}^{\tau_{2}}\alpha_{pp}\times\tanh\tau_{2}\times price_{i}\end{array}\right. \tag{2}\]  
\(n_{2}\) denotes the number of contents a user publishes. \(\alpha_{pp}\) signifies the maximum PV attainable from a single publication. Similar to (1), for the participation of publishing content, the hyperbolic tangent function \(\tanh\tau_{2}\) is employed to curtail the frequency of content purchases, aiming to restrain the malicious behavior to amass PV through voluminous purchase transactions. Should the interval between two successive purchases by a user  
Figure 2: Effect of the hyperbolic tangent function on the PV.  
be notably short, the quantity of PV rewards \(PP\) received will be diminished and thus mitigate the profitability of such actions. The term \(price_{i}\) represents the amount paid by a user per content purchase, indicating that acquiring works of higher value garners greater PV rewards, thereby encouraging the circulation of high-value content in the NexoNet.
# Sparse Low-Rank Based Signal Analysis Method for Bearing Fault Feature Extraction
## 2 Prior Knowledge of Sparsity and Low-Rank Characteristic  
In stationary operating conditions, periodic impulses as displayed in Figure 1 will be produced if a defect occurs on the bearing outer. The repetitive impulses show sparsity in the time domain. Moreover, the repetitive impulses excite resonance of the bearing, and there are similarities among the resonances. Meanwhile, the time-frequency diagram of the repetitive impulses after short-time Fourier transform (STFT) is shown in Figure 2. The similarity among the STFT coefficients of the repetitive impulses in the resonance band makes the STFT coefficient matrix show a low rank characteristic. The coefficients are sparse in the time-frequency diagram. However, the environmental noise does not show a low-rank characteristic and sparsity. Therefore, a low-rank sparse model based on prior knowledge of sparsity and a low rank characteristic has great potential for fault feature extraction from a noisy signal.
## 3 Result and Discussion
### Overall Results and Discussion  
Based on China's new generation of high spatial resolution Earth observation satellites, this paper constructs the MRSDBG dataset and quantitatively analyzes the impact of different improvement strategies of MFA-UperNet on mangrove segmentation accuracy. For feature extraction, MFA-UperNet uses ConvNeXt, a pure convolutional network, as the encoder. Compared to Swin Transformer, ConvNeXt achieves a 0.7% accuracy improvement on the ImageNet dataset and demonstrates stronger feature extraction capabilities for high-resolution remote sensing images, performing better than different backbone networks like ResNet and PIDNet. In terms of multi-scale and feature pyramid improvements, the CPFM and MSKAM modules designed through the Feature Pyramid Network can effectively address the complex and variable background of mangroves and large-scale variations, with the addition of CPFM increasing mIoU and mPA by 4.64% and 0.59%, respectively, and the MSKAM improving them by 1.47% and 1.03%. For edge segmentation optimization, the AEN module enhances the model's segmentation performance through fully convolutional neural networks and hybrid CLloss function, with the addition of AEN module and CLloss improving mIoU and mPA by 0.52% and 0.23%, respectively. In comprehensive comparison experiments with existing methods, our MFA-UperNet model achieved improvements of 0.87% and 0.43% in mIoU and mPA, respectively, compared to Swin-UperNet; compared  
Figure 13: The segmentation result graph of the different modules superimposed on the MRSDBG dataset. (White boxes are used to highlight the differences in the results under different methods.)  
to DeepLabv3+, it achieved improvements of 2.82% and 1.49%. Furthermore, compared to representative methods such as PIDNet, K-Net, and PSPNet, our model also achieved significant performance improvements. These comprehensive experimental results fully demonstrate the excellent performance and effectiveness of MFA-UperNet in mangrove semantic segmentation tasks. Meanwhile, this research has also improved the mangrove monitoring and assessment system in Beihai City, promoting the enhancement of dynamic monitoring capabilities for mangrove ecosystems.