esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of the Durability of Concrete Repair Materials
## 3 Results and Discussion
### Accelerated Carbonation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Target Strength** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Notch Width** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{**Repair Method**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Carbonation Depth** \\ **(mm)** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Carbonation Velocity** \\ **(mm/y)** \\ \end{tabular} } \\ \cline{4-7}  & & & **1 Week** & **4 Weeks** & **1 Week** & **4 Weeks** \\ \hline \multirow{8}{*}{20} & - & Intact (no notch)-NN & 6.1 \(\pm\) 1.25 & 9.4 \(\pm\) 2.12 & 2.3 \(\pm\) 1.36 & 1.8 \(\pm\) 0.11 \\ \cline{2-7}  & & Notched (unrepaired)-N & 10.4 \(\pm\) 0.88 & 14.7 \(\pm\) 1.85 & 3.9 \(\pm\) 0.81 & 2.8 \(\pm\) 0.24 \\ \cline{2-7}  & & Epoxy-E & 11.9 \(\pm\) 1.22 & 13.4 \(\pm\) 2.73 & 4.5 \(\pm\) 1.20 & 2.5 \(\pm\) 0.36 \\ \cline{2-7}  & 2 & Impregnant-I & 8.7 \(\pm\) 1.86 & 13.4 \(\pm\) 1.45 & 3.3 \(\pm\) 1.33 & 2.5 \(\pm\) 1.14 \\ \cline{2-7}  & & Epoxy/impregnant-E and I & 9.2 \(\pm\) 2.19 & 9.9 \(\pm\) 0.95 & 3.5 \(\pm\) 1.24 & 1.9 \(\pm\) 0.36 \\ \cline{2-7}  & & Notched (unrepaired)-N & 12.7 \(\pm\) 0.45 & 13.9 \(\pm\) 2.40 & 4.8 \(\pm\) 0.14 & 2.6 \(\pm\) 1.01 \\ \cline{2-7}  & 5 & Epoxy-E & 9.4 \(\pm\) 2.39 & 13.3 \(\pm\) 1.79 & 3.5 \(\pm\) 0.31 & 2.5 \(\pm\) 0.5 \\ \cline{2-7}  & & Impregnant-I & 8.9 \(\pm\) 3.10 & 13.5 \(\pm\) 2.33 & 3.4 \(\pm\) 0.47 & 2.6 \(\pm\) 1.14 \\ \cline{2-7}  & & Epoxy/impregnant-E and I & 11.5 \(\pm\) 1.46 & 11.7 \(\pm\) 1.28 & 4.4 \(\pm\) 0.98 & 2.2 \(\pm\) 1.03 \\ \hline \multirow{8}{*}{40} & - & Intact (no notch)-NN & 2.5 \(\pm\) 1.36 & 4.4 \(\pm\) 1.42 & 1.0 \(\pm\) 0.21 & 0.8 \(\pm\) 0.10 \\ \cline{2-7}  & & Notched (unrepaired)-N & 3.6 \(\pm\) 0.85 & 5.9 \(\pm\) 0.67 & 1.4 \(\pm\) 0.36 & 0.9 \(\pm\) 0.22 \\ \cline{2-7}  & 2 & Epoxy-E & 4.4 \(\pm\) 1.65 & 6.1 \(\pm\) 2.17 & 1.7 \(\pm\) 0.61 & 1.2 \(\pm\) 0.35 \\ \cline{2-7}  & 2 & Impregnant-I & 2.1 \(\pm\) 0.32 & 7.6 \(\pm\) 2.74 & 0.8 \(\pm\) 0.14 & 1.4 \(\pm\) 0.53 \\ \cline{2-7}  & & Epoxy/impregnant-E and I & 1.6 \(\pm\) 0.11 & 5.8 \(\pm\) 1.70 & 0.6 \(\pm\) 0.03 & 1.1 \(\pm\) 0.24 \\ \cline{2-7}  & & Notched (unrepaired)-N & 2.2 \(\pm\) 0.47 & 6.6 \(\pm\) 1.11 & 0.8 \(\pm\) 0.21 & 1.3 \(\pm\) 0.64 \\ \cline{2-7}  & 5 & Epoxy-E & 3.4 \(\pm\) 0.54 & 7.4 \(\pm\) 0.87 & 1.3 \(\pm\) 0.13 & 1.4 \(\pm\) 0.24 \\ \cline{2-7}  & 5 & Impregnant-I & 1.5 \(\pm\) 0.14 & 6.9 \(\pm\) 2.87 & 0.6 \(\pm\) 0.22 & 1.3 \(\pm\) 0.41 \\ \cline{2-7}  & & Epoxy/impregnant-E and I & 1.6 \(\pm\) 0.43 & 5.9 \(\pm\) 2.54 & 0.6 \(\pm\) 0.10 & 1.1 \(\pm\) 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Carbonation depth and velocity rate test results for various concrete specimens. Figure 7: Carbonation velocities for specimens with target compressive strength of (a) 20 MPa and (b) 40 MPa. the reinforcing bar, which can reduce the adhesion performance and significantly shorten the life of the structure.
## 4 Results and Discussion
We used categorical cross entropy as a cost function to analyze loss metrics for multiclass classification. Furthermore, we presented the confusion matrix used to predict correct sign classes and give misclassified samples. It provides accuracy of each sign gesture in terms of percentage. The resulting confusion matrix of both datasets is presented in Figures 10 and 11, respectively. The confusion matrix signifies the actual and the predicted classes in the x and y axes, respectively. The diagonal values in the confusion matrices show the actual recall values of all the signs, and the consistent rows signify the confused classes. Some classes in the ASL dataset have similar gesture signs, such as \"0\" is misclassified with 10.5% as \"O\" due to similar hand gestures with little variations in the position of hands, and \"V\" is misclassified with 14.3% as \"2\". The reason behind this misclassification is the similarity in sign gestures with only a change in the position of the thumb in signs \"2\" and \"V\". As shown in the confusion matrix of the ISL dataset in Figure 9, the class label \"8\" obtained a minimum recognition rate of 92.3% due to misclassification with 2.6% as \"4\" and 5.1% as \"9\" due to similarity in sign gesture with a small change in the number of fingers in case of \"9\" sign gesture and little change in both finger and thumb position in case of \"4\" sign. The numeral class \"2\" was misclassified with \"3\" by 4.7% and recognized with 95% accuracy as it had similar hand gestures but differences in thumb placement with finger and finger orientation. Overall, our model predicted the class labels accurately for both datasets. The  
Figure 8: Chart of precision, recall, F1-score, and accuracy values for the ISL dataset. Figure 7: Chart of precision, recall, F1-score, and accuracy values for Massey ASL. diagonal values in the confusion matrices show the actual recall values of all the signs, and the consistent rows signify the confused classes.
# Hybrid Consensus Algorithm Based on Modified Proof-of-Probability and DPoS
## 6 Discussion  
This paper proposes a hybrid consensus algorithm based on modified PoP and DPoS. By adopting two different consensus algorithms for block generation and verification, the system combines the advantages of the two consensus algorithms. The superiority of high computing power nodes is weakened by the fake hash and the waiting time mechanism introduced in PoP, and excessive computing power competition is avoided. DPoS greatly improves the efficiency of block generation and verification, but it also sacrifices decentralization to some extent. Each node can choose the appropriate work according to its own situation, and obtain more stable income. Most importantly, this method increases the cost of a 51% attack. An attacker needs to have both 51% of the computing power and 51% of the tokens to effectively attack the system. It effectively prevents various double-spend attacks. The optimization of the waiting time mechanism and the bit adjustment algorithm mentioned in this method will be the main research directions in the future. Due to the introduction of a sublinear function, the voting weight of some nodes holding a small number of tokens is more than that of a single node holding a large number of tokens, which in another aspect, leads to the relatively weak resistance  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & **PoP-DPoS** & \multirow{2}{*}{**PoW**} & \multirow{2}{*}{**PoS**} & **DPoS** & **PoP** \\  & **(Proposed)** & & & & & \\ \hline Bookkeeping & ✓ ✓ ✓ & ✓ & ✓ & ✓ ✓ & ✓ ✓ \\ right & & & & & \\ Resource & ✓ ✓ & ✕ & ✓ & ✓ ✓ ✓ & ✓ \\ saving & & & & & \\ High & ✓ ✓ & ✕ & ✓ ✓ & ✓ ✓ ✓ & ✓ \\ efficiency & & & & & \\ Decentralization & ✓ ✓ ✓ & ✓ ✓ & ✓ ✓ & ✓ ✓ & ✓ ✓ ✓ \\ Scalability & ✓ ✓ ✓ & ✓ & ✓ ✓ & ✓ ✓ ✓ & ✓ ✓ \\ Punishment & & & & & \\ mechanism & ✓ ✓ & ✕ & ✓ ✓ ✓ & ✕ \\ Resistance of & & ✓ ✓ & ✓ & ✓ ✓ ✓ & ✓ \\ temporary fork & & & & & \\ Resistance of & & & & & \\ various attacks & ✓ ✓ & ✓ & ✓ ✓ & ✓ ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between our proposal with other consensus algorithms.  
of the consensus mechanism to attacks similar to Sybil attacks [22]. Related improvement measures will also be the main work in the future. In addition, the further management of the transaction by the PoP node can resist a dusting attack. We will also compare the performance of this method with other consensus mechanisms in extreme cases.  
Writing--original draft, B.W.; writing--review and editing, Z.L.; resources, H.L. All authors have read and agreed to the published version of the manuscript.  
This research (Project NO. 110052971921/005/021) was funded by \"The Fundamental Research Funds for Beijing Universities\".  
The authors declare no conflict of interest.
## 6 Analysis and Discussion
#### 6.1.3 Influence of Lining Thickness  
The influence of the secondary lining thickness on the bearing capacity was also examined. Using a secondary lining with a reinforcement ratio of 0.6% for Grade V surrounding rock, the \(P_{0.3}\) and \(P_{\rm max}\) values were calculated under different thicknesses of 0.8 m, 1.0 m, 1.2 m, and 1.35 m. The results are shown in Figure 15.  
As the thickness of the secondary lining increased from 0.8 m to 1.35 m, both \(P_{0.3}\) and \(P_{\rm max}\) increased nearly linearly. Specifically, \(P_{0.3}\) increased by 12.57%, 28.65%, and 57.50%, respectively, with an average increase of about 11.18% for each 0.1 m increase in thickness. \(P_{\rm max}\) increased by 9.80%, 24.57%, and 39.35%, respectively, with an average increase of about 7.31% for each 0.1 m increase in thickness. These results indicate that increasing the secondary lining thickness significantly enhances both \(P_{0.3}\) and \(P_{\rm max}\), contributing to the overall structural integrity and safety of the tunnel lining. The analysis confirms that thicker linings provide better support to the surrounding rock and can better withstand high-ground stresses, thereby improving the long-term safety and durability of deep-buried tunnels.
# Chitosan Nanocomposite Coatings for Food, Paints, and Water Treatment Applications
## 2 Chitosan and its Properties
### Physico-Chemical Properties of Chitosan
#### 2.2.2 Molecular Weight (MW)  
The molecular weight (MW) of chitosan also influences the physicochemical and antimicrobial properties. Chitosan are categorised in three different forms: high molecular weight (HM-CH) chitosan, low molecular weight (LM-CH) chitosan, and oligochitosan (O-CH, short-chained chitosan) [40]. [PERSON] et al. reported the effect of molecular weights on the coating ability of chitosan and the postharvest quality of mango fruit by using three different molecular weight chitosan, namely, high molecular weight chitosan (HM-CH: 360 kDa), medium molecular weight chitosan (MM-CH: 270 kDa), and low molecular weight chitosan (LM-CH: 40 kDa) [41]. HM-CH coatings were found to be effective in delaying the ripening of mango fruit by retaining the titratable acidity, fruit firmness, and slowing down the rate of weight loss, ethylene production, and respiration. Additionally, HM-CH-coated fruits exhibited no incidences of spoilage throughout the reported storage period of 16 days. More recently, [PERSON] et al. evaluated the effect of MW on film-forming ability, electrostatic spraying atomization performances, and other film characteristics. Chitosan films were prepared with different molecular weights of chitosan (MW 6.55 kDa, 12.93 kDa and 47.70 kDa) using the electrostatic spraying (ES) technique [42]. The results indicated that with an increase in the MW ofchitosan, some film-forming solution properties, such as conductivity, viscosity, surface tension, and contact angle, were raised due to the increase in the proportion of amine-groups and degrees of CH chain entanglements. Moreover, with the increase in MW, the water barrier property and tensile strength of CH films were also found to improve. However, the antibacterial capacities of chitosan films against _Escherichia coli_ and _Listeria innocua_ were inferior for higher MW chitosan-based coatings [42]. High molecular weight chitosan cannot pass through the bacterial membrane and hence, they stack on the cell surface, which may alter the membrane permeability that effects the transport of nutrients into the microbial cell membrane, resulting in cell lysis, whereas chitosan with a lower molecular weight can proactively penetrate into the nuclei of a microorganism and could bind with DNA, inhibiting synthesis of mRNA and resulting in cell death [43; 44]. [PERSON] et al. however, reported that the antimicrobial activity of chitosan was greater with lower molecular weight for Gram-negative bacteria, but not for Gram-positive bacteria, which is still an area of contention amongst researchers [45].
## 4 Discussion
### System Performance  
The experimental results indicate that the assistive system performs well in domestic rooms with the predefined elements for the task (a room with a bedridden user, table, and shelves). The well-established navigation system and the implemented autonomous detection and manipulation routines make it possible to complete the tasks in all tests. However, user testing revealed several challenges. The user reported that while the system was generally compliant with the assistive task, it was slow. There are two main reasons for this slowness: detection and safety.  
The former involves detecting the containers and determining their positions in space. The user only gives a hint of where it is, for example on the table, and locating the robot and adapting it to the manipulation of containers takes time. This is reflected in the fact that the routines that include this process are the slowest.  
The latter is safety. To avoid collisions with the environment, the robot continues to plan and follow manipulation trajectories that are sometimes not the most optimal. It also executes them at a slow speed, so in the event of a collision, the safety system of the robot can stop the movement before it causes damage to the user or the environment. This is most evident in the shelf-picking task, where the trajectory it follows to lower the arm includes passing through pre-defined configurations that ensure the arm is confined to a safe space and does not tip the contents.  
\begin{table}
\begin{tabular}{l l} \hline Improvements & Faster execution, UI with larger typography, UI with pictures \\ \hline \multirow{2}{*}{Future needs} & Voice control, picking up not only containers, opening a closet, picking up from the floor, tasks not only in the main room \\ \hline \end{tabular}
\end{table}
Table 4: Improvements and future needs noted by users.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **User A** & **User B** & **User C** & **User D** & **User E** & **User F** & **Mean** \\ \hline Potential (1–5) & 4.5 & 5 & 4 & 4 & 5 & 5 & 4.6 \\ \hline Satisfaction (1–5) & 4 & 5 & 4 & 4 & 4 & 4 & 4.1 \\ \hline Usefulness (1–5) & 4 & 5 & 4 & 3.5 & 4.5 & 5 & 4.3 \\ \hline UI attractiveness (1–5) & 5 & 5 & 4 & 3 & 4 & 5 & 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The responses of users to surveys about the experiment.  
Moreover, physical safety needs to be considered, as well as the perception of safety by the users. Perceived safety improves when the robot's motion does not occur at a high speed and it is predictable [31]. This was also considered when setting the speed of the robot, accompanied by the continuous explanation with the TTS of the actions, balancing the need for efficiency with the comfort of users.
# Hybrid Energy Routing Approach for Energy Internet
## 1 Introduction
This work introduced a decision-making process that allows houses to determine when they should buy or sell energy, but it did not investigate the efficient path and scheduling problems. However, the authors in [31] suggested the use of bio-inspired methods to solve the energy routing problem. They considered the energy routing problem as an optimization problem and proposed the use of Ant Colony Optimization (ACO) to solve it. The proposed protocol calculates the minimum loss path between producers and consumers using the distance with the average cost by mile. A distributed Bee Colony Optimization (BCO)-based energy routing protocol is proposed in [32] to determine the best producer for each consumer considering the power loss, distance and available power among producers. The authors in [33] proposed an optimized energy routing method using Particle Swarm Optimization (PSO). It determined the energy-efficient path between producers and consumers in the SG by considering the links' quality, transmission cost and latency in the selection of the best optimal neighbors. There are other considerations than distance to be included in the estimation of the power loss, which will be addressed in detail in the next section. Based on the previous analysis, we considered in this paper the energy routing problem with its surrounding issues (subscriber matching, efficient path and transmission scheduling) as an optimization problem. To solve this problem, we proposed an improvement of the existing energy routing method in [31]. The contributions of this paper are:  
* A peer-to-peer energy trading architecture based on energy routers;
* A subscriber matching mechanism, and an Energy Particle Swarm Optimization Algorithm (_EPSOA_). The proposed subscriber matching mechanism determines the consumer-producer pairs for both mono and multi-source consumer cases (normal and heavy loads). The _EPSOA_ is used in a heavy load case to determine the amount power to buy from a set of producers to insure minimum power transmission loss and cost;
* An Improved energy routing protocol based on ant colony optimization which calculates a non-congestion minimum loss path between each producer/consumer pair in the EI. The remaining sections of the paper are organized as follows. Section 2 describes the proposed EI based on energy routers. It is modeled as a weighted graph. Section 3 introduces the subscriber matching mechanism, the energy particle swarm optimization method with the centralized peer-to-peer energy trading network. The Improved Ant Colony Optimization (IACO)-based energy routing protocol is detailed in Section 4. The performance of the proposed algorithms is evaluated by various analysis cases. It is also compared to the algorithm described in Section 5 in terms of power losses, costs and computation time. Finally, Section 7 concludes the paper.
## 3 Proposed Dynamic Fuzzy Logic for Broadcasting
### Reinforcement Learning Integration (Same as Previous Work)
#### 3.5.5 Incorporation into Multi-Profile Approach  
Although we now introduce multiple parameter set profiles for the fuzzy logic system (e.g., to handle radically different topologies or additional metrics like energy constraints), the essence of the RL module remains unchanged. The agent still processes fuzzy-derived states and selects BC or DNB actions under each chosen profile. Hence, no matter which profile is active, the RL loop continues to optimize the BC/DNB ratio, thereby maintaining the adaptive broadcasting benefits demonstrated in our previous dynamic fuzzy logic scheme.
## 3 Main Design
### Overview  
Our key idea comes from the observation that data and beacon capture totally different interference and noise patterns, and the statistical approach lacks short-term estimation.  
At a high level, the proposed link correlation estimation has two main parts, as shown in Figure 2: long-term estimation and short-term estimation. Long-term estimation comes from the statistics of historical packet receptions and losses. Short-term estimation comes from the modeled relationship between SINR values and expected link correlation (Section 3.2). Then, we integrate the long-term and short-term link correlation with a weighting factor \(\alpha\). When used for large batches of packet transmissions, we use a large \(\alpha\) value to represent long-term link correlation; when used for a single packet or a small number of packet transmissions, we use a small \(\alpha\) value to represent short-term link correlation. We first give the notations used in this section in Table 1.
# Performance Analysis of UAV-IRS Relay Multi-Hop FSO/THz Link
## 3 Performance Analysis
### Average Channel Capacity
\tag{57}\]  
According to the findings presented in reference [38], the channel capacities for hard switching and soft switching are determined as follows:  
\[C_{1}=C_{FSO}(\gamma_{th}^{FSO})+P^{FSO}(\gamma_{th}^{THz})\cdot C_{THz}(\gamma _{th}^{FSO}), \tag{58}\]  
\[w_{CTHz,j}=\frac{\Gamma(n)t_{i}}{n!(n+1)^{2}[L_{n+1}^{(-1)}(t_{i})]^{2}}, \tag{59}\]  
\[C_{2}=C_{FSO}\left(\gamma_{th,u}^{FSO}\right)+P^{P1}_{out}(\gamma_{th,J}^{FSO},\gamma_{th,u}^{FSO})\times C_{THz}(\gamma_{th}^{THz})+[C_{FSO}(\gamma_{th,J}^ {FSO})-C_{FSO}(\gamma_{th,u}^{FSO})]\times\frac{P^{P1}_{Hi}}{P^{P1}_{Hi}+P^{P1 }_{Low}}, \tag{60}\]  
\[P^{P1}_{out}(\gamma_{th,J}^{FSO},\gamma_{th,u}^{FSO})=P^{P1}_{Low}+P^{P1}_{Med }\times\frac{P^{P1}_{Low}}{P^{P1}_{Hi}+P^{P1}_{Low}}. \tag{61}\]
# A Novel Device-Free Counting Method Based on Channel Status Information
###### Abstract  
Crowd counting is of significant importance for numerous applications, e.g., urban security, intelligent surveillance and crowd management. Existing crowd counting methods typically require specialized hardware deployment and strict operating conditions, thereby hindering their widespread application. To acquire a more effective crowd counting approach, a device-free counting method based on Channel Status Information (CSI) is proposed. The wavelet domain denoising is introduced to mitigate environment noise. Furthermore, the amplitude or phase covariance matrix is extracted as the eigenmatrix. Moreover, both the spatial diversity and frequency diversity are leveraged to improve detection robustness. At the same experimental environment, the accuracy of the proposed CSI-based method is compared with a renowned crowd counting one, i.e., Electronic Frog Eye: Counting Crowd Using WiFi (FCC). The experimental results reveal an accuracy improvement of 30% over FCC.  
wavelet domain denoising; covariance matrix; spatial diversity; frequency diversity; robustness 2018 18, 3981; doi:10.3390/s18113981
## 3 Results
### Multiplicative Fault Detection
#### 3.1.2 Multiplicative Fault Detection Using Sliding Window Algorithm  
The following diagrams present selected results from the application of the SWA for estimating amplifier parameters using various window lengths (\(K\)). The result of applying the SWA is the set of discrete amplifier parameters, \(a_{1d}\), \(b_{0d}\), and \(b_{1d}\), described in model (8). Figures 17 and 18 show SWA-based parameter estimation in response to a multiplicative fault in the amplifier resistance \(R_{1}\) and the motor's viscous friction coefficient \(B\), respectively.  
Figure 16: Amplifier output voltage \(U_{\text{m}}\)(t) during multiplicative faults on: (**a**) \(Ra\); (**b**) \(Ra\); (**c**) \(B\); (**d**) \(J\). The symbol “**” denotes a multiplication sign.  
Figure 17: SWA amplifier parameters estimation: (**a**) \(b_{0d}\) (**b**) \(a_{1d}\), \(b_{1d}\) during the change of \(R_{1}\) from \(1\,\Omega\) to \(10\,\Omega\).  
Figure 15: Amplifier output voltage \(U_{\text{m}}\)(t) during multiplicative faults on: (**a**) \(R_{1}\); (**b**) \(R_{9}\); (**c**) \(C_{8}\); (**d**) \(R_{12}\).  
In Figures 17 and 18, the time interval from 0 to 10 s represents the estimated parameters before the fault occurs, while the interval from 10 to 20 s corresponds to the post-fault parameter estimation. A transient interval is noticeable within this post-fault period, lasting approximately \((K-1)\cdot T\) s, where \(T\) = 0.01 s denotes the sampling period.  
The graphical estimation results indicated that a sudden change in the amplifier or motor parameters triggers a transient response in the identification algorithm during the measurement period. Consequently, selecting a shorter window length is more advantageous in terms of reducing fault detection delay. However, it is also evident that as the window length decreases, the variance of the estimates increases and the sensitivity to noise becomes more pronounced. In the conducted experiments, \(K\) = 50; \(K\) = 100; and \(K