esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 2 Background  
### Fundamentals of Biomechanics  
It is necessary to understand how the mechanical structures of the human body act to reproduce the movements generated by the user of an exoskeleton. These concepts are approached by biomechanics, a branch of biophysics that studies the movement in living organisms.  
The movements are divided into three planes: sagittal, frontal, and transverse [14; 26]. In the sagittal plane, flexion, extension, and hyperextension movements occur. The extension is the movement that returns to the anatomical position, hyperextension is the movement that goes beyond the anatomical position, and flexion is the movement of rotation in the sagittal plane (movement performed by the head, trunk, arm, forearm, hand, and hip). The frontal plane comprises the abduction and adduction movements. Abduction is the movement of a body segment outside the midline of the body, while adduction moves towards the midline. The transverse plane involves rotation movements around the longitudinal axis. Lateral and medial rotations are two types of transverse plane movement. The lateral rotation occurs when the limb moves away from the midline of the body, whereas medial rotation occurs in the body's midline.  
Muscles play a role in the movement, and they are the only tissue capable of actively producing tension [26]. The muscles are responsible for the movement of the body's appendages, but they can only perform these tasks when stimulated by the nervous system; the muscles and the nervous system form the neuromuscular system [12; 26; 27].  
When a muscle contracts and generates movement, it becomes an agonist [14; 26], and several muscles collaborate to produce movement (the main agonist and assistant agonists). A muscle that acts in opposition to the agonist, generating an eccentric contraction, is called an antagonist or opponent. Agonists and antagonists usually position themselves on opposite sides of a joint. Agonists are responsible for accelerating movement, while antagonists perform deceleration, especially in rapid movements. There are two other functions that muscles can assume: stabilization and neutralization. The stabilizing muscles stabilize a part of the body against an internal or external force. Neutralizers are responsible for preventing the function of a muscle when its contraction performs more than one function, for instance a muscle that causes flexion and abduction, and only one of the movements must occur [26].  
Figure 1 illustrates the elbow flexion and extension movements. Two main muscles are related to these movements: the biceps and the triceps. The biceps act as the agonist of the flexion movement and antagonist of the extension. The triceps, in turn, play the inverse role of the biceps in each of these movements.
## 3 Results
### Identification Results Using Different Dictionary Sizes  
In order to verify the influence of the dictionary size on the classification results, this study compared the results of different dictionary sizes, as shown in Table 5, as an average of 100 trials. Figure 7 is a line chart of the average OA of different dictionary sizes. It can be seen from Table 5 and Figure 7 that the average OA of fruit cracking disease is higher than that of rust spot; the effect of the dictionary size on fruit cracking is also more obvious than that on rust spot. When the dictionary size for fruit cracking disease is 1350, the average OA and kappa coefficient are both the highest, at 92.06% and 0.92%, respectively. When the dictionary size for rust spot disease is 450, the average OA is the highest at 88.98%. With an increase in the size of the dictionary, the average OA and kappa coefficient for rust spot disease change smoothly.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Parameter Indexes**} & **Disease Categories** & **Average OA (\%)** & **Average Kappa Coefficient** \\ \hline \multirow{6}{*}{Feature vector dimension} & 9 feature vectors & 90.81 & 0.90 \\  & 6 feature vectors & Fruit cracking & 90.92 & 0.91 \\  & 3 feature vectors & 53.53 & 0.32 \\ \cline{2-4}  & 9 feature vectors & 84.98 & 0.78 \\  & 6 feature vectors & Rust spot & 86.86 & 0.82 \\  & 3 feature vectors & 79.83 & 0.72 \\ \hline \multirow{6}{*}{Feature points Number} & 1500 (500 points per type) & 90.81 & 0.90 \\  & 2400 (800 points per type) & Fruit cracking & 90.61 & 0.87 \\  & 3000 (1000 points per type) & 90.32 & 0.85 \\ \cline{1-1}  & 1500 (500 points per type) & 84.98 & 0.78 \\ \cline{1-1}  & 2400 (800 points per type) & Rust spot & 83.78 & 0.75 \\ \cline{1-1}  & 3000 (1000 points per type) & 84.95 & 0.78 \\ \hline \multirow{6}{*}{Neighborhood block size} & 3 \(\times\) 3 & 60.20 & 0.45 \\  & 5 \(\times\) 5 & Fruit cracking & 90.61 & 0.87 \\ \cline{1-1}  & 7 \(\times\) 7 & 90.81 & 0.90 \\ \cline{1-1} \cline{2-4}  & 3 \(\times\) 3 & 78.58 & 0.70 \\ \cline{1-1}  & 5 \(\times\) 5 & Rust spot & 83.60 & 0.77 \\ \cline{1-1}  & 7 \(\times\) 7 & 84.98 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical results of different parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Disease Categories**} & \multirow{2}{*}{**Evaluation Indexes**} & \multicolumn{6}{c}{**Dictionary Size**} \\ \cline{3-10}  & & **300** & **450** & **600** & **750** & **900** & **1050** & **1200** & **1350** & **1500** \\ \hline \multirow{3}{*}{Fruit cracking Rust spot} & Average OA (\%) & 90.92 & 89.64 & 89.11 & 88.34 & 89.31 & 91.67 & 92.00 & 92.06 & 91.65 \\  & Average kappa coefficient & 0.91 & 0.89 & 0.88 & 0.82 & 0.89 & 0.87 & 0.92 & 0.92 & 0.87 \\ \cline{1-1}  & Average OA (\%) & 86.86 & 88.98 & 87.16 & 87.52 & 87.40 & 87.52 & 87.35 & 87.62 & 86.20 \\ \cline{1-1}  & Average kappa coefficient & 0.82 & 0.87 & 0.83 & 0.83 & 0.83 & 0.83 & 0.83 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification results of different dictionary sizes.
## 2 Materials and Methods
### Analysis of Principal Components and Discriminant Analysis by Orthogonal Partial Least Squares  
Metabolite data were compressed into \(N\) principal components (PCs) to describe the features of the dataset, where the first principal component (PC1) represented the highest degree of variation, followed by the second principal component (PC2), and so on. Principal components analysis (PCA) was performed using the 'premop' function in the R program, where parameter scale = True' demonstrated the unit variance (UV) scaling for normalizing the data. Biplots were constructed using the first two PCs (PC1 and PC2). Orthogonal partial least squares discriminant analysis (OPLS-DA) for the metabolome data was further implemented to show the differences between each group. During the modeling of OPLS-DA, \(X\) matrix information is divided into information that is either related or unrelated (i.e., noise) to \(Y\), where the variables associated with \(Y\) are the PC, and the information unrelated to \(Y\) is the orthogonal PC [25]. The 'OPLSR.Anal' function in the package 'MetaboAnalystR' in R was used for OPLS-DA. S plots for the OPLS-DA were constructed by plotting the correlation coefficient values between the PCs and the metabolites against the covariance between the PCs and metabolites. The OPLS-DA model validation was performed using permutation tests, represented as \(R^{2}X\), \(R^{2}Y\), and \(Q^{2}\), where \(R^{2}X\) and \(R^{2}Y\) denote the explanatory rate of the model to the \(X\) and \(Y\) matrices, respectively; and \(Q^{2}\) signifies the prediction ability of the model. Values closer to 1 indicate a more stable and reliable model for validation.
## 3 Results
### Membership Function Construction  
#### 3.2.1 PERFORM Input Parameters  
There are various ways to generate MFs to best suit the intentions of the autonomy test engineer. For this application, each testbed sensor used for PERFORM includes fuzzy MFs to account for specific sensor characteristics and uncertainties. For example, total distance traveled is determined using GPS. The uncertainty in the GPS measurement, found experimentally or those provided by product specifications/documentation, is integrated into the MF FOU. Sensor measurement quality can also be affected by sources such as high noise levels and changing environmental conditions (e.g., humidity, rain, etc.) [35; 38]. If desired, these methods may also benefit from added knowledge from a priori simulations giving further context to suitable measurement ranges/bandwidths and corresponding MF intervals for a given testbed.  
It is assumed that the sensors and vehicle are identical to enable the direct comparison of the path planning algorithms. The positioning system used for this testbed is a Marvelmind HW v4.9-NIA indoor positioning system. By taking into account the measurement noise produced from this signal, improvements are expected for the IT2-FL techniques [20]. Marvelmind company documentation gives a measurement uncertainty value of \(\pm\)0.02 m [39]. From the obstacle configuration given in Figure 8, the shortest path to the goal location (including the obstacle) based on Euclidean distance is used as the minimum of the input range for total distance. The maximum distance used in the MF is arbitrarily defined to be twice the minimum distance and can be adjusted depending on the user's acceptable tolerance. Any test run value greater than the maximum will automatically be input at this saturated maximum value.  
The \"total distance\" in this work is calculated by determining the overall sum of the changes in position such that  
\[\sum_{k=1}^{n}d_{k}=\sqrt{(x  
and CPA are given in Figure 11. The same input MF's are used for Case Study I and II (with II using only the 5-MF system).  
For Case Study III, the MFs are modified to represent the new parameters (path percent error and average speed) under this test environment and is shown in Figure 12. Both parameters use data from the testbed GPS unit, so the uncertainty bounds are designed to take this into account. Path percent error and average speed are 0.50% and 0.25 m/s, respectively, and are deemed appropriate for the level of accuracy warranted in this case study. Variable ranges for Case III are summarized in Table 6.
## 3 Results
### Perceived Learning Effectiveness  
Based on the results from the learning experience questionnaire, it appears that learners who completed the VRER in pairs had slightly better perceptions regarding their learning effectiveness compared to those who played individually (Figure 6). Q1 was a reverse question, reading, \"The VRER did not help me learn a lot\". Participants of both groups seemed to agree on the fact that this was not the case by rating the statement with an average of 1.2. In general, all questions yielded positive results for both groups, indicating that both pairs and individual learners consider the VRER an effective learning method. However, running a Mann-Whitney U-test on the overall construct of perceived learning effectiveness by calculating the mean for each question revealed no statistically significant difference (\(p=0.132>0.05\)) between pairs and individuals. It should be noted that, for all categories, reverse questions were taken into consideration, and their score items were reversed before running the tests to ensure that the results reflect the true differences between groups.
## 1 Introduction
In the case of the use of aluminum chlorohydrate alone, 37% and 54% of chemical oxygen demand and turbidity were removed, respectively [9]. In another study, chitosan was used as a biopolymer coagulant, obtaining, with 2 g L\({}^{-1}\) of coagulant dose, pH equal to 8, and 43 min of contact time, the removal of 91% turbidity, 89% total organic carbon (TOC), and 65% of orthophosphate [7]. However, most of the investigations were carried out under controlled laboratory conditions and tested small volumes of (mainly synthetic) BWWs. The aim of this study was to overcome this current gap in the literature by evaluating the effectiveness of the coagulation process on real BWW on a larger scale in order to better estimate the actual effectiveness of the process and the production of chemical sludge. The work was divided into two distinct phases: (i) a lab-scale phase for the identification of the optimal conditions for coagulation and flocculation, and (ii) a subsequent industrial-scale phase where the effectiveness of the previously identified optimal conditions (type of reagent and concentrations) was evaluated on a larger scale for almost one month. Moreover, this stage also allowed us to estimate the production of chemical sludge that has to be managed after the flocculation process. The findings can be valuable for the scientific community, offering insights into the impact of chemical coagulation on industrial-scale wastewater treatment and providing technical operators with crucial data on the efficacy of various reagents under different conditions.
# Experimental Study on the Strength and Durability for Slag Cement Mortar with Bentonite
## 4 Results and Discussion
### Weight Loss after the Sulfate Resistance Test  
The losses of weight for the specimens with various replacement ratios of bentonite subjected to sulfate resistance testing are shown in Figure 3a-c for specimens with various water/binder ratios. As shown in Figure 3, corresponding to the increased number of immersive cycles of specimen in the solution of sodium sulfate, the weight loss of most specimens increased. In the first cycle, the weight slightly increased due to the attachment of the sodium sulfate reaction products on the specimen. As can be seen in Figure 3a, where the w/b ratio is 0.445, partial replacement of slag cement with bentonite seems to reduce the loss of weight of a specimen after being subjected to the sulfate resistance test. The higher is the replacement ratio, the smaller is the weight loss. However, for the group with 0.25% replacement of bentonite, the weight loss phenomenon was similar to that of the sample without replacement with bentonite. When we examine Figure 3b,c, we can see that when the w/b ratios are higher, the reduction of weight loss due to partial replacement of slag cement by bentonite is not significant, especially when the w/b ratio is 0.55.  
Figure 3: Ratio of weight loss after the sulfate resistance test: (**a**) w/b = 0.445, (**b**) w/b = 0.485, and (**c**) w/b = 0.550.
## 2 Dominating Set
### Algorithm for Unit Line Graphs  
We present a local algorithm for dominating set on unit line graphs (ULGs) with locality one. It achieves an approximation ratio of 3. The idea is to divide the line into units of width one. Then we assign one vertex from each unit to the dominating set.  
Now we present the algorithm in detail. Let \(G=(V,E)\) be a unit line graph. For a vertex \(v\) we denote by \(v_{x}\) its \(x\)-coordinate. For all integers \(i\) we define \(V_{i}:=\{v\in V|i\leq v_{x}<i+1\}\). We define \(H:=\{i|V_{i}\
eq\emptyset\}\). For all \(i\in H\) we define \(v[i]\) to be the vertex with the smallest \(x\)-coordinate in \(V_{i}\). We define \(D:=\{v[i]|i\in H\}\). We output \(D\). We refer to this as Algorithm 2.  
```
1// Algorithm is executed independently by each node \(v\);
2// let \(i\) be the integer such that \(v\in V_{i}\);
3// let \(v[i]\) be vertex with the smallest \(x\)-coordinate in \(V_{i}\);
4 Find all vertices in \(N(v)\) and determine \(V_{i}\);
5 if\(v=v[i]\)then become part of the dominating set \(D\)else Do not become part of \(D\)
```  
**Algorithm 2**Local algorithm for finding a dominating set in a unit line graph  
We prove the correctness of Algorithm 2 in Theorem 3.  
**Theorem 3**.: _Let \(G\) be a unit line graph. Algorithm 2 has the following properties:_  
1. _The computed set_ \(D\) _is a dominating set for_ \(G\)_._
2. _Let_ \(D_{OPT}\) _be an optimal dominating set. It holds that_ \(|D|\leq 3\cdot|D_{OPT}|\)_._
3. _Whether or not a vertex_ \(v\) _is in_ \(D\) _depends only on the vertices at most one hop away from_ \(v\)_, i.e. Algorithm_ 2 _is local._
4. _The processing time for a vertex_ \(v\) _is linear in the number of vertices adjacent to_ \(v\)_._  
Proof.: We first prove that \(D\) is indeed a dominating set for \(G\). Let \(i\in H\). As \(V_{i}\
eq\emptyset\) it holds that \(v[i]\in V_{i}\cap D\). So \(v[i]\) dominates all vertices in \(V_{i}\).  
Now we prove that for an optimal dominating set \(D_{OPT}\) it holds that \(|D|\leq 3\cdot|D_{OPT}|\). Consider a vertex \(v\in D_{OPT}\) in a set \(V_{i}\). Since all sets \(V_{j}\) have width one it holds that \(v\) is adjacent to at most 3 vertices in \(D\) (at most one each in \(V_{i-1}\), \(V_{i}\), and \(V_{i+1}\)). Since \(D_{OPT}\) is a dominating set all vertices in \(D\) must be adjacent to at least one vertex in \(D_{OPT}\). This leads to \(|D|\leq 3\cdot|D_{OPT}|\).  
Next we determine the locality of Algorithm 2. In the algorithm, a vertex \(v\) in a set \(V_{i}\) only needs to explore all other vertices in \(V_{i}\). (This is sufficient to determine whether \(v\) is the vertex with the smallest \(x\)-coordinate among the vertices in \(V_{i}\).) So whether or not \(v\in D\) depends only on the vertices which are at most one hop away from \(v\).  
Now we prove the processing time of Algorithm 2. Let \(v\) be a vertex and define \(n_{1}(v):=|N^{1}(v)|\). In the algorithm \(v\) needs to determine for each vertex \(v^{\prime}\in N^{1}(v)\) whether \(v^{\prime}\in V_{i}\) and whether \(v^{\prime}\) has a smaller \(x\)-coordinate than \(v\). This can be done in \(O\left(n_{1}(v)\right)\) time.
## 4 Evaluation  
After the construction of the proposed framework, we conducted a series of experiments to verify its operations and evaluate its performance. First, we verified the performance of the positioning system that makes up the framework and, then, verified how data tampering by malicious attacks affects the positioning performance. Then, in the distributed ledger technology applying a new consensus algorithm, we verified the impact of the confirmation threshold on transaction processing performance and how system performance varied according to changes in the network scale. Lastly, we evaluated the security performance of the proposed framework by modeling a race attack scenario, one of the common blockchain attack methods.
## 4 Results and Discussions
### Simulation Results
#### 4.2.3 Heat Power  
A mathematical model was used to calculate the heat transfer efficiency per unit area of the model pile at a specific time. In the model test, the circulating water flowed into the energy pile at a constant temperature and flowed into the water tank after exchanging heat with the soil. According to the circulating water flow, the pile's total heat transfer power could be calculated based on the fluid's inlet and outlet water temperature using the following equation:  
\[Q=\rho_{\mathrm{w}}vC_{\mathrm{w}}(T_{\mathrm{out}}-T_{\mathrm{in}}) \tag{10}\]  
where \(Q\) is the heat transfer power (W), \(v\) is the circulating water flow rate (m\({}^{3}\)/s), \(T_{\mathrm{out}}\) is the water outlet temperature (K), \(T_{\mathrm{in}}\) is the water inlet temperature (K), \(\rho_{\mathrm{w}}\) is the water density (kg/m\({}^{3}\)), and \(C_{\mathrm{w}}\) is the specific heat of the water [J/(kg\(\cdot\)K)].  
To analyze the influence of specific heat enhancement measures on heat transfer, an ordinary concrete pile (OC pile) and an FR pile were introduced to make a comparison. Referring to previous studies, the thermal conductivity of the OC pile was set to 1.6 W/(m\(\cdot\)K) and the specific heat capacity was set to 880 J/(kg\(\cdot\)K). Figure 9a-c compare the heat power of the FR and FRPC piles at different flow rates. At the beginning of the test, heat transfer was the highest. The total heat power showed a downward trend over time. However, it still did not reach stability at 140 h. Figure 9d,e show the pile's maximum and minimum heat power during operation. Under three different flow rates of 1500 mL/min, 3000 mL/min, and 4500 mL/min, the maximum heat power was 655.9 W,801.8 W, 1008.1 W for the FRPC pile, 698.3 W, 941.8 W, 1231.2 W for the FR pile, and 664.3 W, 850.5 W, and 1095.7 W for the OC pile, respectively. Similarly, the minimum heat power was 148.5 W, 160.2 W, 162.1 W for the FRPC pile, 160.8 W, 174.9 W, 177.1 W for the FR pile, and 156.5 W, 170.1 W, 171.8 W for the OC pile, respectively. By integrating heat power over time, the quantity of heat transfer of different piles in the whole heat transfer process could be obtained, as shown in Figure 9f. Under three different flow rates of 1500 mL/min, 3000 mL/min, and 4500 mL/min, the quantity of heat transfer was 93.8 MJ, 104.3 MJ, and 106.2 MJ for the FRPC pile, 102.8 MJ, 116.1 MJ, and 118.4 MJ for the FR pile, and 99.2 MJ, 111.5 MJ, and 113.7 MJ for the OC pile, respectively.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 2 Related Work
[26] proposed a blockchain-based lightweight authentication scheme for IoD environments. Their protocol employs Hyperelliptic Curve Cryptography (HECC) and is designed to provide secure and anonymous user authentication. Moreover, their protocol is resilient to security attacks, including physical drone capture, denial-of-service, impersonation, and stolen device attacks. [PERSON] et al. [27] proposed a drone-assisted authentication protocol for secure interaction with the Internet of Vehicles, focusing on accident rescue scenarios. They focused on enabling reliable and privacy-preserving access to drone-provided services for users in emergencies. [PERSON] et al. [28] proposed a novel authentication scheme for secure data transmission in IoT-integrated UAV environments. This scheme integrates HECC with a fuzzy extractor, establishing a robust authentication process. In 2024, [PERSON] [13] proposed a secure key agreement scheme for secure communication by allowing entities in the IoD network to establish shared cryptographic keys. [PERSON] highlighted this protocol as verifiably secure by demonstrating its security using the GNY model and ProVerif. However, these protocols are unsuitable for resource-constrained IoD environments because they rely on computationally heavy cryptographic techniques such as ECC. Several vulnerabilities exist in [PERSON]'s protocol [13], including the possibility of drone impersonation, mobile user impersonation, and session key disclosure attacks by eavesdropping on public channel messages. Therefore, this work proposes a robust and lightweight key agreement scheme for the IoD environment to overcome the limitations of [PERSON]'s protocol.