esa-sceva/satcom-chunk-collection huggingface dataset:
# Pedestrian Simulation with Reinforcement Learning: A Curriculum-Based Approach
## 3 Proposed RL Approach
### Agent Action Model  
The agent action in this model is essentially a change in its speed vector; this translates into two potential changes, for each decision, essentially related to the magnitude of the vector, i.e., _walking velocity_ and _direction_.  
Each agent is triggered by the overall execution engine to take a decision about if and how to change its own actual speed vector three times per second, in line with [23], in an attempt to consider cognitive plausibility, quality of the achieved results, and computational cost.  
The agent's action space was therefore modeled as the choice of two (conceptually) continuous values in the [\(-\)1,1] interval that were used to determine a change in velocity vector for magnitude and direction.  
The first element, \(a_{0}\), causes a change in the walking speed defined by Equation (2):  
\[\textit{speed}_{t}=Max\bigg{(}\textit{speed}_{min},\textit{Min}\bigg{(}\textit {speed}_{t-1}+\frac{\textit{speed}_{max}*a_{0}}{2},\textit{speed}_{max}\bigg{)} \bigg{)} \tag{2}\]  
where \(\textit{speed}_{min}\) was set to 0, and \(\textit{speed}_{max}\) was set to 1.7 m/s. According to this equation, the agent is able to reach a complete stop, or the maximum velocity is two actions (i.e., about 0.66 s). To account for the heterogeneity within the simulated pedestrian population,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Observation** & **Observation** & **Value** \\ \hline Intrinsic & Own speed & Number \\ \hline \multirow{2}{*}{Walls and Targets} & Distance & Number \\ \cline{2-3}  & Type/Tag & One Hot Encoding \\ \hline \multirow{4}{*}{Agents and Walls} & Distance & Number \\ \cline{2-3}  & Type/Tag & Boolean \\ \cline{2-3}  & Direction & Number \\ \cline{2-3}  & Speed & Number \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of agent observations.  
each agent is provided with an individual desired walking velocity that is drawn from a normal distribution with average of 1.5 m/s and a standard deviation of 0.2 m/s; so, for each agent, the actual \(speed_{max}\) does differ.  
The second element of the decision, \(a_{1}\), determines the change in the agent's direction according to Equation (3):  
\[\alpha_{t}=\alpha_{t-1}+a_{1}*20 \tag{3}\]  
The walking direction can therefore change 20\({}^{\circ}\) each 0.33 s, which is plausible for normal pedestrian walking, but would be probably not be reasonable for modeling running and/or sport related movements.  
For the perception model, the model associated with the agent's action presents limits: for instance, it is not suited to situations in which an agent can choose to jog or run or to perform sudden and significant changes in the walking direction (such as basketball players trying to dribble around opponents). Normal walking scenarios, not emergency evacuation situations, seem compatible with this setting.
## 2 Agro-Wastes as Concrete Additives
### Coconut Shell Waste  
The escalation of construction costs constitutes a crucial parameter influencing decisions in the construction industries of developed countries. Utilizing coconut shells as a natural source of aggregate in concrete could provide a practical solution for disposing agro-waste materials and consequently contributing to environment preservation [49]. The application of some agricultural waste like small-size coconut shells (20-600 mm) can be used as an aggregate material.  
Evaluating the properties of coconut fibers has a key role for understanding how to incorporate them into RC. Also, owing to the structural properties of the coconut fibers, they can be employed as a filling material within the matrix or used as reinforcement (Figure 5). Furthermore, it is found from experiments that this fiber could absorb internal stresses when mixed with mortars [50; 51].  
[PERSON] and [PERSON] conducted a study to determine the optimal ratio of coconut fibers for both conventional and coconut shell concretes [52]. The obtained results showed that the highest compressive strength of \(43.8\,\mathrm{N/mm^{2}}\) was attained for conventional concrete  
Figure 5: Conventional concrete mixed with fibers (**A**) and proposed coconut shell concrete with fibers (**B**): compressive strength with respect to aspect ratio and volume portion (%) [52].  
with coconut fibers at an aspect ratio of 83.3 and volume portion of 3%. This demonstrated a 45.5% increase compared to conventional concrete without coconut fibers, which measured 30.1 N/mm\({}^{2}\) (Figure 5A). In cases where coconut aggregate was used in the concrete, the results disclosed that the compressive strength of the suggested concrete using coconut shell with coconut fibers reached 30 N/mm\({}^{2}\) at an aspect ratio of two thirds and volume portion of 3%, which was a 17.2% increase compared to the coconut shell concrete without coconut fibers (25.6 N/mm\({}^{2}\)) (Figure 5B). In another study, coconut shell ash showed increasing compressive strength at 15% replacement, but with higher percentages, this property was decreased [53]. [PERSON] et al. recently published a study and their results showed that incorporating coconut shell aggregate (20%) in concrete reduced its compressive strength, but the addition of up to 50% of the additive met the standard values [54]. Similar findings were reported by [PERSON] et al. [55], [PERSON] et al. [56], and [PERSON] et al. [57].  
In summary, the incorporation of small-size coconut shells as a natural aggregate should be used in concrete. The inclusion of coconut shells as aggregate decreases the compressive strength of concrete but it still meets the required standards. In addition, coconut shell incorporation with its fiber provides a better performance than just using the shell individually. Overall, utilizing coconut shells as concrete aggregate is a sustainable solution for disposing of this agro-waste material. Lastly, coconut shell ash is a good supplementary material for cement.
# Study on Nitrogen-Doped Graphene Ink and Its Effects on the Heat Dissipation for the LED Lamps
## 2 Historical Review  
Looking back on the studies by researchers and scholars, the literature review was divided into two main areas: graphene and LEDs. The graphene literature mainly introduces the manufacturing process, properties, and heterogeneous doped graphene, etc.; and the LED literature has been mainly discussed in terms of temperature measurement, thermal resistance, and thermal management to control waste heat.
# Content Management Based on Content Popularity Ranking in Information-Centric Networks
## 4 Performance Analysis
### Cache Hit Ratio
Interestingly, when the maximum available content was relatively small initially, the LFU scheme performed better than the LRU scheme in terms of CHR. Servers utilizing the three different cache removal schemes, LRU, LFU, and CPR-based schemes, achieved approximately 87%, 88%, and 93% CHR, respectively, when the maximum number of available content pieces was 30. However, the LRU scheme started to perform better than the LFU scheme by successfully removing the appropriate content as the number of pieces of content increased. As a result, servers utilizing the LRU scheme achieved a higher CHR than the servers utilizing the LFU scheme. Nevertheless, servers utilizing the proposed CPR-based cache removal scheme outlined to outperform both of these schemes in terms of CHR. When the maximum number of available content increased to 90, the servers utilizing the cache removal schemes based on CPR, LRU, and LFU achieved approximately 66%, 52%, and 45% CHR, respectively. Therefore, the CPR-based cache removal scheme outperformed the LFU cache removal scheme and the LRU cache removal scheme in terms of the Cache Hit Ratio, and the LRU cache removal scheme performed better than the LFU cache removal scheme when the maximum number of available content was relatively high.
# Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image
## 4 Experiment and Results
### The Effect of _D-Fusion_  
To analyze the effectiveness of different fusion methods, we tested four types of fusion methods. They are early fusion, late fusion, deep fusion and the D-fusion we designed. From Table 2, it can be clearly seen that in the case where only the fusion method is different, D-fusion basically exhibits optimal performance in detection tasks of different types of objects or different difficulty levels. Compared with early fusion, late fusion and deep fusion of AP\({}_{\text{3D}}\), the network with D-fusion increased by 8.35%, 8.33% and 9.75%, respectively. In comparison with AP\({}_{\text{BEV}}\), the network with D-fusion increased by 8.94%, 5.94% and 8.35%, respectively.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Modelling Techniques
#### 3.3.3 Sequence and Temporal Modelling
Conditional Random Fields (CRFs): In [70], CRFs were used alongside LSTMs to analyse multimodal data streams comprising facial action units, physiological skin responses and game trace logs. Although LSTMs outperformed CRFs in terms of predictive accuracy, the study demonstrated the complementary role of CRFs in sequence labelling tasks and highlighted their potential for modelling structured interactions in game-based learning environments.
## 3 Classic Numerical Scheme Test Cases
### Numerical Simulation of Rayleigh-Taylor Instability (RT Problem)  
The Rayleigh-Taylor instability (RT problem) is a significant phenomenon in fluid dynamics that describes the instability and vortex formation when a shock wave interacts with an interface between two fluids of different densities. In this study, specific initial conditions were set within a computational domain of \([0,0.25]\times[0,1]\), and computations were performed using improved finite-volume WENO schemes. The initial conditions involve complex flow structures resulting from shock interactions between two fluids of different densities. The initial conditions are as follows:  
\[(\varrho,u,v,p)^{T}=\begin{cases}(2,0,-0.025c\cos(8\pi x),1+2y)^{T}&0<y<1/2,\\ (1,0,-0.025c\cos(8\pi x),y+3/2)^{T}&x<1,y<1,\end{cases} \tag{10}\]  
where \(c=\sqrt{\frac{7p}{p}}\) and \(\gamma=\frac{5}{3}\).  
The boundary conditions are set as follows:  
* Left and right boundaries: anti-symmetric boundary conditions.
* Bottom boundary: \((\rho,u,v,p)=(2,0,0,1)\).
* Top boundary: \((\rho,u,v,p)=(1,0,0,2.5)\).  
The computational domain grid size is 480 \(\times\) 480, and the computed density contour plot is shown in Figure 3, with equally spaced contour levels ranging from 0.56 to 1.67.  
Figure 2: The flow fields of double shock wave reflections computed using different improved finite-volume schemes: (**a**) 3 rd-order; (**b**) 4 th-order; (**c**) 5 th-order; (**d**) 6 th-order.  
Figure 3 compares the results obtained using the new WENO schemes of orders 4, 6, and 8 (referred to as WENO4, WENO6, and WENO8) with the results from the original WENO-ZQ (5 th-order) scheme. Notably, the new WENO schemes demonstrate richer flow details, exhibiting larger vortex structures and well-developed smaller vortices compared to the WENOZQ results.
## 3 Results and Discussion
### Digestate Main Characteristics
Urea is a compound highly soluble, which suggests that the liberation of ammonia into the reactor system could have been immediate. This assumption was also supported by the high CODs (7700 \(\pm\) 500 mg O\({}_{2}\) kg\({}^{-1}\), Table 3). Additionally, the theoretical methane yield of urea was null, so any methane produced should be linked only to the fed _P. oceanica_ (Table S2). On one hand, the findings of this study contrast with previously reported data in the literature, which support the idea of urea as a good nitrogen-rich source that enhances the methanization of high C/N ratio biomasses. For example, an earlier study reported that reducing the C/N ratio of cattle waste from 40 to 27 by using urea slightly increased biogas production (8-11%) [43]. More recently, the use of urea to reduce the C/N ratio of hulless barley straw from 38.92 to 23.68 enhanced the methane production by 69.7% when coupled with alkaline pretreatment (KOH 5.02%; 24.07 h) [44]. On the other hand, recent studies, similar to what was observed in this work, reported urea to be inhibitory at certain concentrations. For example, the plant hybrid Pennisetum was co-digested with different concentrations of urea, and the results showed that up to a TAN value of 4000 mg N-NH\({}_{4}\)\({}^{+}\) L\({}^{-1}\), the system was able to recover, although at 3000 mg N-NH\({}_{4}\)\({}^{+}\) L\({}^{-1}\), the methane yield only achieved 85% of the control, and at 4000 mg N-NH\({}_{4}\)\({}^{+}\) L\({}^{-1}\), the methane yield was less than 50% of the control. Finally, at TAN values above 4000 mg N-NH\({}_{4}\)\({}^{+}\) L\({}^{-1}\), the system failed to recover and was completely inhibited [45].
# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
## 4 Simulation Results and Discussion  
This section presents the application and analysis of the proposed TFWO algorithm for extracting the optimal values of the parameters of various PV models. Real data of a 55 mm diameter commercial R.T.C. France solar cell [7; 44] and experimental data of a KC200 GT module [46] are considered. The considered boundaries of the parameters are explained in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**R.T.C. France Solar Cell [7]**} & \multicolumn{2}{c}{**KC200 GT Module [7]**} \\ \hline
**Parameters** & **Lower Bound** & **Upper Bound** & **Lower Bound** & **Upper Bound** \\ \hline \(I_{ph}\) & 0 & 1 & 0 & 9 \\ \(I_{s1}\cdot I_{s2}\cdot I_{s3}\) (\(\mu\)A) & 0 & 1 & 0 & 1 \\ \(R_{s}\) & 0 & 0.5 & 0 & 0.5 \\ \(R_{sh}\) & 0 & 100 & 0 & 100 \\ \(a_{1}\cdot a_{2}\cdot a_{3}\) & 1 & 2 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The extracted parameters boundaries of test solar cells and modules.  
Figure 4: Flowchart of the turbulent flow of water-based optimization algorithm (TFWOA).
## 2 Methods
### Study Tools and Protocol
#### 2.2.2 Intervention  
Each group received one month of intervention, which commenced immediately following the surgery. The tele-rehabilitation group received 1-2 sessions per week of elbow motion at home via tele-rehabilitation and 1-2 conventional sessions per week in an outpatient clinic. The control group received 3-4 conventional sessions per week in the outpatient clinic. Each session lasted 30 min. Additionally, both groups were instructed to perform home exercises. The tele-rehabilitation group was instructed to use the MediTouch system, and the control group was instructed to repeat the exercises that they performed in the clinic. After one month, both groups continued to receive occupational therapy treatment in the clinic without tele-rehabilitation.  
The tele-rehabilitation group received the treatment with the MediTutor software, the ArmTutor (MediTouch, Netanya, Israel) and 3 DTutor biofeedback hardware (MediTouch, Netanya, Israel; Figure 2). The MediTouch system was successfully used in hand rehabilitation [33; 34]. The ArmTutor was used for practicing elbow flexion and extension, and the 3 DTutor was used for practicing supination and pronation. The software provides visual and auditory feedback while performing different tasks and games. Both devices are strapped to the limb using Velcro straps. The hardware includes wireless electro-optical sensors, which detect joint movement and display them on the screen. The system measures passive and active ROM and movement accuracy in relation to the presented virtual goal and speed. Each session included 15 min of flexion-extension exercises with the ArmTutor device and 15 min of supination-pronation exercises with the 3 DTutor device. At the beginning of every game, the system measures active ROM. Each session started with the existing active ROM for 5 min at a low speed, and then the settings of the chosen game were altered during the game by the therapist; thus, the patient had to either increase the ROM in order to reach the target of the game or stay at the same range and increase the speed. The therapist followed the patients' performances. The difficulty level was personally graded to each participant according to personal preferences, pain, and fatigue levels.  
The conventional intervention in the outpatient clinic included exercises of active ROM (flexion, extension, pronation, and supination) of the arm and the elbow. Common active exercises for flexion, extension, and pronation-supination were used. Resistance and range were personally increased for each patient using common intervention accessories, e.g., Theraband and ball. In addition, the conventional therapy sessions included usage of a hot pack, in order to release joint and soft tissue stiffness at the elbow area, and scar massaging.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions  
#### 3.3.1 Quark  
The Quark family refers to a collection of cryptographic hash functions that were intended for lightweight applications. The initial developers of Quark are [PERSON], [PERSON], [PERSON], and [PERSON] [54]. These functions were created to cater to the needs of resource-constrained devices, such as those found in the IoT and RFID (Radio Frequency Identification) protocols. Quark utilizes a Sponge construction with a capacity (\(c\)) equivalent to the digest length (\(n\)) and a core permutation derived from earlier cryptographic primitives. The design is focused on minimizing resource consumption, making it suitable for environments where computational resources are limited. This approach aligns with the demands of lightweight cryptography, ensuring a balance between efficiency and security in scenarios where devices have constraints on processing power and memory.  
Given an initial state of \(b\) bits, a Sponge construction processes the message m in three main phases. The first phase is initialization, where the message is padded by appending a single '1' bit and then the minimum number of '0' bits such that its length becomes a multiple of \(r\)--the rate size. This step ensures that the message is properly formatted for the absorption process. During the absorbing phase, the \(r\)-bit message blocks are XORed with the last \(r\) bits of the internal state, and then the permutation \(P\) is applied. It starts with an XOR of the first message block with the internal state, and then each subsequent message block alternates between an XOR and a permutation until the message is fully absorbed. The absorbing phase ends with an application of \(P\). The final phase, Squeezing, provides the output by extracting \(r\)-bit chunks from the last \(r\) bits of the internal state interleaved with applications of the permutation \(P\), continuing until \(n\) bits of output are reached. The Quark hash function is based on a permutation \(P\) derived from the Grain stream cipher and the KATAN block cipher. The internal state of \(P\) consists of three feedback shift registers: two non-linear feedback shift registers (NFSRs) with \(\frac{b}{2}\) bits each and a linear feedback shift register (LFSR) with \(\lceil\log 4b\rceil\) bits [55].
###### Abstract  
Water used in cleaning processes within the agri-food industry can be a vector for post-harvest contaminants, thus contributing to cross-contamination. The contamination risk is increased when water is not replaced between batches or when disinfection protocols are insufficient. Given the increasing focus in recent years on the potential of natural, non-invasive plant extracts to combat a variety of pathogens, including multidrug-resistant bacteria, environmental strains, and clinical isolates, this study aimed to evaluate the antibacterial activity of selected water-ethanol plant extracts against six opportunistic pathogens isolated from wash water in the agri-food industry, along with chromatographic analyses of the selected extracts. Plant extracts were obtained from the fruits, leaves, shoots, roots, and bark of 13 species. Antibacterial activity was assessed using the well diffusion method. The results indicated that antimicrobial activity was exhibited by six extracts: _Tilia cordata_ [PERSON], _Camelia sinensis_, _Quercus robur L., Betula pendula_ Roth, _Rubus ideaus_ L., and _Salix alba_ L. The extracts showed strain-dependent antimicrobial activity, with _C. sinensis_ and _R. ideaus_ up to 4.0 mm and 8.0 mm inhibition zones, respectively. _P. aeruginosa_ and _E. faecalis_ were the most susceptible strains, demonstrating the largest inhibition zones. In contrast, _P. vulgaris_ and _K. oxytoca_ were more resistant. The efficacy of the most active extracts can be linked to the presence of phytochemicals identified via GC-MS, including epicatechin, shikimic acid, quinic acid, gallic acid, and caffeine. These metabolites are known to interfere with bacterial cell structures and metabolic pathways. These studies may serve as a preliminary step toward the development of non-invasive water treatment methods for wash water.  
water-born pathogens; plant extracts; gas chromatography analysis; antimicrobial agents +
Footnote †: journal: Applied sciences
# BiModalClust: Fused Data and Neighborhood Variation for Advanced K-Means Big Data Clustering
## 5 Experimental Evaluation
### Synthetic Experiment Results  
In each iteration of BiModalClust, the sampling process introduces a level of sparsification to the dataset. This sparsification allows the initial centroids to move more freely between overlapping clusters, enhancing their flexibility within the current solution. By carefully selecting an appropriate sample size, the algorithm maintains a balance between enabling centroid flexibility and preserving a reasonable approximation of the original dataset. Striking this balance is critical for achieving effective clustering results.  
To further investigate this hypothesis, additional experiments were conducted using synthetic data.  
Consider a mixture of three isotropic Gaussian distributions \(\{\mathcal{N}_{1},\mathcal{N}_{2},\mathcal{N}_{3}\}\) with standard deviations \(0.15,0.08,0.1\), respectively. The mean coordinates of these distributions are \(\mu_{1}=[0.2,0.5]^{T},\mu_{2}=[0.7,0.8]^{T},\mu_{3}=[0.5,1.0]^{T}\). The dataset \(X_{1}\) was generated by sampling 3000 points from \(\mathcal{N}_{1}\), 1500 points from \(\mathcal{N}_{3}\), and 1500 points from \(\mathcal{N}_{3}\). The resulting dataset \(X_{1}\) is visualized in Figure 1a.  
Now, consider the initial centroids \(C=\{c_{1},c_{2},c_{3}\}\) assigned as \(c_{1}=[0.1,0.2]^{T}\), \(c_{2}=[0.1,0.15]^{T},c_{3}=[0.5,1.0]^{T}\). Running K-means++ on \(X_{1}\) with these initial centroids results in the clustering shown in Figure 1b. The objective function value for the ground truth centroids is \(f(\{\mu_{1},\mu_{2},\mu_{3}\},X_{1})=171.5\). However, K-means++ converges to a suboptimal local minimum with an objective value of 191.52, indicating that it failed to escape a poor solution.  
Using BiModalClust with a sample size of \(s=70\) and a time limit of \(T=1.5\), the algorithm achieves the clustering shown in Figure 1c, with an objective function value of 172.08. This demonstrates that BiModalClust successfully avoided being trapped in a poor local minimum, highlighting its ability to overcome limitations inherent in K-means++.