esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Screening of Synthesized NDG for Biofilm Activity  
The antibiofilm activity of the NDG was assessed using a static microtiter plate assay. Briefly, an overnight culture of MRSA and _Pseudomonas aeruginosa_ strains was diluted using Trypic Soy Broth (TSB) and supplemented with 1% glucose, 100 uL of the culture containing 1.5 \(\times\) 10\({}^{8}\) CFU/mL was added to a 96-well Costar polypropylene plate, and NDG 100 uL was added to one well from the stock solution (1 mg/mL) and was two-fold serially diluted using the TSB to obtain concentrations of 100, 50, 25,12.5, and 6.25 ug/mL, and standard antibiotic ampicillin 1000 ug/mL and gentamycin 200 ug/mL concentrations were used and serially diluted to obtain the minimum concentration of the antibiotic to evaluate antibiofilm activity.  
After overnight growth, the planktonic cells were washed away with deionized water and the remaining adhered biomass was stained with 0.1% crystal violet, washed with water, and solubilized in 30% acetic acid. Total biofilm mass was quantified by measuring the optical density (OD) at 595 nm using a microtiter plate reader. The percent biofilm inhibition was calculated in relation to the amount of MRSA biofilm grown in the absence of NDG (defined as 100%) and the media sterility control (defined as 0% growth). Results from three separate biological replicates were averaged.
# PAGE--Practical AES-GCM Encryption for Low-End Microcontrollers
## 3 Face-Light: Fast AES-CTR Mode Encryption for Low-End Microcontrollers
### Optimized Implementation of Masking Operation  
The masking method prevents the potential attacks with additional computations to reduce information leakages. However, the countermeasure needs longer execution timing than basic implementations. For the high performance, two implementation techniques are applied.  
Before performing the encryption operation, round keys are pre-computed. This is XOR with the value M0 from Round 0 to Round 9. By performing XOR operation in advance, the last round does not include M6, M7, M8, and M9 values. However, the M1 value is applied to all keys. The aforementioned operation is performed in advance to optimize the repetition of the XOR operation and loading the masking value in each round. M0 is XORed with M6, M7, M8, and M9 from line 4. In label 1, M6, M7, M8, and M9 get XORed in row by row for a total of 10 key sets from Round 0 to 9. By doing the XOR operation with M0 to each mask value beforehand, 160 times of bit-wise exclusive-or operations are reduced. M1 XOR operations of the final round key are given in label2.  
After the processing Sub-Bytes operation, M0 is replaced with M1. For optimized implementations, Masked-SBOX table is pre-computed and referenced during the substitute operation.  
**4. PAGE: Practical AES-GCM Encryption for Microcontrollers**  
This section describes distinguished features of suggested method by comparing previous works by [PERSON] and [PERSON] [10]. First, the previous work utilized eight garbage registers to calculate the GHASH function. However, the proposed method only utilizes one garbage register without loss of security against CPA, SPA, and TA. In addition, multiplication computations are optimized to save the register. In total, eight more registers are utilized for Karatsuba algorithm than the previous method. Furthermore, the register optimized version is also investigated Second, the repeated value (\(H_{GHASH}\)) of GHASH function allows to pre-compute the part of Karatsuba operand. The pre-computed value is generated once and called several times, which reduces the execution timing. Third, the fast reduction by [PERSON] and [PERSON] performs faster reduction than the implementation by [PERSON] and [PERSON] [10; 16]. The method is optimized for the low-end microcontroller.
## 4 Experiment and Result
### Parameterisation and Evaluation Criteria  
The experimental setup of this paper is detailed as follows: the CPU utilized is an Intel (R) Xeon (R) 2.40 GHz, the GPU employed is an NVIDIA GeForce RTX4090 (24 G), Python version 3.8 and Pytorch version 1.14 are used, and the model's hyperparameters are configured as outlined in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Size** & **Training Sets** & **Validators** & **Test Sets** & **Medical Entities** \\ \hline
196,496 & 157,194 & 19,652 & 19,650 & 81,020 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution statistics of entities in the MCSCSet dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameterization** & **Setpoint** \\ \hline Epochs & 100 \\ Batch size & 32 \\ Learning rate & \(5\times 10^{-5}\) \\ Learning rate decay & 0.01 \\ Text length & 512 \\ BiLSTM dimensions & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings.  
During the model training process, a regularization method (Dropout technology) was used, which can prevent the model from over-relying on the noise features in the training data, thereby improving the generalization performance. Early Stopping was used to stop training in advance when the performance of the validation set no longer improved to prevent the model from overfitting on the training set. The model was cross validated to ensure that the model performed consistently on different datasets, thereby further verifying the stability and generalization ability of the model. To evaluate the model's effectiveness, we use the precision (P), recall (R), and F1 score as metrics.
## 6 Discussion
### Comparison with Existing Models  
Implementing and using the Data-Driven Personalized Learning Model (DDPLM) in language learning settings is an innovative approach comparable to traditional educational models. This section evaluates DDPLM against traditional and modern models in relation to the learner's efficacy, flexibility, and engagement.  
In the past, language education used to have fixed curricula that provided the same information to all students without considering their learning speed or personal needs. On the other hand, DDPLM employs dynamic, data-driven algorithms to tailor learning experiences, enabling it to update content dynamically depending on learner performance and engagement metrics. This personalized method is based on constructivist learning theories that consider learning an active, individualized activity in which the content is tailored to the learner's existing knowledge and competencies.  
Compared to the traditional adaptive learning models that only change the sequence or difficulty of tasks, the DDPLM provides a more complex adaptation. It modifies these settings and offers individual feedback and prognostic insights, based on which the learners can understand their learning patterns and anticipate zones where they may have problems. This type of proactive approach facilitates the keeping of the learning curve, and by doing so, it eliminates a lot of dropouts, which is a familiar problem in many other language learning programs.  
Most other models require significant customization or specific technological ecosystems to function properly, which is not the case with DDPLM, which integrates with numerous digital platforms without any hassle. DDPLM is a flexible tool that can meet different needs across the educational system, from K-12 to adult education, with minimal changes to the current networking structure.  
The greater level of participation and lower attrition found in DDPLM represent the suite of superiority in maintaining learner motivation in comparison with models using a uniform learning approach. Using personalized content that challenges learners at the right level ensures that the learning process is engaging and current with DDPLM.  
Compared to other educational models, DDPLM is unique as it provides a more flexible, efficient, and learner-centered approach. Its ability to integrate big data analytics  
Figure 4: Global Distribution of Language Learning Platform Adoption.  
into the routine learning process adds value to the learning outcomes and opens a new era of personalized language learning.
# Assessment of Heavy Metal Distributions in Sand Beaches in the Maltese Islands
## 2 Materials and Methods  
### Study Area Location  
Despite Malta being a small island, it is distinguished by its relatively long coastline which approximates 272 km [5]. About 90.5% of the coastline is defined by rocky shores, 2.4% are attributed to sandy and shingle shores, and the remaining 7% constitutes a built-up environment [5].  
Given that the Maltese islands were long ago submerged, the number of biogenic factors vary according to the time period in which the strata formed [6]. According to [PERSON], biogenic sand is derived from molluscs, scleractinian coral, and calcareous green algae, with the same presence found in underwater dome features, as published by [PERSON] et al. [16; 17]. The biogenic accumulation produces cherts including silica rocks, which have been reported to be extensively located around Malta. [PERSON] et al. [13] noted that calcareous sediments are characteristic of low latitudes, while calcareous skeletal sand is prominent at altitudes closer to 60\({}^{\circ}\). The Maltese islands lie on a latitude of 35\({}^{\circ}\)55\({}^{\prime}\)4.7028\({}^{\prime\prime}\) N; hence, calcareous sediments are predominant.  
Coralline red algae, corals, echinoids, and foraminifera have been found in the Lower Coralline Limestone in high quantities, and most of these have been exposed to natural processes which have partially or completely degraded their initial form [18]. [PERSON] et al. reported that the coarseness between Coralline Limestone and Globigerina Limestone varies because of such degradative changes [19]. [PERSON] et al. also noted that biota tend to grow in the coarser Coralline rock [19], thus explaining the relationship between the density of the Coralline and its hardness properties which make it increasingly harder than its Globigerina counterpart.  
A total of 18 beaches were chosen, with 14 well distributed in Malta and 4 in Gozo. Table 1 below provides details of the sampling locations and the corresponding number of samples collected from each beach. The Supplementary Figure S1 included illustrates the geographical locations of the beaches reviewed and a distribution of the heavy metal concentrations obtained. Supplementary Figure S2 provides more detail of the locations of the beaches studied by illustrating aerial views of each beach.
# Assessing the Pedological Impact of Local Anesthesia Dental Simulator as Serious Game
## 1 Introduction  
Dentistry is a profession that demands both intellectual and manual dexterity skills from the operator. Therefore, training dental students is a complex process, as it requires attainment of knowledge and preclinical and clinical training [1]. Generally, dental students receive theoretical knowledge, followed by simulation-based training. A series of structured simulation-based activities based on the hierarchy of needs of the dental students are embedded into the curriculum. Traditionally, phantom heads are used as dental simulators to provide simulation-based training in a conventional preclinical laboratory. The concept of the phantom head or mannequin was first introduced into dentistry by [PERSON] in 1894. The administration of profound local anesthesia (LA) is a key skill for dentists and a prerequisite for most dental treatments. Traditionally, dental students have been trained in the skill of administering LA by injecting oranges, peer-to-peer administration, video demonstrations of these procedures, and LA manikin practice before advancing to patients. However, each of these models has its limitations.  
As technology continues to advance over time, new types of learning modalities have emerged, including educational tools based on augmented reality (AR), virtual reality(VR), or a combination of both [2]. In addition, the literature shows that experiences in AR and simulations improve preparedness for dental students [3, 4]. Serious games describe practice for education and training, rather than merely games for entertainment purposes. Serious games could be used as an interactive, entertaining, and motivational approach to learning, and they have the potential to enhance the motor and decision-making skills of the user [5]. Due to these advantages, serious games are gaining popularity and are increasingly commonly used in higher education. Recently, there has been a plethora of literature on embedding serious games into healthcare education; however, the evidence on their impacts is limited.  
Digital LA simulation devices have been sparsely used in dental education, and they have not been utilized at all for teaching LA administration [1]. In addition, there is limited research about using such devices to improve the student's overall learning experience and skills for administrating LA to a patient. This indicates that further investigation is warranted, and addressing this gap in knowledge forms the focus of this study. We aimed to determine the effectiveness of a mobile 3D and AR simulator for LA training. The serious game is meant to improve the dental student's LA administration technique, experience and confidence. The study addressed two primary research questions: (a) whether the use of the digital LA simulation as a serious game could be beneficial for students, and (b) how learning from the digital LA simulations compared with learning with a traditional LA manikin.
# Preamble Design and Collision Resolution in a Massive Access IoT System
## 3 Preamble Design
### Conventional Preamble  
Zadoff-Chu (ZC) sequences are used as the root sequences of preambles due to its desirable periodic correlation. According to [23], a ZC root sequence with index \(u\) is defined as follows.  
\[x_{u}(n)=\exp[-j\frac{\pi uu(n+1)}{N_{ZC}}],0\leq n\leq N_{ZC}-1, \tag{1}\]  
where \(N_{ZC}\) denotes the length of the ZC root sequence. For the \(n\)th root ZC sequence, the corresponding preamble with zero correlation zones of length \(N_{CS}-1\) is defined by cyclic shifts  
\[x_{u,v}(n)=x_{u}((n+C_{v})\text{ mod }N_{ZC}), \tag{2}\]  
where \(N_{CS}\) and \(C_{v}\) are given by [23]. A set of available preambles are generated from one or two root sequences by cyclic shifts.  
Then, the received sequence in the gNB can be expressed by  
\[y(n)=\sum_{k=1}^{n_{k}}\sum_{e=1}^{n_{k,e}}h_{k,e}x_{k}[(n+t_{k,e})\text{ mod }N_{ZC}]+z(n), \tag{3}\]  
where \(h_{k,e}\) denotes the \(e\)th multi-path channel coefficient for the \(k\)th device, \(t_{k,e}\) denotes the delay of the \(e\)th multi-path for the \(k\)th device, \(n_{k}\) denotes the total number of devices which attempt RAs on the same PRACH, \(n_{k,e}\) denotes the amount of multi-path for the \(k\)th device, and \(z(n)\) denotes the circular symmetry complex Gaussian noise that can be expressed as  
\[z(n)=z_{r}(n)+jz_{i}(n), \tag{4}\]  
where \(z_{r}(n)\) and \(z_{i}(n)\) both follow Gaussian distribution.
## 4 Experimental Validation
### Real Scene Experimental Test
#### 4.2.2 AGV Particle Kidnapping Experiment
The AGV was driven for 10, 20, and up to 50 laps, respectively, for the experimental testing, the time used when the kidnapping event occurred and the time after adjustment were recorded, and the data were analysed and counted in Table 6. From Table 6, it can be clearly concluded that the use of the improved AMCL algorithm can effectively avoid the occurrence of kidnapping events, and the solution of the kidnapping problem can save 42.81% of the AGV navigation time, and once such an event occurs, it can be quickly adjusted to avoid the problem of inaccurate positioning in the process of moving forward.
## 3 Methods
### Bayesian Estimator of Abrupt Change, Seasonal, and Trend (BEAST)
#### 3.2.2 Bayesian Ensemble Model of BEAST  
According to Equation (8), the vegetation time series can be written as \(\hat{y}\ (t_{i})=f(t_{i};\ \Theta)\). Conventional models seek the best single linear, piecewise linear, or polynomial model to estimate parameter \(\Theta\). BEAST applies general linear models to fit signals and detect abrupt changes based on Bayesian model averaging. Each model in BEAST has a probability to be the true estimation. The Bayesian model averaging (BMA) scenario (Equation (9)) [57] is exploited to parameterize the informative weight for quantifying uncertainties (likelihood \(P\left(\beta_{\text{M}},\sigma^{2},\text{M}\ |\ y\right)\)). The model reduces overfitting and alleviate model misspecification in conventional methods (i.e., single best methods).  
\[P\left(\beta_{\text{M}},\sigma^{2},\text{M}\ |\ y\right)\propto\ P\left(y\ |\ \beta_{\text{M}},\sigma^{2},\text{M}\right)\pi\left(\beta_{\text{M}},\sigma^{2},\text{M}\right) \tag{9}\]  
\[\pi\left(\beta_{\text{M}},\sigma^{2},\text{M}\right)=\pi\left(\beta_{\text{M }},\sigma^{2}\ |\ \text{M}\right)\pi\left(\text{M}\right) \tag{10}\]  
where \(P\left(y\ |\ \beta_{\text{M}},\sigma^{2},\text{M}\right)\) is the probability of observing data (\(y\))--this probability is simply Gaussian \(P\left(y\ |\ \beta_{\text{M}},\sigma^{2},\text{M}\right)\)--\(\beta_{\text{M}}\) is the given model's parameters; \(\pi\left(\beta_{\text{M}},\sigma^{2},\text{M}\right)\) is the prior distribution of the model parameters and it is considered the normal-inverse Gamma distribution; \(\pi(\text{M})\) is the prior on model structure M; \(\sigma\) denotes the data noise.  
The posterior distribution \(P\left(\beta_{\text{M}},\sigma^{2},\text{M}\ |\ y\right)\) includes the information for inferring ecosystem dynamics. The Markov Chain Monte Carlo (MCMC) sampling was utilized to obtain random samples for posterior inference as the intractable analysis of the posterior distribution. MCMC generates various sample-based statistics to test the BMA-based hypotheses. The details of the MCMC method are available in [58]. Additionally, the information on the posterior inference and uncertainties estimation of change detection can be found in [36].
# Novel Applications of Microbial Fuel Cells in Sensors and Biosensors
###### Abstract  
A microbial fuel cell (MFC) is a type of bio-electrochemical system with novel features, such as electricity generation, wastewater treatment, and biosensor applications. In recent years, progressive trends in MFC research on its chemical, electrochemical, and microbiological aspects has resulted in its noticeable applications in the field of sensing. This review was consequently aimed to provide an overview of the most interesting new applications of MFCs in sensors, such as providing the required electrical current and power for remote sensors (energy supply device for sensors) and detection of pollutants, biochemical oxygen demand (BOD), and specific DNA strands by MFCs without an external analytical device (self-powered biosensors). Moreover, in this review, procedures of MFC operation as a power supply for pH, temperature, and organic loading rate (OLR) sensors, and also self-powered biosensors of toxicity, pollutants, and BOD have been discussed.  
m _Applied_  
_sciences_
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 2 Related Work
### Location-Based Network  
Location-based routing algorithms help to improve the QoE. Location-aided routing (LAR) is a method of broadcasting only in a limited area using the location information of the source node and the destination node [16]. It is assumed that each node knows its physical location using GPS. LAR operates by setting an expected area based on location information between a source node and a destination node to gradually reduce the flooding area. As a result, route request messages are less often sent to unnecessary locations, which can provide more resources for data transmission. LAR is an advantageous routing protocol for dense networks. Greedy Perimeter Stateless Routing (GPSR) involves transmitting data by selecting the node closest to the destination among the nodes in its transmission range using its location information [17]. This method delivers data through the most optimized path between the source and the destination. Each node configures the routing table by periodically sending notification messages to each other to maintain information between neighboring nodes. GPSR consists of only information on nodes within its transmission range, not information on the entire network. Therefore, since a message is not sent to the entire network when searching for a route, the number of cases in which a route request message is delivered to an unnecessary location is reduced. It is a routing protocol used to secure a minimum path from a source node to a destination node. Location-based network routing still causes unnecessary interruptions and overloads the network.
## 7 Receding Horizon Trajectory Planning
### LQR-Based Trajectory Generation
^{T}j_{k+i}+\tilde{\zeta}. \tag{54}\]  
The estimation of both \(J_{k+i}\) and \(j_{k+i}\) can be obtained in a recursive way starting from the goal position \(\mathbf{x}_{x+N}\) to the initial position \(\mathbf{x}_{k}\), using Riccati differential equation for all \(i=\{0,\ldots,N-1\}\). \[J_{k}=Q+A_{k}^{T}I_{k+1}A_{k}- \tag{55}\] \[(P+B_{k}^{T}I_{k+1}A_{k})^{T}\cdot(R+B_{k}^{T}I_{k+1}B_{k})^{-1} \cdot(P+B_{k}^{T}I_{k+1}A_{k})\] \[j_{k}=p+A_{k}^{T}j_{k+1}+A_{k}^{T}I_{k+1}c_{k}\] \[-(P+B_{k}^{T}I_{k+1}A_{k})^{T}\cdot(R+B_{k}^{T}I_{k+1}B_{k})_{k}^ {-1}\cdot(r+B_{k}^{T}I_{k+1}+B_{k}^{T}I_{k+1}c_{k}).\]  
In general, system dynamics is described by:  
\[\mathbf{x}_{k+1}=\mathbf{f}_{d}(\mathbf{x}_{k},\mathbf{u}_{k})=A_{k}\mathbf{x} _{k}+B_{k}\mathbf{u}_{k}. \tag{56}\]  
If the system dynamics is nonlinear, \(A_{k}\) and \(B_{k}\) are recalculated by linearizing the \(\mathbf{f}_{c}\) at each time index. Since linearization has to be carried out in each iteration, it is called the iLQR [90]. \[A_{k}=\frac{\partial\mathbf{f}_{c}}{\partial\mathbf{x}}(\mathbf{x}_{k},\mathbf{ u}_{k}),\quad B_{k}=\frac{\partial\mathbf{f}_{c}}{\partial\mathbf{u}}(\mathbf{x}_{k}, \mathbf{u}_{k}). \tag{57}\]Boundary or goal position conditions are given by \(S_{k+N}=Q_{goal},\quad j_{k+N}=q_{goal}\). The feedback control policy in LQR is fully determined as follows:  
\[ \begin{split}\pi_{k}(\mathbf{x}_{k})&=-(R+B_{k}^{T}k _{k+1}B_{k})^{-1}\cdot(P+B_{k}^{T}I_{k+1}A_{t})\mathbf{x}_{k}\\ -(R+B_{k}^{T}I_{k+1}B_{k})^{-1}\cdot(r+B_{k}^{T}I_{k+1}+B_{k}^{T} I_{k+1}B_{k}).\end{split}  \tag{58}\]  
As given in (55), system stability depends on system dynamics. When quadrotor dynamics is nonlinear, the stability of iLQR is not guaranteed. [PERSON] and [PERSON] [91] attempted to address the stability issue by proposing a novel method called LQR smoothing; this method can be applied for linear or nonlinear systems to acquire the minimum-cost trajectory. The main difference in LQR smoothing compared to LQR is that LQR minimizes the cost of not only backward direction, i.e., cost-to-go, but also applies forward direction, i.e., cost-to-come [58; 91; 92]. However, the output of LQR, iLQR or LQR smoothing does not address the system noise. Both linear or nonlinear state estimator may eliminate the system noise. LQG [93; 94] is one of the ways to solve this problem. LQG consists of a state estimator, i.e., Kalman filter (KF), and state feedback, i.e., iLQR or LQR.