esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
## 3 Methods
### Single-Robot End-to-End Navigation
#### 3.2.1 Basic Reinforcement Learning Framework  
Classical Deep Deterministic Policy Gradient (DDPG) [41] is a kind of remarkable reinforcement learning algorithm to address the continuous control problem. As we all know, the trade-off between the exploration and the exploitation is one of the most important problems in the reinforcement learning field. To increase the sample efficiency, the DDPG uses the deterministic target policy \(\mu:\mathcal{S}\leftarrow\mathcal{A}\) rather than the stochastic policy \(\pi\), and the corresponding exploration decrease is made up of the off-policy technique. The Bellman equation can be rewritten as Equation (4), where the \(\gamma\) term represents the discount factor of the equation:  
\[Q^{\mu}\left(s_{t},a_{t}\right)=\mathbb{E}_{\tau_{t},s_{t+1}\sim E}\left[r \left(s_{t},a_{t}\right)+\gamma Q^{\mu}\left(s_{t+1},\mu\left(s_{t+1}\right) \right)\right]. \tag{4}\]  
At each timestep, the actor networks and the critic networks are updated by sampling a minibatch uniformly from the memory buffer. The algorithm also creates a copy of the actor and critic networks, \(Q^{\prime}\left(s,a|\varrho^{Q^{\prime}}\right)\) and \(\mu^{\prime}\left(s|\theta^{\mu^{\prime}}\right)\), respectively; these target networks are then updated softly by the learned networks: \(\theta^{\prime}\leftarrow\tau\theta+(1-\tau)\theta^{\prime}\) with \(\tau\ll 1\). This trick can greatly improve the stability of learning.  
The loss function for the critic networks can be formulated as Equation (5):  
\[L=\frac{1}{N}\sum_{i}\left(y_{i}-Q\left(s_{i},a_{i}|\varrho^{Q}\right)\right)^ {2},\text{ }where\text{ }y_{i}=r_{i}+\gamma Q^{\prime}\left(s_{i+1},\mu^{\prime}\left(s_{i+1}| \theta^{\mu^{\prime}}\right)|\varrho^{Q^{\prime}}\right). \tag{5}\]  
In addition, the actor networks can be updated by using the sampled policy gradient as shown in Equation (6):  
\[\
abla_{\theta^{\mu}}J\approx\frac{1}{N}\sum_{i}\
abla_{a}Q\left(s,a|\varrho^ {Q}\right)\Bigg{|}_{s=s_{i},a=\mu\left(s_{i}\right)}\
abla_{\theta^{\mu}}\mu \left(s|\theta^{\mu}\right)\Bigg{|}_{s_{i}}. \tag{6}\]
## 3 Case Study: Purse Seiner in the Adriatic Sea
### Limitations and Assumptions  
The limitations and approximations of this paper are as follows:  
* The system boundary of the assessments is placed on the ship, where only the power system is investigated. The other units of a ship (e.g., hull, additional equipment, crew, port operations, etc.) are not considered. However, this approach sufficiently identifies alternative power systems to reduce emissions at a reasonable cost, compared to the configuration of a conventional diesel power system.
* Since the considered purse seiner does not operate on a fixed route and its operative profile varies, an average load of 56% is taken for the calculation of energy needs.
* One of the assumptions in this paper is the simplification of fuel transportation processes. However, stationary processes are a major contributor to emissions from the WTT phase, so this assumption does not have a major impact on total emissions from the WTT phase.
* Storage tank dimensions for a particular fuel (LNG, LBG, hydrogen, etc.) are not considered, which is a limitation for their use onboard small fishing vessels. Such fuel tanks occupy additional space on ships that can be used for different purposes (e.g., for similar energy content, an LNG tank occupies 3-4 times greater a space than an MGO tank) [110]. Nevertheless, environmental and economic analyses performed within the study successfully identified the most appropriate power system configuration that satisfies both criteria.
* Biofuels are assumed to be climate-neutral, and their combustion does not result in CO\({}_{2}\) emissions.
* The environmental impact of dual-fuel engines is assumed to be the same as for diesel engines. Since manufacturing emissions from engine manufacturing are rather small compared to batteries and fuel cells and their share in overall life-cycle emissions is small, this approach does not result in a great change in the final results.
* When the ship is powered by a dual-fuel engine, it is assumed that it is always operating in dual-fuel mode, and the pilot fuel share is 5% for each power system configuration that includes a dual-fuel engine.
* Despite recent fuel price fluctuations, fuel costs within the LCCA are calculated with average fuel prices obtained from the literature. Sensitivity analysis regarding an increase in fuel price is not considered.
* Another limitation regarding the LCCA is that the costs are investigated without analysing the net present value. However, the LCCA still identifies the most cost-efficient option.
* The use of ammonia in ICEs is still in the development phase, and commercial engines should be available in 2024. It can be assumed that the implementation phase of such a power system would yield different emissions and overall costs than what is obtained in this study. The LCA and LCCA of this power system configuration should be repeated when their technology readiness is higher.
## 3 Proposed Congestion Management Approach
### Stability Margin  
To make the system more robust against voltage-related disturbances, a suitable level of voltage stability should always be maintained in the power system. The continuation method is used as a tool for voltage security studies in [25]. In this work, the VSM is applied to measure the voltage stability, and the load is assumed to be of the constant-power type. Figure 2 illustrates the bifurcation curve between the power and the voltage. In the P-V curve, the maximum value of the loading parameter, from the base case up to the saddle node bifurcation (SNB) point, which is regarded as the voltage collapse point, indicates the VSM, which is described as the loading distance between the base case and the SNB point in voltage collapse:  
\[VSM=P_{SNB}-P_{base} \tag{30}\]  
Figure 2: Bifurcation curve for voltage stability analysis.  
Equation (30) illustrates that a greater VSM results in a more stable system from a voltage stability viewpoint. On the other hand, a stressed power system typically experiences a low VSM.
## 3 Data Fusion
### Trends and Challenges  
In the field of data fusion research, there exist some research trends, including data processing position, lightweight data fusion algorithms, design of pervasive context-aware frameworks, as well as cloud computing. As communication consumes more power than local computation, wireless data transmission uses a large amount of power [26]. Therefore, processing data locally is an efficient way to save power. However, the data processing ability of nodes is generally limited, which leads to a contradiction about data processing localization. How to solve this problem is a hotspot for further research. At the same time, limited resources and computation ability make it difficult for nodes to carry out large scale computing, and therefore designing lightweight embedded data fusion algorithms to alleviate the load of processors is necessary. In addition, BSNs usually operate in many different situations, while the human body has diverse signs and actions, and so does the environment. Studies on adaptive and distributed BSN frameworks to adapt to these cases is necessary. In Reference [85], researchers designed a distributed framework based on a Markov decision process for coordinating sensing among sensors in a BSN system. The framework can generate a globally optimal sampling policy by analyzing the status of each sensor. It solves the problems that occur when the sampling rates of sensors are too high or too low, so as to extend the overall lifetime of the BSN system. With the development of the cloud computing technique, mining data streams of body sensors online or offline with cloud technology has become a new research hotspot. In Reference [86], researchers integrate cloud computing into BSNs, providing flexibility of data storage solutions using cloud technology. They globally realize data analysis, storage and access for BSN services based on cloud computing.  
There are still some difficulties in data fusion of BSNs. Affected by human activities and equipment, signals of sensors are inevitably mixed with some noise, which leads to the fact that data collected by sensors cannot be used directly [27, 87]. How to effectively filter all kinds of noise is now a big challenge. In practical BSN applications, there is a certain degree of redundancy among data collected by multiple nodes. Collaborative data fusion could effectively reduce the amount of data and improve calculation accuracy. To figure out the relevance of heterogeneous data is difficult, therefore, designing a better collaborative data fusion algorithm becomes another challenge. Furthermore, the combination of BSN techniques and cloud computing increases the difficulty of data management and calculation. Large scale computing frameworks design, real-time data processing and data stream management have all become major challenges.
# Mechanism to Estimate Effective Spectrum Availability Inside Smart Buildings
## 3 System Model
### C1 Model  
The propagation losses between the BS and the place where each SS was located can be determined using the penetration loss with the NLOS conditions model [25] as follows:  
\[L_{BS-SS}=L_{outside}+L_{indoor} \tag{3}\]  
\[L_{indoor}\ =W_{e}+W_{ge}+\max(nW_{i},\alpha W_{a})-G_{FH} \tag{4}\]  
\[G_{FH}=\left\{\begin{array}{ll}pG_{n}\\ hG_{h}\end{array}\right.. \tag{5}\]  
\(L_{outside}\) represents the LOS model component and is the propagation loss between the transmitting antenna and the incident wall, while \(L_{indoor}\) represents the internal path propagation loss of the SB. \(W_{e}\) is the attenuation from the building's external wall, and \(W_{ge}\) compensates for the penetration of the outer walls. \(nW_{i}\) is the total loss considering \(n\) interior walls, \(\alpha W_{a}\) represents the total ascending loss, and \(G_{FH}\) is a measure of the total gain due to the height of the SS, using the floor gain \(G_{n}\) in dB/floor with \(p\) floors or the height gain \(G_{h}\) in dB/m with height \(h\).  
According to the FRIIS model, \(L_{outside}\) can be represented by [25]  
\[L_{fs}=32.44+20\log(f_{MHz})+20\log(r_{Km}) \tag{6}\]  
where \(f_{MHz}\) is the operating frequency in MHz, and \(r_{Km}\) is the path length in km.  
When considering the EGLI model, \(L_{outside}\) is represented by the following expression [26]:  
\[L_{egli}=-4.28+40\log(r_{Km})+20 log\left(\frac{f_{MHz}}{40}\right)-20\log(H_{ BS}H_{SS}) \tag{7}\]where \(H_{BS}\) is the height in meters of the antenna of the primary system and \(H_{SS}\) is the height in meters of the measuring point.  
The third model considered, the COST-WI LOS component, is represented in \(L_{outside}\) by the following equation [25]:  
\[L_{WI}=42.6+20\log(f_{MHz})+26\ \log(r_{Km}) \tag{8}\]  
To model the power level with a high level of precision, the power contribution of the surrounding SBs inside an SC was considered. For this reason, a five-ray model was used: three reflected rays corresponded to reflections due to neighboring buildings, and the two other direct rays came from the BS, as shown in Figure 3. The angles shown in the figure were used to find the appropriate power level in the specific direction of both the incident rays and reflected rays. All the power contributions were added to minimize the spectrum availability inside the SB.  
The first four rays were calculated as horizontal components of the power level received from the transmission site, and the fifth ray was the vertical component of the incident power that crossed the roof of the building. Therefore, by replacing it in Equation (1), we found that Condition 1 was satisfied if  
\[I_{BS-SS}=\left(\sum_{j=1}^{5}(P_{BS}-L_{outside}+10\log cos\theta_{j}-L_{ indoor})\right)<P_{TH} \tag{9}\]  
where \(j\) represents the path of the BS signal on each of the four faces and the roof of the building, and \(cos\theta_{j}\) is the factor that corrects the direction of the path for the normal incidence of each ray.
# A Hybrid Exact-Local Search Approach for One-Machine Scheduling with Time-Dependent Capacity
###### Abstract  
Machine scheduling is a hard combinatorial problem having many manifestations in real life. Due to the schedule followed, the possibility of installations of machines operating sub-optimally is high. In this work, we examine the problem of a single machine with time-dependent capacity that performs jobs of deterministic durations, while for each job, its due time is known in advance. The objective is to minimize the aggregated tardiness in all tasks. The problem was motivated by the need to schedule charging times of electric vehicles effectively. We formulate an integer programming model that clearly describes the problem and a constraint programming model capable of effectively solving it. Due to the usage of interval variables, global constraints, a powerful constraint programming solver, and a heuristic we have identified, which we call the \"due times rule\", the constraint programming model can reach excellent solutions. Furthermore, we employ a hybrid approach that exploits three local search improvement procedures in a schema where the constraint programming part of the solver plays a central role. These improvement procedures exhaustively enumerate portions of the search space by exchanging consecutive jobs with a single job of the same duration, moving cost-incurring jobs to earlier times in a consecutive sequence of jobs or even exploiting periods where capacity is not fully utilized to rearrange jobs. On the other hand, subproblems are given to the exact constraint programming solver, allowing freedom of movement only to certain parts of the schedule, either in vertical ribbons of the time axis or in groups of consecutive sequences of jobs. Experiments on publicly available data show that our approach is highly competitive and achieves the new best results in many problem instances.  
scheduling; constraint programming; heuristics; local search Article
## 2 Materials and Methods  
### Quantitative Analysis of JGT by HPLC  
Each of the nine reference standards for JGT received from ChemFaces Biochemical (Wuhan, China) has a purity of 98% or above. These standards were used in high-performance liquid chromatography (HPLC) for quantitative analysis of JGT. The JGT water extract was dissolved in methanol and adjusted to a 5 mg/mL before being filtered. To obtain standard mixtures for quantitative analysis, methanol was used to mix and dilute the stock solutions of the nine reference standards. The nine JGT compounds were quantitatively analyzed by using a Waters Alliance e2695 HPCL system (Waters Corporation, Milford, MA, USA) outfitted with an ultraviolet (UV) photodiode array (PDA) detector (Waters Corp.) with a wavelength range of 190-400 nm. Chromatographic separation was performed using a 0.1% (_r_/_v_) aqueous FA gradient solvent and acetonitrile at 30 \({}^{\circ}\)C with a Sunfire C18 Column (Waters Corp.). The flow rate was 1 mL/min, and the gradient elution was 5 to 55% B for 0 to 35 min, 55 to 100% B for 35 to 40 min, 100% B for 40 to 46 min, and 5% B for 47 to 55 min. Each sample was injected at 10 \(\upmu\)L and all data were analyzed with Empower software version 3 (Waters Corp.).
## 3 Artificial Neural Networks
### Overfitting Phenomenon  
A recurring problem when using supervised learning models is the occurrence of **overfitting**. Overfitting is a common challenge in machine learning where a model is trained to learn from a specific dataset to the point that it becomes too specialized to that dataset. This can lead to the model becoming less accurate, or even ineffective, when presented with new, unseen data. Essentially, overfitting occurs when the model has learned to recognize the training dataset so well that it becomes unable to generalize and make accurate predictions on new data. For this reason, a validation procedure is necessary to prevent the occurrence of the overfitting phenomenon. There are several ways of performing the validation process that can be grouped into two categories:  
* **Dataset split or data partitioning:** This allows for the evaluation of the model's performance on a separate set of data that the model has never seen before;
* **Cross-validation:** This involves dividing the dataset into multiple subsets (folds) and systematically training and testing the model on different combinations of these folds to assess its generalization performance and to tune hyperparameters.  
In this work, it was decided to use the data-partitioning technique to avoid overfitting. This decision was made because a relatively large dataset was available, so dividing it did not pose any risk to net training. With this technique, the overall dataset is divided into two subsets: a training subset and a validation subset. The former is used to train the model, while the latter is used to verify that the model generalizes correctly. Specifically, if the model's performance on the validation set starts to degrade while the training error continues to decrease, it is possible to stop the training to prevent overfitting.  
Furthermore, in order to utilize the extensive dataset effectively, a test subset was included for independent evaluation at the end of training. This subset helps to assess the model's performance on unseen data, providing an unbiased estimate.  
Regarding this division of the dataset, it was performed on Matlab with the appropriate \"Divideblock\" function, with which data are split into three subsets using three contiguous blocks of the original dataset. The fraction of the original data that goes into each subset is as follows:  
* 70% of the dataset for the training phase;
* 20% of the dataset for the validation phase;
* 10% of the dataset for the test phase.  
The dataset was divided in this way strategically to strike a balance between preventing underfitting, ensuring an adequate number of training samples, and guarding against overfitting by providing a sufficiently sized validation set for assessing model generalization.
## 3 Type-Reduction Algorithms  
Currently, there exist different algorithms in order to obtain the centroid of the IT2 MFs [9], and the most-used type-reduction methods are inspired in the Karnik Mendel algorithm. This algorithm is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Step** & **Left Point** & **Right Point** \\ \hline
1 & Sort \(x_{i}\) by increasing order & Sort \(x_{i}\) by increasing order \\ \hline \multirow{3}{*}{2} & Initialize \(w_{i}\) as: & Initialize \(w_{i}\) as: \\  & \(wi=\frac{\overline{w_{i}}+\underline{w_{i}}}{2}\) & \(wi=\frac{\underline{w_{i}}+\overline{w_{i}}}{2}\) \\ \hline \multirow{3}{*}{3} & Compute: & \\  & \(y=\frac{\sum_{i=1}^{N}x_{i}\underline{w_{i}}}{\sum_{i=1}^{N}w_{i}}\) & \(y=\frac{\sum_{i=1}^{N}x_{i}\underline{w_{i}}}{\sum_{i=1}^{N}w_{i}}\) \\ \hline \multirow{3}{*}{4} & Find the switch point \(k\) where & Find the switch point \(k\) where \\  & \(x_{k}<y<x_{k+1}\) & \(x_{k}<y<x_{k+1}\) \\ \hline \multirow{3}{*}{5} & Set & Set \\  & \(w_{i}=\left\{\begin{array}{l}\overline{w_{i}},\ i\leq k\\ \underline{w_{i}},\ i>k\end{array}\right.\) & \(\underline{w_{i}}=\left\{\begin{array}{l}\underline{w_{i}},\ i\leq k\\ \overline{w_{i}},\ i>k\end{array}\right.\) \\ \hline \multirow{3}{*}{6} & Compute: & Compute: \\  & \(y=\frac{\sum_{i=1}^{N}x_{i}\underline{w_{i}}}{\sum_{i=1}^{N}w_{i}}\) & \(y=\frac{\sum_{i=1}^{N}x_{i}\underline{w_{i}}}{\sum_{i=1}^{N}w_{i}}\) \\ \hline \multirow{3}{*}{7} & If \(y==y^{\prime}\) then stop, set \(y_{i}=y\) and \(L=k\), if not go to step 8. & If \(y==y^{\prime}\) then stop, set \(y_{i}=y\) and \(R=k\) if not go to step 8. \\ \hline \multirow{3}{*}{8} & Set \(y=y^{\prime}\) and go to step 3. & Set \(y=y^{\prime}\) and go to step 3. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Karnik Mendel algorithm.  
Figure 2: Type-1 and Interval Type-2 Mamdani Fuzzy Inference Systems.  
The main goal of this algorithm is to find the critical points that define the combination of upper and lower membership functions, and these points are called the switch points.
## 3 Metaverse Classroom: A Layered Architectural View
### Personalization and Accessibility Layer
#### 3.3.2 Assistive Technologies  
Assistive technologies in a Metaverse classroom ensure that virtual education is inclusive and accessible to all students, particularly those with disabilities or language barriers [141]. These tools help break down barriers to learning, allowing every student to fully engage with class activities and course materials [15]. For example, closed captioning provides a text version of spoken content, benefiting students who are deaf or hard of hearing [7]. Similarly, visual cues guide students through interactive content, helping those with auditory processing issues better understand instructions [9]. Voice-to-text technology enhances accessibility by allowing students to use voice commands to interact with the Metaverse classroom [142]. This tool is particularly valuable for students with mobility challenges or learning disabilities. For example, students with dyslexia can participate in written assignments or group discussions by speaking instead of typing [143]. Voice-to-text technology promotes independence by allowing students to contribute without relying on traditional text-based inputs, ensuring that all voices are heard [19]. Real-time translation services support a multilingual learning environment by overcoming language barriers [144]. These tools allow international students or those learning the primary language of instruction to follow course material in their preferred language [128]. For example, a student in a science lesson can receive real-time translations of complex terms, enabling them to participate as actively as their peers [13]. Real-time translation fosters intercultural understanding and collaboration, making the Metaverse classroom more inclusive and globally connected.  
Customizable accessibility settings allow students to adjust features based on their preferences and needs [145]. Students can modify font sizes, adjust colors for visual impairments, or alter audio settings to improve sound clarity [146]. For students with ADHD or attention challenges, features that reduce visual clutter or provide focus cues can enhance engagement [147]. These adaptable settings create a flexible learning environment that caters to individual needs and promotes self-directed learning [148]. Finally, gesture recognition and spatial audio further enhance the inclusivity of the Metaverse classroom [149]. Gesture recognition enables students with limited mobility to interact with virtual environments using hand or head movements, while spatial audio directs sound toward specific areas, improving auditory focus [150]. For example, spatial audio can help students concentrate on their teacher's instructions without being distracted by background noise. Gesture recognition allows students to engage with interactive elements, even if they cannot use traditional controls. Together, these tools make the Metaverse classroom more accessible for students with diverse abilities, creating a truly inclusive educational experience.