esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Multi-Robot Task Allocation with Reinforcement Learning
## 2 Background
### Reinforcement Learning  
Recent advances in hardware have made deep learning possible, and an artificial neural network trained on massive amounts of data shows high performance in many fields, including vision and natural language processing. Reinforcement Learning (RL) has also progressed rapidly with deep learning. In RL, an agent learns the optimal action in a given state, and massive amounts of data and a simulator are required to train the agent. RL is defined on a Markov Decision Process with a tuple \(<\mathcal{S},\mathcal{A},\mathcal{P},\mathcal{R},\gamma>\) where \(\mathcal{S}\) is the set of all possible states and \(\mathcal{A}\) is a set of all possible actions. \(\mathcal{P}\) represents the transition probability, \(\mathcal{R}\) is the reward function and \(\gamma\) is a discount factor. The advantage of using MDP is that the decision process can be completely characterized by the current state. In other words, the current state is a sufficient statistic of the future. The goal of the training is to find the optimal policy \(\pi\) which maximizes the expected discounted return \(V^{\pi}(s)\) of a state \(s\) where  
\[V^{\pi}(s)=\mathbb{E}[\sum_{k=0}\gamma^{k}r_{t+k+1}|s] \tag{1}\]  
Similarly, the goal of MRTA is to find the optimal policy \(\pi\) which describes how to allocate agents. In detail, instantaneous allocation can be viewed as a type of RL with a discount factor \(\gamma\) of 0, meaning that the future is not considered. If time is related, MRTA problem includes the future impact, and then discount rate \(\gamma\) is a non-zero number. We hypothesize that the neural network can encode information optimal allocation.  
There are several benefits of RL-based approaches. First, inference is fast despite the fact that training takes a long time. Inference computes the cross-attention between robots and tasks to allocate a single robot, with time complexity of \(\mathcal{O}(MN)\). Because neural networks compress decision rules learned from the training data into parameters, it is possible to approximate the solution without considering all possible combinations. These trained parameters result in better performance than a meta-heuristic solution search. Next, this method can handle complex dependencies, where the optimal allocation for a task may be affected by the previous allocation. Additionally, it does not require delicately designed heuristics, which is one of the problems with meta-heuristic algorithms.
## 5 Simulation Results
### Simulation Parameters  
In ISTN, multiple users are served by an LEO satellite within its coverage. The free space loss coefficient is given as \(\eta=(1/4f\pi d)\), in which \(f\) and \(d\) are the frequency and distance between satellite and user.  
The neural networks (NNs) in the proposed algorithm all consist of three fully connected hidden layers with ReLU as the activation function and Adam optimizer. The first two hidden layers of NN in the soft value network has 64 neurons while the last layer has 1 neuron. The hidden layers of NNs in the policy network and the soft Q network all have 64 neurons. The learning rate of the Adam optimizer is \(10^{-3}\) while the update rate of target networks is 0.005. Their loss functions are given in Equations (21), (22) and (27), respectively. Replay memory size and batch size are 200 and 32, respectively, and the episode is 5000 with 200 timestamps. Since all users are equally \"important\" in the ISTN, their QoS constraints contribute to the reward equally as well. So, we initialize the Lagrangian multipliers to 1. The initial value of the learning rate of the Lagrangian multiplier is usually small. So we set it to be \(10^{-3}\). The tuning of them could be performed by introducing additional NNs with different optimizers; however, we fix their values to avoid extra complexity. The parameters are listed in Table 1.
# The Effect of the Improvement Technology on the Quality of Midu Pork Roll
## 1 Introduction  
Midu pork roll (MPR), traditional fermented meat produced in Midu County, Dali Bai Autonomous Prefecture, Yunnan province, China, has a longer shelf life than fresh meat products. This product has been included in royal recipes for more than 300 years since the Qing Dynasty (1636-1912). The traditional fermentation process of MPR is: (1) put an appropriate amount of fresh pork into a bag made from the skin of the feet of pigs; (2) add some ingredients into the bag, i.e., salt, red yeast rice and a variety of natural spices; (3) tightly seal the bag and cook it; (4) the pork meat in the bag will undergo fermentation under anaerobic conditions for 20 d. It is named roll hood because its shape looks like the fresh feet of a pig [1]. The processed MPR is favored by local people for its nutritional value and non-greasy characteristics. For mass consumers, however, the strong sour taste, hard texture and single taste of MPR have become increasingly unacceptable. This, in fact, has restricted the development of MPR products.  
Flavor is an important quality attribute for determining consumer acceptance and preference [2]. Fermentation conditions, the types of raw and auxiliary materials and the ratio of ingredients have an important influence on the formation of the characteristic flavor of MPR. Therefore, the degradation of meat protein, the oxidation of lipids and the Maillard reaction generated by heating, the rich short peptides and free amino acids (FAAs) are all important factors that determine the flavor of MPR. The amino acid metabolites producedduring protein degradation account for 6.00% of the total volatile compounds [3]; the hydrolysis and oxidation of lipid substances generate about 60% of the volatile compounds in fermented meat products [4; 5].  
Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) has been widely used recently to identify volatile compounds [6; 7; 8]. FAAs, color, texture and water activity (Aw) are widely used in meat quality analysis. These factors are important indicators for distinguishing flavor profiles and characterizing differences in meat quality [9; 10]. Hence, HS-SPME-GC-MS combined with FAAs analysis and physical and chemical indicators analysis can be used to characterize the flavor and the quality of MPR under different process conditions, which may also provide a reference for establishing a much more comprehensive, quick method to identify those key factors of MPR.  
Based on the traditional production method of MPR, this study specifically improved the process flow by controlling the fermentation conditions, changing the steaming time, adjusting the fat-to-lean ratio of meat, adding flavor enzymes and enzymatic tenderization technology and tumbling technology. In this way, the aim of this study is to improve MPR's sensory quality and flavor, increase consumer acceptance, and also increase the economic value of MPR production. It provides a theoretical basis and more references for the development of traditional fermented meat products from different regions.
## 4 Experimental Validations
### Inspection of Small Flaws Compared to the Wavelength  
Several measurements have also been carried out to evaluate the models validity on cracks with a small height with respect to the wavelength.  
The component used for the following experimental validations is described in Figure 13. The thickness of the specimen, which is the depth to be inspected in experiments (see Figure 14b for an example of inspection configuration), is indicated in red in Figure 13.  
The specimen contains the following:  
* Four electro-eroded notches (5 mm height \(\times\) 30 mm extension) and four side-drilled holes (2 mm diameter \(\times\) 40 mm extension) which are embedded with respective bottom ligaments (distance between notch extremity and backwall) of 5, 10, 15 and 20 mm;
* One backwall breaking notch (not considered in the following validation study);
* Four flat bottom holes on the right side.  
Figure 12: (**a**) TOFD configuration on a skewed backwall breaking crack; (**b**) top edge diffraction echoes amplitudes versus skew angle for measure, 2.5D GTD and 3D PTD and FEM simulations.  
The experimental validations presented here concern inspections in immersion of the notch of the highest ligament 20 mm (surrounded in blue in Figure 14b). We privilege validation on the flaw with the highest ligament for a better confidence in the measurements, even though measurements on the other flaws were satisfactory. A 10 mm extent starter hole was pre-drilled before producing the notch of 30 mm extension by electro erosion. To study small notch heights with respect to the wavelength, since the flaw height (5 mm) is relatively important, low frequency transducers (0.5 MHz and 1 MHz) have been employed in experiments. We controlled the measurements reproducibility with an obtained confidence interval of the order \(\pm\)3 dB owing to the low time resolution of the echoes observed at the low frequencies used (0.5 MHz and 1 MHz). Measurements of side-drilled holes have been carried out to set the input signal in simulation [2].
## 3 Heterogeneous MLR Network of the NG-PON Type
In order to be adapted to this type of network, it is found that by increasing the capacity of the channel from 2.5 Gbps to 10 Gbps, the information spectral density (ISD) increased; and by increasing the spacing between channels from 25 GHz to 100 GHz, the ISD decreased, that is, at a higher transmission rate. For example, from 10 Gbps to 40 Gbps, the ISD increases. In this article the difference between the spectral density of information and the spectral efficiency is clarified. The first refers to a communications channel with n wavelengths, while the second has a single wavelength; in the present work the Spectral density is the same distributed spectral efficiency that is presented as a ratio of the total capacity generated in the system vs. the optical spacing, validating and reaching the conclusion that this factor can be considerably improved if the spacing of the optical channels is significantly reduced.
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 2 Methodology  
This review was conducted using a literature search to identify relevant studies on the biotechnological remediation of organic pollutants in coastal marine ecosystems. The literature search was performed across multiple scientific databases, including Scopus, Web of Science, PubMed, Google Scholar and ScienceDirect. Keywords used include combinations of terms such as \"bioremediation\", \"phytoremediation\", \"marine pollution\", \"organic pollutants\", \"coastal ecosystems\", \"microbial degradation\", and \"biotechnology for environmental remediation\".  
Inclusion criteria were as follows:  
* Peer-reviewed articles published in English.
* Studies focusing on organic pollutants (e.g., hydrocarbons, pesticides, pharmaceuticals, microplastics) in coastal or marine environments.
* Articles discussing biotechnological approaches, such as bioremediation and phytoremediation.
* Publications from the last 15 years (2010-2025) were prioritized to ensure recent and relevant findings, although seminal works predating this period were also included when foundational to the topic.
* Reference lists of key publications were manually reviewed to identify additional sources.
* Data synthesis was thematic, emphasizing pollutant categories, microbial and phytoremediation mechanisms, limitations, and recent technological advancements.
# Continuous-Service M/M/1 Queuing Systems
###### Abstract  
In this paper, we look into a novel notion of the standard M/M/1 queueing system. In our study, we assume that there is a single server and that there are two types of customers: real and imaginary customers. Real customers are regular customers arriving into our queueing system in accordance with a Poisson process. There exist infinitely many imaginary customers residing in the system. Real customers have service priority over imaginary customers. Thus, the server always serves real (regular) customers one by one if there are real customers present in the system. After serving all real customers, the server immediately serves, one at a time, imaginary customers residing in the system. A newly arriving real customer presumably does not preempt the service of an imaginary customer and hence must wait in the queue for their service. The server immediately serves a waiting real customer upon service completion of the imaginary customer currently under service. All service times are identically, independently, and exponentially distributed. Since our systems are characterized by continuous service by the server, we dub our systems continuous-service M/M/1 queueing systems. We conduct the steady-state analysis and determine common performance measures of our systems. In addition, we carry out simulation experiments to verify our results. We compare our results to that of the standard M/M/1 queueing system, and draw interesting conclusions.  
queueing systems; simulation; operations research; optimization Article
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### Road Segmentation
#### 3.3.2 Distance Limitation  
A trimming function was used to remove irrelevant areas far from the road. Parcel sections extending 100 m from the road path were discarded. This eliminated unnecessary features like highway service areas or adjacent land uses that were not relevant to the road.
## 2 Routing Protocol for LLNs
### Node Rank  
A node's rank (\(=\)the distance vector) is \"a scalar representation of the location or radius of a node within a DODAG Version\" [4] (p. 20). Along an end-to-end path to the root, ranks must decrease monotonically. This enables routing loop avoidance [15] (Section 4.1). Loop detection (\(\
eq\)avoidance) is in turn facilitated by RPL Packet Information (RPI) contained in (nearly) all data packets [4] (Section 11.2).
## 4 Experiment and Analysis
### Point-Surface Fusion Results of NMRI
In the point-surface fusion process, the grid data of latitude and longitude have the same interval and a fixed range, so it is easier for input variables with the same latitude and longitude to obtain the same prediction value, resulting in a blocky boundary similar to the distribution of the longitude and latitude in the fusion results. By analyzing the importance of the model variables, we find that, in the RF regression model, the importance of latitude and longitude ranks in the top three among all the predictive variables (Figure 10), indicating that the model is too sensitive to the longitude and latitude variables. To conclude, although the blocky effect exists in the fusion results, the overall accuracy and the trends of spatial distribution of the results will not be affected. After fusing the site-level NMRI product and optical remote sensing VWC-related indices using machine learning methods, the spatial limitations of the original NMRI product can be compensated.
## 4 Results and Discussion
### Scenario 1: Disturbance Rejection  
In the disturbance rejection test, a change in the initial operating conditions was induced, and the controllers restored the process. If the manipulated variables deviate from their nominal operating levels, a good control system will exert minimum effort to compensate for the effects of any given disturbance. A flowrate disturbance was caused to the process where the flue gas flow rate was reduced by 50% to 162,565 kg/h. The CO\({}_{2}\) stream maintained purity of >98%, the temperature was the parameter with the most extreme reaction where it dropped to 8 \({}^{\circ}\)C for around 2 min before returning to the initial set point temperature as the condenser duty adjusted to the new reduced flow rate. The pressure was well maintained around the set point value with a slight increase to reach 1.97 bar. The desorber behavior to the disturbance is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Controller**} & \multirow{2}{*}{**PV**} & \multirow{2}{*}{**OP**} & \multicolumn{2}{c}{**Variable Range**} & \multirow{2}{*}{**Gain (\%/\%)**} & \multirow{2}{*}{**Integral Time (min)**} \\ \cline{5-6}  & & & & & **Min** & & **Max** \\ \hline \multirow{4}{*}{Absorber} & PC & Flue gas pressure, bar & Flue gas flow rate & 0 & 3.6 & 20 & 12 \\  & Sum LC & Sump height, m & Rich amine flow rate & 0 & 7.35 & 0.187 & 85 \\  & Capture & CO\({}_{2}\) exit flow rate, kg/h & Lean amine flow rate & 0 & 14,711 & 0.635 & 2.17 \\ \hline \multirow{4}{*}{Stripper} & PC & Exit stream pressure, bar & CO\({}_{2}\) stream flow rate & 0 & 4 & 20 & 12 \\  & Sump LC & Sump height, m & Lean amine flow rate & 0 & 6.16 & 0.136 & 34.58 \\ \cline{1-1}  & TC reverse & CO\({}_{2}\) stream temperature, \({}^{\circ}\)C & Condenser duty & 0 & 49.95 & 0.18 & 0.999 \\ \cline{1-1}  & QC reverse & Stripper temperature, \({}^{\circ}\)C & Reboiler duty & 0 & 222.5 & 61.30 & 1.998 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tuned controller parameters.  
Such a large drop in the flow rate is not so common in a steady-state mode, where the control system should quickly reject any disturbances. Nevertheless, we are interested in the dynamic operation of the plant, and the 50% reduction represents part-load operation, which would occur if we were to integrate the plant with a renewable-fed electrolyzer. Since the plant provides feedstock for a desired synthetic fuel, if the demand for the fuel is to decrease at some time interval, then the prior stages (carbon capture, electrolysis, and reactors) need to decrease the production. This decrease maintains a profitable operation given there is no planned storage for the raw material.
# UAV Formation Shape Control via Decentralized Markov Decision Processes
## 4 UAV Motion Model
#### UAV Motion Control  
We implement a linear controller [36] to produce the appropriate torque and thrust in order to drive the UAV to the desired state in SO(3), governed by the optimized waypoints. The Figure 1 shows how the waypoints generator works with the controller. We make the following assumptions for the linear controller.  
* We linearize the trigonometric functions assuming roll angle \(\phi\) and pitch angle \(\theta\) small enough, i.e., \(\cos\phi=1\), \(\sin\phi=\phi\), \(\cos\theta=1\), \(\sin\theta=\theta\)
* The angular velocity of the UAV is also considered small enough  
The linear controller is described extensively in [37; 38]. The control problem is to calculate the inputs \(u_{1}=\sum_{i=1}^{4}F_{i}\) and \(u_{2}\) required to track a set of waypoints \(r_{k}^{w}\). The input \(u_{2}\) is given by the following equation.  
\[u_{2}=\begin{bmatrix}0&L&0&-L\\ -L&0&L&0\\ \gamma&\gamma&\gamma&\gamma\end{bmatrix}\begin{bmatrix}F_{1}\\ F_{2}\\ F_{3}\\ F_{4}\end{bmatrix}\]  
where, \([F_{1},F_{2},F_{3},F_{4}]\) are propeller forces and \(\gamma\) is the drag coefficient.  
**Position control.** The position control method use the bank and the pitch angles as inputs to drive the position of the UAV. The position controller determines the desired bank angle \(\phi^{des}\) and desired pitch angle \(\theta^{des}\). The desired bank and pitch angles are used to calculate the desired speed of the UAV [37].
## 3 Results  
### DOC and Bacteria in Cultivation Pond  
The dynamics of _T. striata_ cell density and DOC concentration in the cultivation pond sampled are shown in Figure 3. The cell density of _T. striata_ increased after initial inoculation of _T. striata_ with the maximum cell density (\(1.8\times 10^{5}\) cells\(\cdot\)mL\({}^{-1}\)) measured on day 5, and then gradually decreased until day 9. The DOC concentration rapidly increased from day 2 with the maximum DOC concentration (428.7 mg\(\cdot\)L\({}^{-1}\)) measured on day 3 and then a gradual decrease until day 7. The DOC concentration within the natural seawater also increased and decreased from day 2 to day 7, but its maximum concentration (24.7 mg\(\cdot\)L\({}^{-1}\)) was significantly lower than that of the samples from the cultivation pond.  
The bacterial community within the cultivation pond was substantially different to the natural seawater community (Figure 4). The Planctomycetaceae bacterial group dominated at day 0 and maintained its dominance (29.4%) in natural seawater until day 9. On the other hand, the Rhodobacteraceae and Saprospiraceae dominated the cultivation pond on day 3 and day 9. The maximum percentage contributions of the Rhodobacteraceae group and Saprospiraceae group were 37.1% and 26.5%, respectively, on day 3 in the cultivation pond.  
Figure 4: Changes in bacterial composition in cultivation pond and near (3 m away) seawater for nine days.  
Figure 3: Dynamics of abundance of _Tetraselmis striata_ cells cultured in cultivation pond, as well as concentrations of dissolved organic carbon (DOC) in cultivation pond and near (3 m away) seawater for nine days.
## 5 Discussion
### Limitations and Scope for Future Works  
Although this study provides valuable insights into the use of AI methods for predicting the CBR of soil-alum sludge mixtures, there are limitations that need to be addressed in future work. Firstly, the study only considered nine input parameters, and there may be other important parameters that were not included. Therefore, future research could explore the effect of additional parameters, such as the pH value of the sludge and the particle size distribution of the soil, on the CBR prediction accuracy.  
Secondly, the study only used a limited number of CBR test results, which may not be representative of all possible soil-alum sludge mixtures. Thus, future work could expand the database to include more CBR test results, from a wider range of soil and sludge combinations, to further evaluate the accuracy and robustness of the AI models.  
Thirdly, while the study evaluated the sensitivity and importance of the input parameters, it did not consider the interaction effects between the parameters. Therefore, future work could investigate the interaction effects, to provide a more comprehensive understanding of the CBR prediction process.  
Fourthly, while the AI models used in this study can accurately predict CBR values, they do not provide physical insights into the underlying mechanisms. Understanding the physical processes that govern the strength and stability of soil-alum sludge mixtures, could help to improve the accuracy and reliability of the AI models. Therefore, future work could combine AI methods with physical modeling approaches, to gain a deeper understanding of the system's behavior.  
Finally, the study focused on predicting the CBR of soil-alum sludge mixtures for geotechnical engineering applications. However, the use of alum sludge has potential applications in other fields, such as agriculture and water treatment. Thus, future work could explore the use of AI methods to predict other parameters relevant to these fields, such as soil pH and nutrient availability.