esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Dynamic Scheduling Approaches  
While the formulations and models described in the last subsection can derive optimal schedules for a provided system, weather dynamics require dynamic approaches to cope with the associated uncertainties. Several authors provide a review of current methods for the dynamic scheduling in production environments, e.g., [32; 33; 34]. Therefore, they provide a more or less common classification of these approaches, which is summarized below:  
**Reactive and Continuous Approaches:**: Reactive approaches generally do not incorporate uncertainties into the initial planning but rely on so-called recourse actions in cases of plan failures, i.e., a rescheduling. Therefore, some authors distinguish between approaches that only react to disturbances and between approaches that perform a continuous rescheduling. The first class of reactive approaches mostly focuses on a reduction of rescheduling times, e.g., by only selecting affected operations or by applying dispatching rules. The second class often uses receding horizon techniques (e.g., variations of the Model Predictive Control) and combines these with state-space descriptions to avoid expensive optimizations.
**Preventive (Robust and Stochastic) Approaches:**: Preventive approaches try to provide schedules that can cope with the dynamics of the system in general. At this, the plans are generally designed to either focus on the worst possible case (robust plans) or to provide alternatives for possible scenarios using stochastic programming techniques. In general, robust plans tend to be too conservative as they usually assume the worst case. In contrast, stochastic plans come at very high computational costs, as the solver needs to compute alternative plans for different probable cases in advance.
**Predictive-Reactive Approaches:**: This class of approaches tries to assume the extent of disturbances during the initial planning stage and employ reactive recourse actions if additional disturbances occur. [PERSON] and [PERSON] provide the example of fuzzy-based approaches, which translate uncertainties into a fuzzy description of the resulting variables. These descriptions are then used to derive schedules [34]. This type of reformulation enables a reduction of the models' complexity as done for robust scheduling approaches, but does not limit the results to the worst case. [PERSON] and [PERSON] provide an overview of approaches, which use a bi-objective formulation, minimizing the makespan, and the impact of disturbances on the overall plan [33].
## 1 Introduction
The contribution of our work is as follows:  
* Considering the load and platform operation of satellites in reality, we establish a mathematical model of satellite scheduling with complex constraints, and the related mathematical statement is given. * We propose the real-coding Population-Based Incremental Learning (PBIL) algorithm to solve the MSSP. Compared to the traditional PBIL algorithm with binary coding, the real-coding format can greatly shorten the coding length so that the computational efficiency is improved. Additionally, we design the value probability matrix, correction coefficient and mutation operator to guide better evolution and avoid early convergence. * We conduct some numerical simulations and analyses to verify the real-coding PBIL algorithm for the MSSP. The performance of the algorithm is analyzed by comparing it with binary-coding PBIL and Genetic Algorithm (GA). Additionally, the influence of key parameters on algorithm performance, such as probability correction coefficient, is analyzed. We note that a shorter conference version of this paper appeared in _Wireless and Satellite Systems-11 th EAI International Conference_ (WiSATS 2020) [14]. Our initial conference paper neglected to provide the mathematical expressions of constraints in the multi-satellite scheduling model and did not address the influence of key parameters or coding formats on the performance of the PBIL algorithm. This manuscript addresses these issues and provides additional comparisons and analyses to verify the efficiency and quality of algorithms for multi-satellite scheduling in our study. The organization of this paper is as follows: Section 2 provides a mathematical model of MSSP and gives the related mathematical statement of constraints; Section 3 proposesthe real-coding PBIL algorithm for satellite schedules and explains a detailed framework of our method; the simulation details and results are elaborated on and analyzed in Section 4; lastly, the conclusion is presented in Section 5.
# Cause Analysis of Pier Inclination of An Expressway Interchange Bridge: Case Study
## 5 Numerical Analysis
### Pier Deformation Considering Support Slipping  
A contact analysis model was developed using Abaqus and was used to conduct a contact analysis of support friction to determine the influence of the frictional force of the support on high-pier inclination.  
The Abaqus solid model of Piers P30, P33 and P35 was established, including piers, caps, and the top and bottom plates of supports (see in Figure 16). Hard contact between top and bottom plate was defined to simulate the friction under support slip, and the top plate of the support exerted the superstructure reaction force. The friction coefficient of the support refers to the JT 391(friction resistance \(f=\mu\)R, where \(\mu=0.03\) and R is the supporting reaction force).  
In the situation that the concrete girder is constructed on one side of a pier while the steel girder is not, under the conditions of the dead load on Pier P30, the longitudinal inclination at the top of the pier is \(-28\) mm considering support friction resistance, while the displacement is \(-70\) mm without considering this, as shown in Figure 17. Therefore, the deformation of Pier P30 caused by support friction resistance is 42 mm, in the direction opposite to pier deformation. It is worth noting that the deformation caused by support friction in the subsequent stages is stable. Similarly, the deformation values caused by the support friction resistance of Piers P33 and P35 are 41 mm and 20 mm, respectively. Therefore, the influence of support friction on pier inclination is not negligible when the support slips obviously, especially in high piers.  
Figure 16: Abaqus contact analysis model.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Location** & **27\#S** & **27\#L** & **30\#S** & **30\#L** & **33\#S** & **33\#L** & **35\#S** & **35\#L** \\ \hline Temperature & \(5\) & \(-5\) & \(4\) & \(-10\) & \(3\) & \(-2\) & \(3\) & \(-3\) \\ \hline Shrinkage/creep & \(-13\) & \(13\) & \(-11\) & \(4\) & \(-1\) & \(14\) & \(-4\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Displacement of girder end under temperature change (rise of \(10\)\({}^{\circ}\)C) and shrinkage/creep of concrete (mm).
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 4 Performance Evaluation
### Average Path Length Evaluation  
In this test, network performance is investigated through different link successful transmission rates. In particular, link successful transmission rate among nodes varies between values 0.3, 0.5, and 0.7. In other words, the higher successful transmission rate a link has, the higher child-to-parent accessibility is provided, and the fewer number of efforts are required for data transmission. Consequently, as the successful transmission rate of a link reduces, the number of efforts made by a typical child in order to connect to its corresponding parent increases. This leads the connection and communication between child and parent to be more problematic; all of which can culminate in either link failure or consecutive  
Figure 13: The results of energy consumption evaluation, while network traffic is set to 5, 10, and 15 packets per minute.  
child parent session failures. In this test, which is known as the average path length test, the average number of traversed hops between the leaf node and the root node is indicated.  
Since the primitive criterion for network graph formation in the basic RPL approach is defined as the hop-count distance from the sink node, the values of average path length in this approach is lower than other approaches. In BD-RPL, similar to our proposed method, the number of hop-counts to the root node has increased, in that nodes' degrees have been restricted. Through the utilization of a combinational objective function as well as the consideration of hop-count parameter in graph formation, the tree height in M-RPL and A-RPL approach has been decreased. In the proposed method of this paper, as a result of degree restriction, the network tree height has been increased. As a result, the more successful transmission rate of link increases, the less average path length; as illustrated in Figure 15.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 4 Additional Regulations by the German National Metrology Institute
### Requirements for Processing Meter Measurements  
As opposed to the BSI technical guideline, which is partially open to interpretation with respect to measurement resolutions, the _PTB-A 50.8_ clearly states at which temporal resolution meter measurements have to be collected for billing-relevant purposes. The relevant requirements are as follows [18]:  
1. In the case of a unidirectional communication of the meter measurements in the LMN, the SMGW must promptly receive and immediately process the data.
2. For communicating measurements in a bidirectional fashion, the SMGW must collect the data exactly every quarter hour for electricity and every hour for gas.
3. To allow a comparison of the meter readings registered by the gateway and the readings display on the actual meter, the SMGW must be able to receive (unidirectional case) or request (bidirectional case) the latest measurements rapidly and repeatedly, as often as the meter and the transport channel allow. The information is accessible via the _Consumer Interface_.  
Even though DA6.1 states that the measurements need to be collected rapidly and as often as possible when requested via the _Consumer Interface_, the _PTB-A50.8_ adds a displaying interval to the _Meter Profile_ which configures the interval for updating the stored measurements while accessed by the consumer.
## 3 Methodology  
### The Pipeline of Proposed Algorithm  
The algorithm proposed in this paper consists of three key components:  
* **Scene Decomposer**: The first component uses the COS prompting method to decompose the input problem into a list of scenes, each represented as a text closure. This method is detailed in Section 3.2.1 and constitutes one of the primary contributions of our algorithm.
* **Relation Extractor**: The second component uses the Scene-Aware \(S^{2}\) method to extract a relation set from each scene, producing a list of relation sets. This method is another major contribution and is discussed in Section 3.3.1.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Name** & **Year** & **Approach** & **LLM** & **Symbolic Solver** \\ \hline ZhangAlg [31] & 2022 & Sequence-to-Sequence & No & No \\ JieAlg [28] & 2022 & Sequence-to-Sequence & No & No \\ YuAlg [17] & 2023 & Relation-Flow & No & Yes \\ LiuAlg [14] & 2023 & Prompting-LLM & Yes & No \\ YasuAlg [32] & 2024 & Prompting-LLM & Yes & No \\ Our Work & 2024 & Prompting-LLM + Relation-Flow & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the characteristics of related algorithms.  
* **Symbolic Solver**: The third component derives the final solution by reasoning through the list of relation sets. This component primarily enhances the symbolic solver proposed by [17], adapting it for use in our context.  
An overview of our algorithm is shown in Figure 1. In this figure, \(S_{i}\) denotes the \(i\)-th scenes decomposed by the LLM, and \(R_{i}\) and \(A_{i}\) denote the relation set and the solution acquired from \(S_{i}\), respectively. The algorithm operates as described in Algorithm 1. To implement the tasks of the algorithm, three procedures are employed: Procedure 1 for the scene decomposer, Procedure 2 for the relation extractor, and Procedure 3 for the symbolic solver.  
```
Input: A problem text \(T\) Output: A solution \(A\)
1 Step 1: Scene Decomposer: Use Procedure 1 to implement the COS method to acquire a list of scenes \(S\) from \(T\);
2 Step 2: Relation Extractor: Use Procedure 2 to implement the Scene-Aware \(S^{2}\) method to acquire a list of relation sets \(\mathcal{R}\) from \(S\);
3 Step 3: Symbolic Solver: Use procedure 3 to reason a list of relation sets \(\mathcal{R}\) and acquire a solution \(A\).
```  
**Algorithm 1**The algorithm for solving AWPs.
# Optimized Dissolved Oxygen Fuzzy Control for Recombinant _Escherichia coli_ Cultivations
###### Abstract  
Due to low oxygen solubility and mechanical stirring limitations of a bioreactor, ensuring an adequate oxygen supply during a recombinant _Escherichia coli_ cultivation is a major challenge in process control. Under the light of this fact, a fuzzy dissolved oxygen controller was developed, taking into account a decision tree algorithm presented in the literature, and implemented in the supervision software SUPERSYS_HCDC. The algorithm was coded in MATLAB with its membership function parameters determined using an Adaptive Network-Based Fuzzy Inference System tool. The controller was composed of three independent fuzzy inference systems: Princ1 and Princ2 assessed whether there would be an increment or a reduction in air and oxygen flow rates (respectively), whilst Delta estimated the size of these variations. To test the controller, simulations with a neural network model and _E. coli_ cultivations were conducted. The fuzzification of the decision tree was successful, resulting in smoothing of air and oxygen flow rates and, hence, in an attenuation of dissolved oxygen oscillations. Statistically, the average standard deviation of the fuzzy controller was 2.45 times lower than the decision tree (9.48%). Results point toward an increase in the flow meter lifespan and a possible reduction of the metabolic stress suffered by _E. coli_ during the cultivation.  
adaptive network-based fuzzy inference system; dissolved oxygen; fuzzy control; recombinant _Escherichia coli_; SUPERSYS_HCDC
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 2 Materials and Methods
### Media Composition and Fermentation Conditions  
Enzyme production was carried out under submerged fermentation using minimal medium with the following composition (g/L): 0.1% KCl, 0.1% NaNO\({}_{3}\), 0.1% K\({}_{2}\)HPO\({}_{4}\), 0.05% MgSO\({}_{4}\), 0.05% yeast extract, supplemented with 1% carboxymethyl cellulose (CMC) (Merck, Modderfontein, South Africa) or beechwood xylan (ROTH, Cape Town, South Africa) as carbon source for cellulase and xylanase production, respectively. The initial pH of the medium was adjusted to 7.0 and sterilized by autoclaving at 121 \({}^{\circ}\)C for 15 min [1]. An inoculum was prepared by placing two loopful of the bacterial cells in a 25 mL of minimal medium, and incubated at 30 \({}^{\circ}\)C for 48 h. Subsequently, 1 mL of preculture adjusted to 0.1 OD\({}_{600}\) was used to inoculate 100 mL of production medium and incubated in an orbital shaker, at 140 rpm, 30 \({}^{\circ}\)C for 72 h, along with control culture of an uninoculated 100 mL fermentation broth. Thereafter, the fermentation broth was then centrifuged at 15,000\(\times\)\(g\) for 15 min at 4 \({}^{\circ}\)C and the supernatant obtained served as crude extracellular enzyme solution [26].
###### Abstract  
The frequency diverse array (FDA) is an architecture capable of beamforming in both range and angle, improving upon the traditional phased array (PA) which can only achieve beamforming in angle. The FDA employing directional modulation (DM) for secure directional communications (SDC) can reduce bit error rates (BERs) in both range and angle, again improving upon the traditional PA which can only reduce BER in angle. In this paper, we document the challenges involved in the design and implementation of a two-element linear FDA employing fast-time binary phase-shift keying (BPSK) modulations. We also show that the experimentally collected field data match well with the results of simulations based on our analytical model.  
f (FDA); binary phase-shift keying (BPSK); secure directional communications (SDCs); Universal Software Radio Peripheral (USRP); physical layer security +
Footnote †: journal: Sensors  
202525193
## 1 Introduction
(2) It constructs a spectrum allocation model based on incomplete information games: Through the framework of incomplete information games, this paper proposes a novel spectrum allocation model that adapts to complex electromagnetic environments and changing game dynamics, providing theoretical support for multi-agent collaborative decision-making. (3) It verifies the feasibility and effectiveness of the MCCFR-based multi-agent hybrid game spectrum allocation module; simulation results show that the probability of interference for the frequency being affected is 32.5%. The probability of successful interference from the interfering party is 37.3%. This verifies the feasibility and effectiveness of the multi-agent hybrid game spectrum allocation module based on the Monte Carlo Counterfactual Regret Minimization (MCCFR) algorithm. The organization of this paper is as follows: Section 2 provides an overview of the primary methods used in this study and constructs a spectrum allocation model based on incomplete information games. Section 3 employs the proposed spectrum allocation model in game-theoretic adversarial scenarios to verify and analyze the feasibility and effectiveness of the multi-agent hybrid game spectrum allocation module based on the MCCFR algorithm. Section 4 presents the conclusion of the paper.
# Phytochemical, Antimicrobial and Cytotoxic Activities of Gaultheria Trichophylla [PERSON]
## 3 Results
### Antibacterial Assay  
The antibacterial activity was performed for various extracts of _G. trichophylla_ to check out the zone of inhibition (Table 6). Gram positive bacteria such as _Bacillus subtilis_ was inhibited by MIC value of GTME (15 mg/mL), GTEE (7.5 mg/mL), and GTEE (15 mg/mL), respectively. MIC values for _Staphylococcus aureus_ include 7.5 mg/mL for methanolic and other extracts. In case of Gram-negative bacteria GTEE, GTEE, GTEE, and GTEE (7.5 mg/mL) showed MIC against _Escherichia coli_; however, GTME and GTEE inhibited the growth of _Escherichia coli_ with MIC 15 mg/mL and 30 mg/mL. The bacterial strain _Pseudomonas aeruginosa_ was inhibited by 7.5 mg/mL of GTME, GTEE, GTEE, GTEE, GTEE, and GTEE; however, no inhibition was recorded, for the extracts were symbolized with ND (not detected) against the said strains. Figure 4 shows the zone inhibition recorded for the tested extract against the bacterial strain used. Figure 4: _Gaultheria trichophylla_ (**A**). Growing in its natural habitat (**B**), and (**C**) Clear zone of inhibition of antibacterial activities. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**\% Inhibition of Various Fungal Species**} \\ \hline
**Treatment** & _Helmithosporiumsolani_ & _Fusarium solani_ & _Aspergillus flavus_ & _Aspergillus fumigatus_ \\ \hline Control & 86.33 \(\pm\) 0.88 \({}^{\rm a}\) & 77.3 \(\pm\) 1.2 \({}^{\rm a}\) & 84 \(\pm\) 1.52 \({}^{\rm a}\) & 86.66 \(\pm\) 0.09 \({}^{\rm a}\) \\ GTME & 71 \(\pm\) 2.87 \({}^{\rm b}\) & 58 \(\pm\) 0.57 \({}^{\rm b}\) & 78 \(\pm\) 2.886 \({}^{\rm b}\) & 59 \(\pm\) 5.19 \({}^{\rm b}\) \\ GTEE & 48.5 \(\pm\) 3.75 \({}^{\rm d}\) & 70 \(\pm\) 1.154 \({}^{\rm ab}\) & 35 \(\pm\) 4.04 \({}^{\rm c}\) & 53.5 \(\pm\) 3.17 \({}^{\rm c}\) \\ GTEE & 57 \(\pm\) 2.89 \({}^{\rm c}\) & 67.5 \(\pm\) 0.29 \({}^{\rm b}\) & 37 \(\pm\) 0.577 \({}^{\rm c}\) & 44 \(\pm\) 1.54 \({}^{\rm d}\) \\ GTEE & 58.5 \(\pm\) 0.29 \({}^{\rm c}\) & 72.5 \(\pm\) 2.59 \({}^{\rm ab}\) & 29 \(\pm\) 1.154 \({}^{\rm d}\) & 61.5 \(\pm\) 0.29 \({}^{\rm b}\) \\ GTEE & 53 \(\pm\) 4.04 \({}^{\rm cd}\) & 65.5 \(\pm\) 3.75 \({}^{\rm b}\) & 37 \(\pm\) 0.577 \({}^{\rm c}\) & 55 \(\pm\) 0.58 \({}^{\rm c}\) \\ GTME & 68 \(\pm\) 4.04 \({}^{\rm b}\) & 72 \(\pm\) 0.57 \({}^{\rm ab}\) & 30.5 \(\pm\) 1.44 \({}^{\rm cd}\) & 53 \(\pm\) 4.04 \({}^{\rm c}\) \\ \hline \hline \end{tabular}  
* **Key:** Means significantly different from one another have different English alphabets while those not significantly different from one another share the same English alphabets at HSD (2.45) and \(p\) < 0.05.  
\end{table} 
Table 5: Results of antifungal activities for different extracts of _Gaultheria trichophylla_.
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 3 Overview of the Discrete Analog Functional Blocks of ArduSiPM
### Communication Interfaces  
Fast communication interfaces are essential for such high-speed detectors to timely transfer the measured data to an external computer for postprocessing, storage, or data transmission (for example, for underground data transfer in satellite). Cosmo ArduSiPM takes advantage of copious peripherals available on SoC to offer several industrial high-speed communication interfaces like CANBUS, USB 2.0, UART, and I2C. The Universal Serial Bus (USB) protocol allows communication rates of up to 480 Mbps and enables interfacing with other computers or a wide variety of peripherals. Moreover, there is also a possibility to install a Wi-Fi module to introduce a wireless radio interface to create a network of multiple Cosmic ArduSiPM modules over a large geographical area.  
Figure 5: In blue, the pulse from a SiPM Hamamatsu 13360-1325 CS acquired with the oscilloscope (20 mv and 20 ns per div.) from the analog output of Cosmo ArduSiPM. We measure a 7 ns delay between the SiPM pulse and the output of the comparator (TTL_out, green). The multiple peaks are due to cable reflections.
## 5 Sensor Faults and Outlier Detection Models for IoTs
### Comparison of Fault and Outlier Detection Techniques for IoTs
#### 5.6.4 Cluster Techniques  
**Pros:**  
* Once the clusters and new data points inserted into the system and tested for sensor faults and outliers, the model can easily be made to be adaptable to an incremental form.
* No supervision is needed.
* Very much suited for detecting sensor anomaly from the IoTs temporal data.
**Cons:**  
* Is very computationally costly when working on multivariate sensor data for fault detection.
* Due to the models' high computational cost, it is unsuitable for inadequately resourced sensors.
* Cannot cope with any changes in the IoT data.