esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Physical Shape Recovery Test  
Shape recovery speed is another key property of shape memory polymers, which refers to the time for a deformed SMP sample to recover their original shape. The recovery or actuation times for SMPs are in the range of about one minute to over one hundred minutes [27], much longer than those of shape memory alloys. For most applications of SMPs, shape recovery should be as quick as possible, so that the SMPs can be used to develop microactuators and devices which can be activated easily by external heat or light in the shortest time. For example, SMPs with high speed and low \(T_{\text{g}}\) have good potentials for medical applications such as drug delivery systems, polymer-based stents and micro-surgery tools or sutures. In this study, the physical shape recovery tests were performed to study the recovery behavior and recovery speed of the SMPUs. The samples with a thickness of 0.5 mm, a width of 3 mm and a length of \(\sim\)70 mm, were used for the experiments with one example shown in Figure 10. The SMPU sample was initially stretched to 100% at 60 \({}^{\circ}\)C (\(>\)\(T_{\text{g}}\)), and the deformed shape was fixed by cooling it to room temperature (\(\sim\)20 \({}^{\circ}\)C). The sample was then put on a hot plate set at 60 \({}^{\circ}\)C and its subsequent shape changes over time were recorded.  
It was found that the PEG-SMPU samples start to shrink (recovering the original state) instantly once they are put on the hotplate. The PEG-H3 samples show about 90% recovery in less than 10 s, and a full recovery process is completed within \(\sim\)20 s, demonstrating an excellent actuation capability. This is significantly different from those of the PCL-SMPUs, all of which show an actuation  
Figure 10: Shape recovery of the 100% extended PEG-H3 at 60 \({}^{\circ}\)C. The shape recovery takes place immediately when heated, and completes the process within 20 s.  
time \(>\)50 s [20], though they were in a roll form which may have a non-uniform temperature distribution. An example of the PCL-S3 sample (100% extension) is shown in Figure 11. It shows that the sample takes a long time to recover, with \(<\)80% recovery after 50 s. The results clearly demonstrate that the high molecular weight PEG-SMPUs have a much higher actuation speed as compared to those of the PCL-SMPUs, and may be a good choice for polymer-based microactuator applications [31]. Further work is needed to investigate SMPUs with other high molecular weight polyols.
# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
###### Abstract
The rest of the paper is structured as follows. Section 2 reviews related work in FL, focusing on client selection strategies for dynamic IoT environments. Section 3 introduces the system models, including mobility-aware and resource-aware considerations, and formulates the client selection problem. Section 4 details the MACS algorithm, explaining its mobility prediction, resource-aware client evaluation, and dynamic resource allocation. Section 5 presents simulation results, comparing MACS with baseline methods. Finally, Section 6 concludes the paper and discusses future research directions.
# GNSS-Based Vertically Monitoring of Super-Tall Buildings
## 2 Calculation of the Monitoring Point's Coordinate Time Series
### Criteria of Eliminating the Gross Errors
\tag{7}\]  
where \(\sigma_{i}\) is the precision of the baseline vectors.
# Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems
## 5 Performance Measurements and Wireless Tests of the Link Adaptation
### Simulation
Since the curves on the left half of Figure 18 are shifted right or left individually, it is hard to compensate for the errors even with the SNR offsets, in which case the impact of the MMI table errors will be severe. Hereafter, the simulation environment is set up assuming that the MCS is determined by using the left half of Figure 18.  
The throughput gain of APBLA versus ARF for a 1-tap channel is plotted in Figure 19 as a function of the ARF packet transmission interval. As Doppler frequency grows from 2.5 Hz to 10 Hz, the throughput gain curves are shifted left, from which it follows that the APBLA-vs-ARF throughput gain for a 1-tap channel is up to 46% over 10 m-250 m of the  
Figure 17: APBLA vs. ARF throughput simulation results. Figure 18: Shifted (**left**) and standard (**right**) MMI-to-throughput tables. normalized Doppler frequency (which is the product of Doppler frequency and the packet transmission interval). The throughput gain of APBLA relative to ARF is plotted in Figure 20 for a 3-tap channel (where it is assumed that the first three taps have a uniform power-delay profile) as a function of the ARF packet transmission interval. The gain has significantly reduced and the range that the gain can be acquired has also reduced, compared with that for the 1-tap channel, but the general property that the throughput gain is limited to a specific range of the normalized Doppler frequency is maintained. Similarly to the 1-tap channel case, as Doppler frequency grows, the range where the gain of APBLA over ARF occurs is shifted to the left. The gain of APBLA over ARF for a 3-tap channel manifests itself up to 15% over 16 m-160 m of the normalized Doppler frequency. Figure 19: Throughput gain of APBLA relative to ARF as a function of the packet transmission interval over some Doppler frequencies (1-tap channel). Figure 20: Throughput gain of APBLA relative to ARF as a function of the packet transmission interval over some Doppler frequencies (3-tap channel). In turn, given that the packet transmission interval is fixed and Doppler frequency is varied, the throughput gain of APBLA against ARF is plotted in Figure 21, which exhibits to what degree the transmission interval should be, according to Doppler frequency, when a throughput gain is expected. To summarize, with a 1-tap (3-tap) channel, the throughput gain of APBLA vs. ARF is at its maximum of 46% (15%) when the normalized Doppler frequency range is about 10 m-250 m (16 m-160 m). When the packet transmission interval is less than 1 ms, the throughput gain of APBLA over ARF can be expected only if Doppler frequency is over 100 Hz-1000 Hz. The throughput gain of APBLA relative to ARF can be maximized if the normalized Doppler frequency is about 50 m and the packet transmission interval is greater than 1 ms.
# Querying and Extracting Timeline Information from Road Traffic Sensor Data
## 3 Timeline Modeling
### Timeline Model  
With previous definitions, we provide a formal definition of a timeline model, which is the main concept of the QET system in Definition 7. Intuitively, we try to connect a list of extracted congestions based on the start time and the affectedness. Thus, the timeline model enables us to describe the chronology and effects of congestion events.  
**Definition 7**: _(Timeline model) A timeline model from raw traffic data is a set of \(TM=\{tl_{i},tl_{2},\ldots,tl_{n}\}\), where each \(tl_{i}\) is represented by a pair of \((\)congestion list, event list\()\) such that:_  
* _a \"congestion list\" is a sorted list of tuples_ \((c_{i},\)__\(p_{affectedby},\)__\(p_{affecting},\)__\(dur,\)__\(len)\) _according to the start time of_ \(c_{i}\)__\((c_{i}.t_{start})\)_;_
* _an \"event list\" is a sorted list of congestion events_ \(e_{i,type}\) _according to the time_ \(e_{i,type}.t\)_._  
Figure 6: An example of congestion dependency.  
The congestion list is ordered according to the start time of \(c_{i}\) (denoted by \(c_{i}\)_-\(t_{start}\)), and its elements are tuples of (\(c_{i}\), \(p_{affectedby}\), \(p_{aftecting}\), \(dur\), \(len\)). \(c_{i}\) denotes congestion defined in Definition 4. \(p_{affectedby}\) and \(p_{affecting}\) are pointers representing the affected/affecting congestion in Definition 6. A \(p_{affectedby}\) pointer links to the source of congestion, and a \(p_{affecting}\) pointer refers to the next congestion that occurs due to this congestion. A value of \(dur\) is calculated by subtracting \(c_{i}\).\(t_{start}\) from \(c_{i}\).\(t_{end}\), and a value of \(len\) represents the total length of the congestion. An element of an event list is a congestion event \(e_{i,type}\), as defined in Definition 5.  
**Example 5**.: _Figure 7 depicts an example of the timeline model. A congestion list on the right is connected with an event list on the left. Three congestions \(c_{1},c_{2},c_{3}\) are maintained in the congestion list. Because each congestion has a beginning event and an end event, it is connected to, at most, two elements of the event list. For example, congestion \(c_{1}\) is connected to \(e_{1,START}\) and \(e_{1,STOP}\). Congestion \(c_{2}\) is connected to only \(e_{2,START}\), which means the congestion is still in progress. Congestion \(c_{3}\) occurs due to congestion \(c_{1}\). Thus, the \(p_{affecting}\) pointer of \(c_{1}\) is connected to \(c_{3}\), and the \(p_{affectely}\) pointer of \(c_{3}\) is connected to \(c_{1}\)._
## 4 Simulation Results
### The Mantuce R3 Model 2
From Figure 4, the results of the improved method with FMU coincide with the results of the reference research completely when the LPM method with 20 nodes is applied. When it comes to applying the LPM method with 100 nodes, the results of both are still highly consistent. What is more, from Table 1, the optimized performance indexes are all the same. Although the proposed method needs extra time to analyze FMU, which is a black-box function, so that the value of FMU can be applied in the DCM as the RHS equation, the total cost time of the CPU of the proposed method and the reference research is of the same order of magnitude. Furthermore, the decrease in nodes in the LPM method has little impact on the result of performance index, but can greatly improve the efficiency of calculation. This property can help us to obtain good initial values or analyze the structure of results quickly. To sum up, the proposed method in this work is not only validated, but also proved to be highly efficient.
# GC- and UHPLC-MS Profiles as a Tool to Valorize the Red Alga Asparagopsis armata+
## 1 Introduction  
Marine organisms live in complex habitats and are often at the mercy of too variable environmental conditions. To protect themselves from predators, competitors, and many other threats, sea creatures were forced to develop protective mechanisms that include producing a vast number of bioactive compounds, many of which are unique in chemical structure and biological function [1, 2]. Seaweeds are among the most explored marine organisms due to their richness in bioactive compounds [3]. However, there are still several species for which the chemical profile is unknown, and _Asparagopsis armata_ is one example.  
_Asparagopsis_ species belong to the phylum Rhodophyta (red seaweeds), order Bonnemaisionales, genus _Asparagopsis_ with three taxonomically accepted species: _A. armata_ [PERSON], _A. sveddii_ [PERSON], and _A. taxiformis_ [PERSON] [4]. _Asparagopsis taxiformis_ has been used as food by Hawaiians [5] while _A. armata_ is commercially farmed in northern Europeto extract bioactive molecules [6]. _Asparagopsis armata_ is native to Australia and is now widely distributed from the North Atlantic to the coast of Senegal and in the Mediterranean basin [7]. This seaweed seems to exhibit an ecological advantage due to its toxic metabolites that affect the competing algae fixation, forming monospecific coverages, reducing the habitat's species richness, and causing an indirect economic impact by negatively affecting fishing and aquaculture [8]. Despite its negative impact, _A. armata_ is commercially farmed in northern Europe to extract bioactive molecules such as sulphated polysaccharides with iodine and bromine groups [6], that are applied as a natural preservative in cosmetics and products to anti-acne treatment (e.g., Invincity(r)). In Azores, this invasive seaweed presents an enormous rate of biomass production for which there is no use. Uses such as food or technological application that increase their commercial value and thus create a dynamic that mitigates the negative effects mentioned above are essential.  
Although several studies have been published on the biological activity of _A. armata_ extracts showing their potential as a source of natural compounds with antiviral, antimicrobial, and antifouling activity [9; 10; 11], there are few published works dedicated to the characterization of secondary metabolites existing in the extracts of this species, apart from sterols and volatile halogenated compounds [12; 13; 14]. Herein we present and discuss the composition of the polar and lipophilic extracts of _A. armata_, obtained by greener and more efficient methodologies and using GC-MS and UHPLC-MS as tools for separation and identification of the metabolites present.
# Fine-Grained High-Utility Dynamic Fingerprinting Extraction for Network Traffic Analysis
## 4 Fine-Grained High-Utility Dynamic Fingerprinting Extraction
### Dynamic Fingerprinting Extraction Algorithm Based on Huf-Tree  
This section mainly proposes a dynamic application fingerprinting extraction algorithm based on Huf-Tree. The Huf algorithm mainly consists of three parts: data preprocessing, building Huf-Tree, and mining high-utility sequence, as shown in Figure 12:  
The specific algorithm is described in Algorithm 1, Algorithm 2 and Algorithm 3:  
Figure 11: Huf-Tree prune. (**a**) Delete nodes. (**b**) Path merge.  
Figure 12: The process of Huf.  
```
Input: network packet \(T_{f}\); Output: Application fingerprint library \(T_{hid}\); (1) IAT generates an application fingerprint: \(T_{f}\to T_{IAT}\) (2) Generate a fingerprint library based on the application fingerprint: \(T_{IAT}\to T_{hid}\) (3) Compute the utility of the messages in \(T_{hid}\): \(u(p_{m}),\varphi(n_{p_{m}},l),\phi\Big{(}n_{d},n_{d}^{p_{m}}\Big{)}\); (4) Calculate the average utility of \(T_{hid}au(s)\).
```  
**Algorithm 1**Data Preprocessing  
```
Input: \(SDB_{1},SDB_{2},\cdots,SDB_{n},\cdots,\epsilon\); Output: Huf-Tree \(T_{huf}\); (1) \(SDB_{1}=\langle p_{l},p_{m},\cdots,p_{n}\rangle\), Generate the utility list: \(L_{T}\); (2) Create ROOT nodes and insert nodes in sequence: \(ROOT\to T_{Huf}\); (3) Insert each fingerprint \(P\): \(p_{l},p_{m},\cdots,p_{n}\to T_{Huf}\); (4) If the new fingerprint coincides with the existing path, it is merged with the existing path \(L_{T}\); (5) Delete nodes whose value is smaller than the threshold and connect their child nodes to ROOT; (6) The duplicate nodes of the ROOT node are merged into path P, and the different nodes become child nodes; Repeat (1)-(6), insert \(SDB_{2},\cdots,SDB_{n}\).
```  
**Algorithm 2**Huf  
```
Input: Huf-Tree \(T_{huf}\), \(L_{T}\), \(\epsilon\); Output: Fingerprint sequence \(F_{p}\); (1) Compute Utility of \(L_{T}\), sort results to generate \(L_{T}^{\prime}\); (2) Keep nodes greater than the threshold: \(au(s)>\epsilon\) (3) Generate an application fingerprint: \(F_{p}=(fp_{1},fp_{2},\cdots,fp_{n})\).
```  
**Algorithm 3**Mining High Utility
# TinyGS vs. SatNOGS: A Comparative Analysis of Open-Source Satellite Ground Station Networks
## 5 Discussion
### Comparison of the Growth in Number of GSs Over Time  
Since, in TinyGS, it is not possible to access the database, there is no API, and only the GSs that were online in the last 24 h are available on the web application, it was not possible to observe the growth in the number of TinyGS GSs over time.  
However, for SatNOGS, this was possible due to the existence of the API. To carry out this study, the SatNOGS network API [10] was used and the \"created\" parameter of each GS was studied, which indicates the date and the time when the GS was created.  
In Figure 14, a quarterly study is presented where it is possible to observe the number of SatNOGS GSs created from 30 September 2015 to 30 June 2023. As one can see, from the last quarter of 2018, the graph has an approximately linear behavior.  
Figure 13: Frequency of SatNOGS operational satellites (according to SatNOGS DataBase) that had at least one data packet received and decoded as of 29 July 2023.  
It is important to clarify that although this graph shows the SatNOGS created up to 30 June 2023, not all of them are currently in operation. Some of them have been offline for more than a year. However, the purpose of this figure is to show that, over time, this project has had more and more members and more people building GSs.
## 2 Materials and Methods
### Sequence Quality Filtering and Data Analysis  
Reads were imported into QIIME2 version 2018.2 [36], demultiplexed and primers were removed using the EMP procedure. Sequences were filtered, dereplicated and denoised, chimeric sequences were removed, and paired using the DADA2 plugin [37] in QIIME2, resulting in amplicon sequence variants (ASVs) of 253 bp in length. ASVs were aligned using MAFFT [38] and used to generate a mid-point rooted phylogenetic tree using FastTree [39]. Taxonomic assignment was performed using the RDP classifier version 2.10.1 [40].  
All statistical analyses were conducted in R version 3.6.2 [41]. The ASV table, taxonomy table, phylogenetic tree and associated metadata were imported into the package Phyloseq version 1.30.0 [42], and any non-bacterial ASVs and those taxonomically affiliated to chloroplast and mitochondrion were removed. The resulting table was rarefied to 15,704 sequences per sample to account for any differences in sequencing depth.  
Observed ASV richness, Shannon and Simpson diversity indices were calculated using Phyloseq and Faith's phylogentic diversity (PD) in the packages picante version 1.8 [43] and btools version 0.0.1 [44]. Significant differences in \(\alpha\)-diversity measures (i.e., variation of species within a sample) between cultivars, rootstocks and their interaction were tested using ANOVA on Aligned Rank Transformed data (i.e., a non-parametric ANOVA) using the package ARTool version 0.10.7 [45]. This method was used because the \(\alpha\)-diversity measures did not meet the assumption of normally distributed and/or homogeneity of variance for a parametric ANOVA. As PD showed the strongest effect of treatment, a post hoc analysis was performed for the rootstock \(\times\) cultivar interaction term using estimated marginal means with the package emmeans version 1.4.5 [46].  
Bacterial community structure (\(\beta\)-diversity, i.e., variation in species between samples) was assessed using principal coordinate analysis (PCoA) based on Bray-Curtis distances, which calculates dissimilarity between samples using relative sequence abundances. To determine potential significant effects of rootstock, cultivar and their interaction, PERMANOVA with 999 permutations was used with the package vegan version 2.5.4 [47]. Bacterial taxonomic composition was evaluated by merging taxa to the phylum level (and summing their abundances), and removing those with an abundance <20. Data were plotted based on relative abundances per rootstock and cultivar combination. To detect bacterial taxa with an affinity towards specific (combinations of) cultivars or rootstocks, indicator species analysis using the package indicspecies [48] was performed using abundance data of taxa merged at the genus level. The analysis was carried out separately for rootstock and cultivar samples and resulted in a list of species associated with individual, or groups of, cultivars or rootstocks, based on calculations that take into account the strength of association and the statistical significance of the relationship between species abundances and treatment groups.
## 1 Introduction
Most of the existing algorithms focus on theoretical optimal solutions or simplified experimental environments, neglecting the complexity and resource constraints of real-world scenarios. In particular, many algorithms only deal with independent course scheduling, overlooking the significance of joint course scheduling in practical applications [31]. Joint course scheduling involves multiple classes sharing certain courses, requiring the schedule to simultaneously satisfy the timetables of several groups, classroom capacities, and teacher availability. This makes joint course scheduling more challenging than independent course scheduling. The current research gap lies in the inability of many of the existing algorithms to effectively address the complexity of joint course scheduling. While joint schedulinghelps improve the utilization of classroom resources, it also significantly increases the difficulty of scheduling, and the performance of the existing algorithms in this area has been unsatisfactory. Therefore, developing an algorithm that can efficiently handle both independent and joint course scheduling is of great importance. To address this challenge, this paper proposes a progressively optimized heuristic algorithm that combines genetic algorithm (GA) and dynamic programming (DP) techniques, referred to as POGA-DP. This algorithm is not only capable of handling complex constraints within the UCSP but is also particularly suited to joint course scheduling problems. Compared to previous studies, our contributions are as follows:  
1. A progressively optimized hybrid genetic algorithm tool (POGA-DP) was developed and tested on 520 course scheduling tasks at Beijing Forestry University to improve the resource utilization and meet the practical scheduling needs. The innovation of this algorithm lies in its combination of the advantages of the GA and DP, enhancing the computational efficiency while avoiding local optima. 2. We not only verified the effectiveness and stability of POGA-DP in complex instances but also conducted a comprehensive performance comparison with a GA, the Producer-Scavenger Method (PSM) [32], and the Particle Ant Colony Algorithm (ACO). The results demonstrated the significant advantages of POGA-DP in terms of the resource utilization and scheduling quality. 3. This paper designed multiple fitness functions to optimize different objectives (such as theresource utilization and the number of classrooms used), further enhancing the practicality and stability of the algorithm. 4. To ensure the continuity of the scheduling scheme, we also analyzed the similarity of the course arrangements between adjacent weeks, verifying the stability and applicability of POGA-DP in long-term scheduling. In terms of the other sections of this paper, Section 2 details the soft constraints, hard constraints, and specific aspects of the UCSP studied in this paper using a mixed-integer linear programming model. Section 3 provides a detailed description of the POGA-DP algorithm. Section 4 presents the results of the numerical experiments. Section 5 summarizes the contents of this paper.