esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
To estimate the travel times in periods for which historical travel data were unavailable, this study used the travel times estimated for other periods to perform machine learning and establish a nonlinear regression equation capable of generating estimated travel times for the periods with no data. This study employed GPS data collected from fleet vehicles rather than the average driving speed data for each road or historical data because company policies, driver habits, vehicle performance, and procedures might vary across fleets. Thus, the average driving speed on each road might vary across fleets. Based on the actual operating experience and the normal distribution principle of Statistics, this study filtered the outliers. For future research, we will strengthen data collection from GPS driving records and deeply discuss the causes of these outliers and their impact on the prediction model. By collecting GPS records for a specific fleet, this study obtained results that accurately reflected the fleet's operations. However, the generalizability of the results of this study to other fleets requires further verification.
# Personalized Federated Learning with Progressive Local Training Strategy and Lightweight Classifier
## 3 Methodology
### An Overview of PFPS-LWC  
To provide a concise overview of the proposed method, we present the algorithm in a pseudocode format below, as shown in Algorithm 1. The algorithm is divided into two parts: one for the client-side operations and the other for the server-side operation. Before local training begins on the client side, the model needs to undergo an initialization process. In this step, we optimize the global feature extractor by minimizing the discrepancy between its output and the output of the previous round's feature extractor for this client. The optimized feature extractor is concatenated with the classifier to form the initial model for local training, which is then used in this process. On the server side, we perform aggregation based on the proportion of data from each client. By calculating the data proportion for each client, we determine their aggregation weights, which are then used to aggregate the global feature extractor.  
```
1:Input: Global feature extractor \(\theta_{g}\), local feature extractor \(\theta_{g}\): round of global training \(\theta_{i}^{l}\), local classifier \(\phi_{i}^{l}\), dataset \(D_{i}\), local epoch \(E\), global round T, the number of participating clients \(N_{l}\), learning rate \(\eta\).
2:Client Training:
3:for\(i=1\) to \(N_{l}\)do
4:if\(\theta_{i}^{l}\
eq NULL\)then
5: Localize global feature extractor \(\theta_{g}\):
6:\(\theta_{g}^{l+c}\leftarrow\theta_{g}^{l+c-1}-\
abla\mathcal{L}_{sim}(\theta_{ g},\theta_{i}^{l},D_{i})\)
7:endif
8: Initialize local model: \((\theta_{i}^{l},\phi_{i}^{l})=(\theta_{g},\phi_{i}^{l})\)
9:for\(e=1\) to \(E\)do
10:\((\theta_{i}^{l+c},\phi_{i}^{l+c})\leftarrow((\theta_{i}^{l+c-1},\phi_{i}^{l+c- 1}))-\eta\
abla\mathcal{L}_{local}\)
11:endfor
12: Get trained local feature extractor: \(\theta_{i}^{l+E}\)
13: Update the local feature extractor in the last round
14: of global training: \(\theta_{i}^{l}=\theta_{i}^{l+E}\)
15:return\(\theta_{i}^{l+E}\) to server
16:endfor
17:Server Aggregation:
18:for\(t=1\) to \(T\)do
19:\(\left\{\theta_{1}^{1+E},\theta_{2}^{1+E},...,\theta_{N_{l}}^{l+E}\right\} \leftarrow\)Client Training0
20: Compute aggregation weights \([\alpha_{1},\alpha_{2},...,\alpha_{N_{l}}]\)
21: Get the aggregated global feature \(\theta_{g}\)
22: Send \(\theta_{g}\) to clients who are selected in round t + 1.
23:endfor
```  
**Algorithm 1** PEPS-LWC.
# Encryption of 3D or Higher-Dimensional Images: A Review
## 3 Methodology and Research Analysis
### Search Results Analysis and Mapping  
The filtered articles bring bibliometric data that can be analyzed and displayed. Figure 7 presents the distribution of the number of papers published and citations between 2019 and 2024 according to the Web of Science platform. The year with the greatest number of articles published on the selected databases was 2022. Three papers were published in 2023 and 2024. On the other hand, 2019 was the year with the fewest articles published. Furthermore, it is shown that there has been an increasing number of citations over the years, with a peak of 40 citations in 2023, only decreasing in 2024.  
As far as we are concerned, the research related to 3D image encryption or encryption in higher dimensions emerged mainly in 2019; this is the reason why this year was defined as the beginning of the interval considered in this paper.  
Figure 8 shows the distribution of the selected works in the digital databases. ScienceDirect appears with 43.75% (7 articles), which is close to half of the distribution. Optica comes in the second position with 25% (4 articles), and after that the Springer database, containing 18.75% (3 articles), followed by the ACM and IEEE databases with 6.25% (1 article) each.  
Figure 9 is a data visualization known as a word cloud. This image represents the words or terms with most occurrences in the keywords of the selected papers. When a word appears in a huge size, it means that the word has a lot of occurrences. It is possible to conclude that keywords such as encryption, image, light field, and transform are significant in the topic area.  
Figure 6: Database research methodology fluxogram.  
Figure 10 shows the publication's co-citation network in the dataset, revealing the relationships between references which are often cited together. Each node represents a reference in the network that has received at least three citations by publications in the chosen dataset. The larger the node size, the greater the number of citations received. The corresponding color represents the cluster to which it belongs. Connecting two nodes indicates that these two references were cited together by a publication.  
The Louvain clustering [32] algorithm was used by the VOSviewer software (version 1.6.19) to find twenty-nine nodes. The result was five different groups of author keywords. A co-occurrence network was formed from these five clusters, as shown in Figure 11. The largest nodes can be found in the blue cluster. Image encryption in the blue cluster and 3D medical images in the yellow cluster share a strong connection.  
Figure 11: Co-occurrence network of five clusters from author keywords.  
Figure 10: Co-citation network.  
Figure 9: Word cloud.
## 4 Experimental Setups  
The performance of the optimization algorithms of NNs that introduced to FFE are investigated by a 129-Gbit/s optical PAM8 system. The Figure 2 shows the experimental setups. At the transmitter, the input bits are first modulated to PAM8 symbols. After added 2000 training samples and 120 synchronized tokens, the digital PAM8 frames are uploaded into a digital-to-analog converter (DAC) with 86-GSa/s sampling rate and 16-GHz 3-dB bandwidth to generate electrical PAM8 frames. There are 82,360 PAM8 symbols per frames and the symbol rate of electrical PAM8 frames is 43 GBaud. A 40-Gbit/s electro-absorption integrated laser modulator (EML) modulates the electrical PAM8 frames to generate the optical PAM8 frames. Next, the generated optical PAM8 signals are launched into 2-km standard single-mode fiber (SSMF). At the receiver, received optical power (ROP) of the signals is adjusted by a variable optical attenuator (VOA). Then the received optical signals are converted into electrical signals by a photodiode (PD). The electrical signals are converted into digital signals by a real-time oscilloscope (RTO) with sampling rate of 80 GSa/s and 3-dB bandwidth of 36 GHz. Finally, off-line processing is implemented to deal with the digital signals, including re-sampling, synchronization, equalization using the FFE with optimization algorithms of NNs, post filter, maximum likelihood sequence detection (MLSD), PAM8 demodulation. The tap number of FFE is set to 101. After equalization, the high-frequency noise is amplified, which greatly degrades the performance of the system. So, a two-tap post filter is adopted to suppress the amplified high-frequency noise [25; 26]. Furthermore, a known ISI is introduced by the post filter unavoidably, but it can be eliminated by MLSD based on Viterbi algorithm [27; 28].  
Figure 2: Block diagram of 129-Gbit/s optical PAM8 system. TS, training samples; DAC, digital-to-analog converter; EML, electro-absorption modulator integrated laser; SSMF, standard single-mode fiber; VOA, variable optical attenuator; PD, photodiode; RTO, real-time oscilloscope; Sync, Synchronization.
## 2 Results
### PtGPPS1.LSU and PtGPPS.SSU1 are Induced Upon Herbivory  
The analysis of RNAseq data obtained from gypsy moth larvae-damaged and undamaged _P. trichocarpa_ leaves [27] revealed that transcript accumulation of _PtGPPS1.LSU_ and _PtGPPS.SSU1_ was significantly increased in herbivore-damaged leaves in comparison to undamaged control leaves (Figure 2). The expression of _PtFPPS1_, _PtGPPS2.LSU_, and _PtGPPS.SSU2_, however, was not influenced by the herbivory treatment. Three of the putative long-chain _IDS_ genes, Petri.010g138800, Petri.006g135300, and Petri.001g380500, also showed no changes in transcript accumulation upon herbivory, while the remaining _IDS_ genes including the putative _FPPS_ pseudogene Petri.016g004100 were not or nearly not expressed in this organ (Figure 2).  
Figure 2: Transcript accumulation of putative _IDS_ genes in _Lymantria dispar_-damaged and undamaged _Populus trichocarpa_ leaves. Gene expression in herbivore-damaged (herbivory) and undamaged (control) leaves was analyzed by Illumina sequencing and mapping the reads to the transcripts of the _P. trichocarpa_ genome version v3.0. Expression was normalized to RPKM (reads per kilobase of transcript per million mapped reads). Significant differences in EDGE tests (\(p\leq 0.001\)) are visualized by asterisks. Means \(\pm\) SE are shown (n = 4 biological replicates). _PtFPPS1_ (\(p=0.10443\), weighted difference (WD) = \(1.61411\times 10^{-5}\)); Petri.016g004100 (\(p=1\), WD = \(9.19891\times 10^{-8}\)); _PtGPPS1.LSU_ (\(p=5.45471\times 10^{-27}\), WD = \(0.000173475\)); _PtGPPS2.LSU_ (\(p=0.008505317\), WD = \(-2.06885\times 10^{-5}\)); Petri.017g124700 (\(p=1\), WD = \(6.61109\times 10^{-9}\)); Petri.017g124600 (\(p=1\), WD = \(-1.12331\times 10^{-8}\)); Petri.004g090600 (\(p=0.500021071\), WD = \(7.53238\times 10^{-7}\)); _PtGPPS.SSU1_ (\(p=5.0039\times 10^{-28}\), WD = \(0.000269403\)); _PtGPPS.SSU2_ (\(p=0.667703669\), WD = \(4.49444\times 10^{-6}\)); Petri.010g138800 (\(p=0.918085163\), WD = \(-1.05663\times 10^{-6}\)); Petri.006g135300 (\(p=0.338295043\), WD = \(3.93657\times 10^{-6}\)); Petri.011g100400 (\(p=1\), WD = \(0\)); Petri.001g380500 (\(p=0.253763889\), WD = \(-1.31158\times 10^{-5}\)); Petri.011g101200 (\(p=1\), WD = \(1.12627\times 10^{-7}\)); Petri.011g101400 (\(p=1\), WD = \(1.04422\times 10^{-7}\)); Petri.011g099500, no expression detected.  
A qRT-PCR analysis with _PfFPPS1_, _PfGPPS1.LSU_, _PtGPPS2.LSU_, _PtGPPS.SSUI1_, and _PtGPPS.SSUI2_ confirmed the herbivory-induced upregulation of _PtGPPS1.LSU_ and _PtGPPS.SSUI1_ gene expression (Figure S3).
## 2 Literature Review  
Many studies have presented the risks of overcrowding from different perspectives. Fuzzy logic (FL)-based methods have been utilised for modelling uncertainties in overcrowding dynamics. A fuzzy modelling method has been proposed to simulate and analyse factors such as evacuation time against varying crowd velocities [18]. Takagi-Sugeno-type fuzzy rules have been used in the suggested fuzzy model to convert physical laws of pedestrian motion into a fuzzy inference system (FIS) [19]. Moreover, it was demonstrated that high congestion in railway stations affects the quality of service to travellers, potentially damaging the image of the business, or worse, interrupting operational continuity because of the growth of the dwell time [20; 21]. Furthermore, high levels of passenger density have been connected to many psychological and social issues, including perceptions of risk to personal safety and security [22; 23; 24; 25]. Congestion risk management is increasingly important for the railway industry to protect passengers, the public, and employees while enhancing safety and decreasing maintenance costs [14]. Additionally, from a financial perspective, overcrowding affects the rail business by causing significant revenue losses to operators and being a constraint to the government's rail passenger growth objectives and management of demand [26]. Passenger flow is based on nonlinear relationships in which changes occur naturally. To overcome the limitations of models that assume linearity in passenger forecasting, researchers have used various methods, such as statistics-based approaches, machine learning, hybrid models, and deep learning. Related works are shown in (Table 1).
## 1 Introduction  
The European Union has recently emphasized the significant potential of biobased and biodegradable products to foster more sustainable economies and reduce dependence on fossil fuels, presenting promising growth opportunities for the industry. Biomaterials, particularly bioplastics, are garnering increasing attention in the food industry for packaging applications. European Bioplastics defines bioplastics as materials either sourced from renewable or biological resources or as biodegradable plastics [1; 2]. Additionally, the packaging industry is actively seeking more eco-friendly alternatives to conventional materials, driving researchers to focus on developing biodegradable and bioplastic-based food packaging. Common materials used in this area include thermoplastic starch (TPS), polybutylene adipate terephthalate (PBAT), polylactide (PLA), and polymer blends [3; 4; 5].  
This work focuses on biodegradable films made from thermoplastic starch (TPS) blended with polybutylene adipate terephthalate (PBAT). PBAT is a random copolyester synthesized via a polycondensation reaction involving adip acid, 1,4-butanediol, and dimethyl terephthalate. Although derived from petroleum-based raw materials, PBAT is biodegradable. It consists of two structural segments: (i) polybutylene adipate (PBA), an aliphatic component, and (ii) polybutylene terephthalate (PBT), an aromatic component, thereby incorporating both aliphatic and aromatic characteristics [6; 7]. It is regarded as an excellent and highly inert material with excellent flexibility, thermal stability, and biodegradability, which are comparable to those of low-density polyethylene (LDPE) [8; 9; 10]. However, PBAT had limitations such as a high cost and slow and longer degradation cycle than other biodegradable plastics because of the butylene group. For TPS, as starch-based materials are highly hydrophilic, this leads to poor mechanical strength, formability, and stability [3; 7; 11]. Therefore, polyester/starch-based blends are among the most widely used biodegradable plastics for food packaging applications, particularly as food contact materials (FCMs) [4; 5; 12]. The development of new food packaging must prioritize safety by ensuring that no harmful compounds migrate from the packaging into the food [6; 13; 14].  
The safety assessment and migration of substances from packaging needs to involve both intentionally added substances (IAS), such as plasticizers, slip agents, monomers, additives, and solvents, as well as non-intentionally added substances (NIAS), which include degradation products formed during or after manufacturing, impurities in raw materials, and interaction products between packaging components [15; 16]. Therefore, the aim of this article is to assess the risk associated with IAS and NIAS present in the developed active packaging from our previous work [3]. Migration screening was conducted using gas chromatography coupled with mass spectrometry (GC-MS) and ultraperformance liquid chromatography quadrupole-time-of-fight mass spectrometry (UPLC-Q-TOF-MS) to analyze the migration from biodegradable TPS/PBAT samples intended for food packaging applications.
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 2 Alginate Structure and Properties
### Mucoadhesive Properties of Alginates  
Alginate is one of the water-soluble bioadhesive polymers that have been used in drug delivery systems. The surface properties of alginate are studied based on the thermodynamically and mechanical approach to evaluate its mucoadhesive properties [20]. For alginate, the mucus-polymer interaction is mainly affected by the polar force. This study also notes that polysaccharides, such as alginate and scleroglucan, show better biocompatibility than xanthan, carbopol, poly co-(methyl vinyl ether-maleic anhydride (Gantrez), and hydroxypropylmethyl cellulose (HPMC).  
Studies have been conducted to improve the adhesion of alginate. The improvement aims to strengthen the mucus-polymer bond by chemical modification [21; 22]. The modification introduces the thiol or sulfhydryl groups (-SH) to the alginate side chains. The modification product is known as a thiolated alginate. Cysteine is covalently bound to alginate via the carbodiimide method. Thus, the improvement of thiolated alginate  
Figure 1: Chemical structure of G\(-\)block, M\(-\)block, and alternate block in alginates [2].  
mucoadhesiveness is due to the covalent bond between cysteine and mucus [23]. Such interaction displays high cohesive properties to ensure the active ingredients' localization at a given target side. However, increasing the polymer and mucin mixture viscosity of this modified alginate is observed by rheological study with mucin [22].  
Other studies successfully promote a better polymer-mucus interaction by the physical modification of alginate [9; 24]. This modification provided higher non-covalent molecular interaction by adding another bioadhesive polymer. Because this mixture does not change the functional groups of alignates, the gelation ability of alginate is not affected. Nevertheless, the selective hydroxyl group's chemical modification of alginate will also keep the gelation ability of alginate intact.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 1 Introduction
The mathematical model and solution of the long time span SEI problem are presented for the first time. In this study, the problem is transformed into a domain adaptive problem. This idea is confirmed to be feasible through extensive experimental verification. To our knowledge, this is the first work to solve the long time span SEI problem. 2. A novel long time span SEI method, called LTS-SEI, is proposed in this study. The framework of the LTS-SEI method includes four modules: data preprocessing module, feature extractor, classifier, and domain discriminator. It can learn features with domain invariance, interclass separability, and intraclass compactness. These features are confirmed to be effective in identifying different time span signals. 3. A large number of satellite navigation signals are collected using a 13 m and a 40 m large-aperture antenna. We use these signals to construct two real datasets. Dataset A contains data on 10 navigation satellites and three data subsets. The time span between each data subset is 15 min, with a total time span of 30 min. Dataset B contains data on two navigation satellites and 14 data subsets. The time span between each data subset is in the range of 1-2 months, with a total time span of nearly 2 years. 4. Through extensive experiments, the proposed LTS-SEI method is confirmed to satisfactorily perform for the long time span SEI problem and outperform the existing methods. To our knowledge, in addition to our previous work [29], this is also the first work to study SEI using such real, long time span signals.
# Ergonomics Evaluation Using Motion Capture Technology--Literature Review
## 2 Literature Review Methodology
### Defining the Research Queries  
To create a systematic literature search, first, it is important to propose the area of challenge based on the issues addressed in the opening chapters of this work. The individual steps of the research, which can be determined according to [PERSON] and [PERSON] [53], summarise the research process and provide a more detailed description of the problem, formulate research queries, and then answer them. In the introductory sections, the relevance of the topic under study due to the ageing population was mentioned. For this reason, more and more attention will be paid to the ergonomics of the workplace and, in connection with the development of modern approaches, it is appropriate to upgrade the ergonomic evaluation of the workplace.  
The research area is defined and outlined for the topic: ergonomics evaluation using modern approaches.  
To better specify the research topic, the area is described in more detail: the use of motion capture technology for the evaluation of ergonomics of work positions.  
Main research query: how is motion capture currently used as a modern tool to evaluate the ergonomics of work positions?  
Sub-research queries:  
1. What industries are using motion capture technology for ergonomics evaluation?
2. What ergonomic evaluation methods can motion capture be applied to?
3. How has the frequency and content of scientific publications on the topic of using motion capture for ergonomic evaluation changed over time?
4. What problems do authors face when using the motion capture tool for ergonomics evaluation, and what responses do authors mention in their publications regarding these problems?
5. What direction do the authors believe future research in combining ergonomics and MoCap technology will take?
###### Abstract  
5G and beyond networks are being designed to support the future digital society, where numerous sensors, machinery, vehicles and humans will be connected in the so-called Internet of Things (IoT). The support of time-critical verticals such as Industry 4.0 will be especially challenging, due to the demanding communication requirements of manufacturing applications such as motion control, control-to-control applications and factory automation, which will require the exchange of critical sensing and control information among the factory nodes. To this aim, important changes have been introduced in 5G for Ultra-Reliable and Low-Latency Communications (URLLC). One of these changes is the introduction of grant-free scheduling for uplink transmissions. The objective is to reduce latency by eliminating the need for User Equipments (UEs--sensors, devices or machinery) to request resources and wait until the network grants them. Grant-free scheduling can reserve radio resources for dedicated UEs or for groups of UEs. The latter option is particularly relevant to support applications with aperiodic or sporadic traffic and deterministic low latency requirements. In this case, when a UE has information to transmit, it must contend for the usage of radio resources. This can lead to potential packet collisions between UEs. 5G introduces the possibility of transmitting \(K\) replicas of the same packet to combat such collisions. Previous studies have shown that grant-free scheduling with \(K\) replicas and shared resources increases the packet delivery. However, relying upon the transmission of \(K\) replicas to achieve a target reliability level can result in additional delays, and it is yet unknown whether grant-free scheduling with \(K\) replicas and shared resources can guarantee very high reliability levels with very low latency. This is the objective of this study, that identifies the reliability and latency levels that can be achieved by 5G grant-free scheduling with \(K\) replicas and shared resources in the presence of aperiodic traffic, and as a function of the number of UEs, reserved radio resources and replicas \(K\). The study demonstrates that current Fifth Generation New Radio (5G NR) grant-free scheduling has limitations to sustain stringent reliability and latency levels for aperiodic traffic.  
grant-free; scheduling; URLLC; ultra-reliable and low-latency communications; 5G; deterministic; time-critical; reliability; latency; aperiodic traffic; Industry 4.0 +
Footnote †: journal: Sensors