esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D-STARNET: Spatial-Temporal Attention Residual Network for Robust Action Recognition
## 5 Conclusions  
The focus of this study is to use skeleton data to identify human behavior. We introduced the skeleton-based action recognition model 3D-STARNET to achieve a robust detection of action. Firstly, 3D heat maps were used instead of 2D skeleton sequences as the input of the model to reduce the strong dependence on skeleton points. Secondly, we proposed a spatiotemporal attention mechanism to improve the model's information processing and feature extraction capabilities in the spatiotemporal dimension. Finally, we introduced a multi-stage residual structure to achieve efficient information transmission without changing the computational complexity. We verified it on the NTU-RGBD120 dataset, and the overall accuracy reached 96.74%, which has broad application prospects in monitoring and early warning, safety prevention and control, rail transit, and human-computer interaction.  
Although 3D-STARNET has achieved remarkable results in action recognition, we hope that the model has generalization capabilities in action recognition in different environments and different complexities. Future research can further explore the performance of the model in diverse scenarios and explore the fusion of skeletal data with other types of data, such as RGB images or depth information, to further improve the accuracy and robustness of behavior recognition.  
The authors confirm contribution to the paper as follows: methodology, J.Y. and S.S.; writing--original draft preparation, J.Y. and S.S.; resources, J.Y.; writing--review and editing, J.Y., S.S., J.C. and Z.Y.; data curation, J.C.; software, H.X. validation, Z.Y.; investigation, S.S., J.C., H.X., Y.W. and Z.Y.; supervision, S.S. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Special Project of Science and Technology Basic Resources Survey (grant No. 2022 FY101400) and the National Natural Science Foundation of China Innovation Group Project (grant No. 52121003).  
Not applicable.  
The NTU-RGBD 120 dataset is available at [[https://rosel.ntu.edu.sg/dataset/actionRecognition/](https://rosel.ntu.edu.sg/dataset/actionRecognition/)]([https://rosel.ntu.edu.sg/dataset/actionRecognition/](https://rosel.ntu.edu.sg/dataset/actionRecognition/)) (accessed on 24 June 2024).  
We would sincerely like to thank the people who supported this work and the reviewing committee for their estimable feedback.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 3 Chemical Modifications: Guardians of tRNA Integrity and Function
### RNA Modification on tRNA
#### 3.1.4 N7-Methylguanosine (m\({}^{7}\)G)  
The modification of m\({}^{7}\)G is one of the most prevalent RNA modifications located at the 5'cap of mRNA, internal ribosomal RNA (rRNA), and tRNA across all species, and recent studies have identified its new location on internal mRNA. The mRNA cap m\({}^{7}\)G is modified by RNA guanine-N7 methyltransferase in complex with RNMT-activating miniprotein (RNMT/RAM); responsible for promoting mRNA processing, export, and translation initiation; and protects mRNA from degradation by various nuclease, while m\({}^{7}\)G on internal mRNA modified by methyltransferase-like 1/WD repeat-containing protein 4 (METTL1/WDR4) is reported to function as a cellular translation regulator. METTL1/WDR4 is also responsible for the methylation of positions 46G to m\({}^{7}\)G46 at the variable loop of about 22 different types of tRNA [85; 86; 87; 88]. The METTL1-WDR4 complex recognizes the elbow of target tRNA through shape complementarity, while the N-terminal of METTL1 coupled with the WDR4 C-terminus and tRNA variable loops to form a catalytic pocket to secure the methyltransferase activity of METTL1 [89; 90].  
Growing evidence has unveiled the important role of tRNA m\({}^{7}\)G46 in tRNA metabolism. The deletion of METTL1 significantly reduces the m\({}^{7}\)G levels of both tRNA and mRNA and impairs overall translation efficiency in cells [91]. In fact, the loss of tRNA m\({}^{7}\)G46 increases tRNA sensitivity to RNase A/T in vitro and promotes the biogenesis of tsRNA in vivo, which subsequently disturbs the cellular steady-state tRNA pools and inhibits overall protein synthesis. Moreover, the downregulation of m\({}^{7}\)G46 on some tRNAs, such as tRNA\({}^{\rm Arg}\), can induce ribosome pausing during translation and decoding processes, thus impairing the cellular translation bias that adapts to a high proliferation rate of tumor cells.  
In light of the methylation activity of the METTL1-WDR4 complex, increasing evidence confirms the oncogenic role in various cancers, such as acute myeloid leukemia [86; 92], bladder cancer [93], hepatocellular carcinoma [94], prostate cancer [95], etc. The expression levels of the METTL1-WDR4 complex are prevalently higher in tumor samples and associated with poor prognoses in patients [86; 96]. The deletion of METTL1 reduces cancer cell proliferation and promotes cell apoptosis, thus inhibiting cancer metastasis. Moreover, METTL1-WDR4 complex-dependent tRNA m\({}^{7}\)G46 plays pivotal roles in stem cell fate determination and aging [16; 97]. Knockout of _Mettl1_ alters cellular tRNA expression signature and leads to self-renewal- and neural differentiation-defective phenotypes in mESCs [16].
# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
## 2 Related Works
As can be seen from the presented review, most authors emphasize different ways, methods and techniques to increase security against different cyber-attacks, which is normal and understandable, but do not discuss if the attack would have any impact on the parameters of the individual VoIP flows. The very idea of studying the effect of an attack on the performance of a VoIP server is interesting and will show what would happen if the methods to defend against such attacks were overcome.
## 5 Simulation Results
### Sum Rate and Spectral Efficiency for Different Traffic Conditions  
The _SLNR_ precoding allows us to spatially multiplex users in an effective way. In Figure 14 we examine the interference rejection capability of the _SLNR_ precoder by varying the traffic loading. In particular, we analyze the average spectral efficiency (averaged over all subcarriers for each user and over all users) and we detect the maximum attainable sum rate before experiencing a noticeable sum rate loss. At first, the total sum rate will likely increase as long as the number of users per sector does not significantly exceed the number of antennas \(N_{ty}\) at the BS along the horizontal axis, even if the per-user spectral efficiency will decrease, as the power must be split among more transmissions. Nevertheless, when the density of the users in the sector becomes too high, the sum rate drops, then more and more users will experience outage as their SINR becomes too low to communicate  
Figure 12: CDF of UT rates for different layers with single or dual polarized antenna elements, \(N_{t}=[N_{ty}\times N_{tz}]\), \(N_{r}=[N_{ty}\times N_{rz}]\), \(P_{TX}=47\) dBm, traffic density = 2500 users/km\({}^{2}\).  
with the BS. The peak of the sum rate roughly reaches 24 Gbps in the correspondence of traffic loading of approximately \(\mu=4500\) users/km\({}^{2}\), i.e., \(\frac{1}{3}\mu\frac{3\sqrt{3}}{2}R^{2}\approx 39\). The curve of the spectral efficiency follows an almost linearly decreasing behavior w.r.t. the number of UTs. As expected, the outage probability is directly proportional to the traffic loading.
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 5 Giardia intestinalis
### Epidemiology of Giardiasis  
_G. intestinalis_ was responsible for approximately 180 million symptomatic infections annually, and its prevalence in the stool of asymptomatic children was between 17% and 18% in Spain and between 18% and 64% in subtropical countries, such as Brazil, Ethiopia, Argentina, and Mozambique [15]. Giardiasis was the most frequently reported food- and waterborne parasitic disease in the EU according to the report on giardiasis of the European Centre of Disease and Prevention (ECDC) for 2019, with 5.2 cases per 100,000 inhabitants, highest for Belgium and Bulgaria in the age group 0-4 years [145].  
Between 2011 and 2017 over 140 waterborne outbreaks of giardiasis occurred globally, while foodborne transmission occurred in approximately 23.2 million cases annually, but these could be underestimated since the detection of _G. intestinalis_ in food is inefficient, and reporting and surveillance are carried out mainly in countries in which giardiasis is notifiable, namely the USA, European Union (EU), some Australian states, and New Zealand [26; 138; 146].  
_G. intestinalis_ showed a prevalence of 53.3% in 2020 and 56% in 2021 among 5-8 year-old children in Sheno, Ethiopia 2020 [147]; of 52% in Zambezia province, Mozambique [148], where one of the associated risk factors is drinking untreated river/spring water [149]; of 37% in marginalized rural areas in the north of the Palestinian West Bank Region, where it was the predominant parasite [150]; an estimated prevalence in Colombia between 0.9 and 48.1% when analyzed with classical microscopy and between 4.2 and 87% when analyzed by PCR [151]; and a prevalence of 10% in asymptomatic school children in Lusaka, Zambia [152]. _G. intestinalis_ accounted for 13.8% of intestinal infections in immunocompromised patients with intestinal illnesses in Yemen, a country with limited water resources [153].  
According to a meta-analysis of cases reported globally between 1977 and 2016, impaired immunity was associated with giardiasis, while breastfeeding was a protective factor in African countries. This was explained by the inhibitory activity of unsaturated fatty acids and specific secretory IgA antibodies present in human milk in areas of endemicity and with reduced exposure to contaminated milk. Not washing hands after the toilet and before eating was significantly associated with giardiasis in children, while not using soap was associated with giardiasis in the general population in Asia, Africa, and South America. Consumption of fresh produce was identified as a risk factor in children and consumption of composite ready-to-eat foods represented a highly significant risk factor in Canada. The consumption of unwashed vegetables increased the risk of giardiasis in Cuba and Malaysia [29]. A recent global meta-analysis showed significant positive associations between _G. intestinalis_ infection and lack of safe drinking water and no access to the toilet. Meta-regression showed that _G. intestinalis_ infection risk decreased significantly over time [140].
However, a practical application of metallic NPs is limited, as they concurrently reveal a tendency to aggregate and sediment [11]. A solution to this problem seems to be the synthesis of polymeric nanocomposites (pNCs) that contain metallic NPs. Such pNCs do not only limit the catalytic activity of metallic NPs but even enhance it, ensuring their stability and safety [12]. As such, more attention is put in the literature regarding the synthesis of NPs supported by an organic matter containing reductive moieties. This includes, e.g., the metal vapors synthesis (MVS) method using a metal scavenger [12; 13], the hydrothermal method [14], as well as the reduction-coupled adsorption method occurring at a solid-liquid interface [15; 16]. Based on the author's own works [15; 16], pNCs loaded with AuNPs facilitate the catalytic activity of these nanostructures in the catalytic reduction of 4-nitrophenol (4-NP). Moreover, the morphology of the polymeric matrix (suspension copolymers) enables an efficient re-use of the so-prepared catalysts, retaining the catalytic activity of AuNPs for over 11 cycles [15]. Hence, it is hypothesized that, similarly as in the case of AuNPs, a barely known nanomaterial, i.e., dhenium nanostructures (ReNSs) as active sites of pNCs-based NCats, would significantly boost the catalytic activity of such NCats toward NARs reduction. However, due to a rare occurrence and a high price, the literature lacks the information on ReNSs. There are only a few reports on ReNSs synthesis that include advanced physical processes [17; 18; 19; 20; 21]. It is also confirmed that ReNPs reveal an extraordinary catalytic activity outperforming this for AuNPs and NPs of platinum group metals (PGMs) in organic syntheses (e.g., oxidation of olefins) [18; 22; 23; 24]. Although two examples are reported so far in which ReNSs-based NCats outperform other PGMs catalysts in the decomposition of NARs [25; 26], none of these examples provides a solution to problems of heterogenous catalysis related to the stability of the NCat and a difficulty with its operation. As a result of this, the premise of the present work was to synthesize a new pNC that would be based on ReNSs loaded directly within the structure of a suspension copolymer (in situ). It is hypothesized that such an approach would result in the synthesis of a heterogenous NCat, revealing an increased catalytic activity owed to the stable ReNSs presence. The procedure involved an anion exchange reaction between ReO\({}_{4}\)- oxoanions and amino functionalities derived from 1,1\({}^{\prime}\)-carboinidazole present in the polymeric matrix. Then, Re (VII) ions were reduced, and the resultant ReNSs were precipitated within the polymer, forming a new pNC with ReNSs. Then, the so-obtained heterogenous NCat was employed in the catalytic process involving model reactions of 4-NP and 4-NA hydrogenation. It was expected that due to a unique catalytic activity of ReNSs, followed by their direct precipitation (in situ) within a polymeric matrix, the so-prepared NCats would outperform other heterogenous catalysts with metallic NPs, simultaneously ensuring the same or even better catalytic activity.
# Detection of Azo Dyes in Curry Powder Using a 1064-nm Dispersive Point-Scan Raman System
## 2 Materials and Methods
### Sample Preparation  
Sudan-I (95% dye, Aldrich, Carson City, NV, USA), metanil yellow (70% dye, Aldrich, Carson City, NV, USA), and organic curry powder (Frontier Natural Products CO-OP, Norway, IA, USA) were used to prepare mixture samples. Curry powder-Sudan I and curry powder-metanil yellow mixture samples were prepared separately at 1%, 3%, 5%, 7%, and 10% (\(w/w\)) concentrations by mixing the powders in a vortex mixer (Scientific Industries Inc., Bohemia, NY, USA) for 10 min. For simultaneous detection of the two azo compounds in the curry powder, Sudan-I and metanil yellow (5% each, \(w/w\)) were mixed together with the curry powder in the vortex mixer for 10 min. Each sample was packed in a shallow nickel-plated sample container (25 mm \(\times\) 25 mm \(\times\) 1 mm), and the surface was leveled flush with the top edge of the sample container. An amount of 0.27 g of mixture sample at each concentration level was prepared to completely fill the volume of the sample container for Raman spectral measurement.
# A Parallel Algorithm for Scheduling a Two-Machine Robotic Cell in Bicycle Frame Welding Process
## 7 Computational Experiments
### Experiments Results and Discussion  
The results of the experiments are shown in Table 1 and Figure 5. First, let us discuss the MILP-based method. Gurobi Optimizer was unable to solve larger problem instances, and shown an inferior performance also for smaller instances. The choice of the model (with- or without the reduction, see Section 4) had no significant impact on the performance, even thought the presolver did not prune any binary/integer variables. For the reduced model, the presolver was also unable to decrease the number of constraints, however it computed a correct upper bound on \(\mathbf{s}\),  
\[\forall i\in\mathcal{J}\quad\mathbf{s}_{i}\leq\max_{j\in\mathcal{J},\;a\in \mathcal{M}}s_{j\to i}^{a}. \tag{24}\]  
To improve the performance, a hot-starting method was also tested. High-quality solutions were generated using the one-opt heuristic [12]. Unfortunately, the hot-start had no impact on the computation time, as the technique is generally more effective when finding feasible solutions is hard. Providing a lower bound, analogous to (24), also did not affect the computation time.  
The sequential [12] algorithm and the new parallel one were able to solve all the considered instances. The parallel algorithm was inferior in respect to the sequential one for \(n<128\), mostly due to an overhead of using GPU. For larger instances, the speedup was increasing steadily, reaching almost 314 for \(n=8192\) (see Figure 6). While in the considered bicycle factory the number of jobs usually does not exceed 400, the ability to schedule larger payloads might be important in different applications, e.g., where welding time is shorter. However, even for \(n=256\) the speedup obtained is significant, allowing to assign jobs in almost 10 times more product mixtures or processing orders in the same time (i.e., solve 10 times more problem instances).  
\begin{table}
\begin{tabular}{r r r r r} \hline \hline
**Number** & **Speedup** & \multicolumn{4}{c}{**Average Algorithm Run Time [s]**} \\ \cline{3-5}
**of jobs \(n\)** & CPU vs. GPU & CPU [12] & GPU & MILP \\ \hline
16 & 0.013 & 0.000006 & 0.0005 & 0.4775 \\
32 & 0.074 & 0.000140 & 0.0019 & 36.0980 \\
64 & 0.538 & 0.000880 & 0.0017 & 7597.6177 \\
128 & 2.601 & 0.006282 & 0.0024 & – \\
256 & 9.605 & 0.062134 & 0.0065 & – \\
512 & 32.904 & 0.729642 & 0.0222 & – \\
1024 & 75.606 & 7.453988 & 0.0986 & – \\
2048 & 122.774 & 69.092194 & 0.5628 & – \\
4096 & 216.115 & 643.553306 & 2.9778 & – \\
8192 & 313.890 & 5589.780725 & 17.8080 & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the experiments; for MILP and \(n>64\) no optimal solutions were found in 8 h of computations per instance.
## Results
### Gpp\({}_{\text{capacity}}\) Selection Criteria  
For GPP\({}_{\text{capacity}}\) selection, diurnal NEP and VPD averaged over 16-day periods were examined. The results are shown in Figure 5 for the CA-Let 5(a,b), TH-SKR 5(c,d), and JP-FJY 5(e,f) sites. In Figure 5(a), the midday depression followed Pattern 2 (Figure 2) around 14:00 (local time), as shown  
Figure 5: Diurnal variation in the mean half-hourly dataset over 16-day periods: (**a**) net ecosystem production (NEP), (**b**) vapor pressure deficit (VPD) at CA-Let, (**c**) NEP and (**d**) VPD at TH-SKR, and (**e**) NEP and (**f**) VPD at JP-FJY. Arrows indicate the timing of the midday depression.  
by the arrow. In Figure 5(b), VPD at the time of the midday depression was around 2 kPa, as shown by the arrow. Figure 5(c) shows a midday depression for TH-SKR at around noon, indicating Pattern 2 diurnal variation (Figure 2). In Figure 5(d), VPD at the time of the midday depression was around 2 kPa. VPD values for all Japanese sites were less than 2 kPa (Figure 5(f)). Therefore, daytime GPP (PAR \(>\) 0) and VPD \(<\) 2 kPa (VPD threshold) were applied as GPP\({}_{\text{capacity}}\) selection criteria for each study site.
###### Abstract  
Images with sensitive content require encryption for storage and transmission. Symmetric schemes can cipher them, while an asymmetric cryptosystem can distribute the secret key safely. For this reason, we propose a dynamic hybrid cryptosystem, which ciphers images and transfers its private keys. It has a symmetric algorithm that applies the Lorenz equations for generating different boxes and permutations in every encryption process and round. Since the secret key concatenates two private numbers, an asymmetric algorithm is included for its key distribution. The proposal uses the Diffie-Hellman protocol with ElGamal for obtaining a seed and building 128 strings. Then, the SHA-512 is applied in each of them a number of times associated with the secret key value in its blockchain representation. The resultant strings are concatenated to conform to the public key. Finally, the tests indicate that the cryptosystem resists differential, linear, algebraic, and brute-force attacks. Its cipher quality is high according to the entropy, correlation, DFT, NPCR, UACI, AC, texture analysis, and goodness of fit test. Additionally, occlusion, additive, multiplicative, and the proposed \(\chi^{2}\) noise attacks are simulated on encrypted images. Finally, the sharpness loss is measured with the Similarity Parameter and improved with a filter \(5\times 5\).  
Lorenz equations; ElGamal system; chaos; Hash SHA functions; dynamic 5-box; dynamic permutation 2023
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 2 Sample Preparation  
Aflatoxin samples are generally collected from the field following predefined sampling plans [27; 28; 29]. After sample collection, determination of aflat relies on how samples are homogenized, followed by the removal of test portions (analytical samples) to ensure representative qualitative and quantitative analytical data can be generated to support risk assessment, exposure studies and compliance activities. Furthermore, due to the limitation of available detection technologies, the majority of foods that are prone to mycotoxin contamination cannot be directly analyzed for aflat without extraction and clean-up, a fact that has been incentivizing researcher to use different extraction and clean-up methods to separate aflat from matrix components. The purpose of sample preparation is to minimize the impact of heterogeneous distribution of aflat in various food commodities [30; 31; 32], selectively enrich for aflat based on their physicochemical properties and reduce bulk background matrix to make final extracts more suitable for instrumental analysis.