esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Generic Detection Approaches
The alarms may exhaust security workers and disguise serious dangers. Machine learning classification approaches have been utilized for detecting DDoS attacks on IoT systems. Unlike traditional techniques, ML algorithms are more precise when it comes to distinguishing traffic attacks from flash crowd, thus lowering the false positive occurrence. In order to identify DDoS attacks in IoT environments, researchers have studied several supervised learning algorithms categorized as either conventional classification methods or DL approaches [32]. Conventional approaches set threshold values to identify attack traffic using extracted properties such as packet size, protocol type, behavior patterns. The utilization of AI-driven learning approaches have played a significant effect on the automatic generation of hierarchical representations from network data [33]. These techniques have special qualities; some perform well in identifying trends in sequential data and others may extract features from traffic representations that would otherwise be hidden in many dimensions. In IoT environments, ML classification offers several advantages over traditional detection methods. Unlike signature-based systems limited to known attack patterns, classification models allow for the identification of new attacks displaying traits comparable to training samples [34]. Their multi-dimensional analytic features help to evaluate different traffic conditions, thereby strengthening the detecting systems. With sophisticated models lowering false positive rates by 40-60% while keeping detection sensitivity, research by [35] showed considerable increases in detection accuracy. The improved accuracy helps security specialists focus their attention on real dangers and saves their effort. Moreover, the flexibility of such systems proves to be an operational benefit as regular retraining solves evolving usage patterns and growing device populations [36]. Further benefits come from implementation flexibility as these models may be used depending on resource availability throughout several network tiers. Combining ML approaches with current security architecture improves protection and produces defensive systems that are more resistant to sophisticated attacks. Depending on the environment of application and particular requirements, ML techniques show different efficiency. Network designs, device populations, and attack strategies affect performance [37]. This diversity emphasizes the need for ongoing research targeted at the identification of ideal algorithms fit for certain operating environments and danger settings. Recent research has focused on developing frameworks for algorithm selection that takes predicted risks, network properties, and resource availability into account. The rapid development of IoT environments necessitates continuous research on flexible security solutions that efficiently handle new attack strategies and take into account the resource limitations common in IoT systems.
## 5 Conclusions  
In this study, we tested the Noah-MP model for snow accumulation and melting process modeling at the Kuwei site in the Irtysh River Basin, and simulated the snow cover process by using CMFD and ERA-Interim as forcing data at the whole basin from 2000 to 2018. The simulation results were also compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD) and snow depth obtained from microwave remote sensing (LSD and CSS data). The main findings are as follows:  
1. STC, SFC, and ALB schemes mainly affect the snow melting process, while SNF mainly affects the accumulation process. Among the four schemes, STC has the greatest impact on the accuracy of snow cover simulation. When STC use the semi-implicit scheme, the overall simulation accuracy is better than that of the fully implicit scheme.
2. CMFD and ERA-Interim as the forcing data can accurately simulate the snow accumulation and melting process of the whole basin, and the results of CMFD simulation are more accurate than those of ERA-Interim simulation. The main reason is that the data accuracy of CMFD is higher than that of ERA-Interim.
3. In the years with low snow depth, the snow depth retrieved based on microwave remote sensing is in good agreement with the observed snow depth. However, in the years with high snow depth, such as 2002, 2004, 2008, 2009, 2010, and 2012, the snow depth retrieved by remote sensing is seriously underestimated.
4. Spatially, the snow depth and snow depth equivalent in the north of Irtysh River Basin are higher than those in the south, mainly because the altitude and precipitation in the north are higher than those in the south. The snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly from 2001 to 2017. However, the end time of snow melting was obviously advanced.  
Figure 8: Daily maximum wind speed at the Altay, Habahe, and Fuyun sites from 2001 to 2018.  
**Author Contributions:** Conceptualization, L.Z.; methodology, L.G.; software, L.G. and M.A.; validation, L.G.; formal analysis, L.G. and L.Z.; investigation, L.G., L.Z. and W.Z.; resources, Y.S.; data curation, L.G. and W.Z.; writing--original draft preparation, L.G. and L.Z.; writing--review and editing, Y.Z.; supervision, Y.S.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Natural Science Foundation of China (grant number 42001060, 42171467 and 41705139), Key Special Subject of the National Key Research and Development Program of China (grant number 2019 YFC1510502) and the Natural Science Foundation of Qinghai Province (grant number 2021-ZJ-947Q).  
Not applicable.  
The data supporting this study are available from the authors.  
The authors declare no conflict of interest.
## 3 Results and Discussions
### Hydrogen/Air Mixture
#### 3.1.3 Time-Averaged Fuel Specific Impulse  
The time-averaged fuel specific impulse is reported in Figures 11-14 for all the cases that achieved stable detonation. Overall, the fuel specific impulse figures are higher as a direct consequence of the different definitions employed.  
For low oxidizer pressure (i.e., 4.5 bar), the 400 mm configuration dominates the high ER region for 100 Hz (Figure 11) and 150 Hz (Figure 12) operation, whereas the same configuration dominates the low ER region for 233 Hz (Figure 13) and 350 Hz (Figure 14) operation.  
For high oxidizer pressure (i.e., 6 bar), the same 400 mm configuration performs the best in terms of the fuel specific impulse for all the operating frequencies, except 233 Hz, where the 500 mm configuration reaches higher values.  
Figure 11: Time-averaged fuel specific impulse for a frequency of 100 Hz for \(\text{H}_{2}\text{/}\text{air}\).  
Figure 10: Time-averaged total specific impulse for a frequency of 350 Hz for \(\text{H}_{2}\text{/}\text{air}\).
## 6 Comparison Analysis and Research Challenges
### Comparison Analysis
All of the protocols described here have focused on load-balancing by controlling the size of the forwarder node group. Moreover, the simulations provided by most of the protocols are performed in both sparse and dense networks, except for LORA. LORA has been analyzed and simulated in a sparse network, but it is important to observe the behavior of the protocol in dense networks as well. Moreover, except for MOR, all the mentioned OR protocols are simulated in simulators, and the real-time experiments of MOR show that as the number of channels increases, the length of the duty cycle also increases, which leads to more energy consumption [14].
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 6 Performance Analysis of PrECast
### Convergence Analysis
Based on Theorems 5 and 6, we can precisely estimate how soon PrECast converges depending on how many hosts are connected to each access layer switch. Let \(\lambda\) be the arrival rate of ARP requests in the network. Then PrECast converges in \(n_{m}/\lambda\) or \(\lceil n/2\rceil/\lambda\) time depending on whether \(n_{m}\geq n_{1}+n_{2}+\cdots+n_{m-1}\) or not.
## 3 Results  
Effects of Pre-Fementative Addition of UFAs Mixture on Yeast Growths, Fermentation Activity and Major Fermentation Products  
The profiles of cell growth and sugar consumption (glucose and fructose), and some fermentation parameters (including fermentation duration time, maximum biomass, maximum growth rate, and fermentation rate) in different treatments are showed in Figure 1 and Table 1, respectively. In all wines, fermentation finished successfully to dryness (below 4 g/L residual sugar) within the monitored period of 326-446 h, and both yeast strains broadly followed similar growth patterns, while the fermentation kinetics was significantly different due to the UFAs addition. In fact, UFAs addition promoted yeast growth and population, but unexpectedly decreased fermentation rate (sugar consumptions rate, from 1.94 g/L/h in the control to 1.61 g/L/h in high-UFAs fermentation, on average) and prolonged fermentation duration time compared to the control, especially in BDX strain (326 h vs. 446 h in low-UFAs and high-UFAs added fermentations). The strain of BDX is more sensitive to UFAs addition than EC1118 strain because its fermentation duration time was extended by 120 h relative to EC1118 strain in low-UFAs added fermentation, which might explain the great variation of chemical and aromatic compounds in final wine as showed below. In general, the effect of UFAs addition on fermentation performances is strain-dependent, with a prolong effect of low addition on the fermentation duration of BDX, but not for EC1118.  
Figure 1: Cell growth and sugar consumption of EC1118 (**A**) and BDX (**B**) treated by different levels of UFAs during alcoholic fermentation (Control, L, and H represented the control, low-UFAs, and high-UFAs added juice).  
The extracellular metabolites concentrations in wines at the end of fermentation are showed in Table 1, including glycerol, ethanol, citric acid, malic acid, succinic acid, and acetic acid. The pre-fermentative addition of UFAs influenced the final contents of glycerol, ethanol, and acetic acid in respective strain fermentation, but had a minor impact on succinic acid, citric acid, and malic acid productions. The contents of glycerol and ethanol reached the highest concentration in wines with high-UFAs addition (10.57 and 10.45 g/L, 14.57 and 14.72% \(v\)/\(v\) in EC1118 and BDX strains fermentation wines, respectively), while acetic acid contents were lowest in these wines regardless of the yeast strains (0.43 and 0.24 g/L, respectively), implying that UFAs has a special mechanism of influencing the syntheses of the major fermentative products. These results are inconsistent with the data of [PERSON] et al. (2014), who reported that linoleic acid addition resulted in a promotion on acetic acid synthesis by _S. cerevisiae_ EC1118. The inconsistent results could be due to the difference in added UFAs composition (single vs. synergistic effect), which needs to be further investigated.
## 4 Experiments
### Comparison with State-of-the-Art Methods
#### 4.3.2 Qualitative Evaluation  
Figure 5 shows a visual comparison of our model with existing SISR and RefSR methods. We compared our method with several of the current top-performing methods, including ESRGAN [18], RankSRGAN [19], TTSR [4], MASA [5], C\({}^{2}\)-Matching [6], and RRSR [26]. It is evident from the figure that SISR methods struggle with severe degradation of high-frequency information in the LR images, and ESRGAN and RankSRGAN have difficulty reconstructing texture details, especially when dealing with text, faces, and fine textures. Compared to SISR, RefSR methods can transfer similar textures from reference images, thereby generating more texture details. In comparison with existing RefSR methods, our model, through its adaptive features, better perceives and transfers texture information from the reference image. As a result, our model effectively compensates for the missing high-frequency details in the low-resolution image, leading to texture reconstruction that is much closer to the real image. We have marked the areas of interest (i.e., enlarged details) with red boxes in the figure, and presented corresponding enlarged images for comparative analysis below.  
For example, in the first group of images, regarding text information, ESRGAN and RankSRGAN methods failed to effectively restore clear text. Although the visualized images of other methods show some recognizable letters, there is still noticeable distortion. In contrast, our method is able to clearly and accurately restore the text information in the image, avoiding blurriness and loss of details, demonstrating a significant advantage.  
Through six sets of comparison images, it is evident that our method outperforms existing RefSR methods in texture detail restoration, recovering more high-frequency information and fine details. This thoroughly demonstrates the effectiveness of our proposed adaptive texture matching and aggregation approach, which not only improves image quality but also enhances the realism of the reconstructed images.
## 2 Literature Review
### Supply Chain Management (SCM)  
SCM primarily faces persistent issues such as lack of transparency, fragmented data ecosystems, and the dynamic nature of global markets [25; 26]. Supply chains (SCs) often encounter critical performance bottlenecks that elevate costs, compromise product quality, and reduce operational efficiency [27; 28]. Notable inhibitors include the bullwhip effect, where small demand fluctuations amplify up the Supply Chain (SC), influencing the launching of new product lines without agile coordination, overstocking or excessive inventory, and restrictive or siloed data flows.  
These challenges can lead to inefficiencies where manufacturers are compelled to overproduce or overstock raw materials, increasing operational strain. Furthermore, limited interoperability and delayed data sharing in complex global SC networks hinder real-time visibility and slow down the seamless movement of goods and services.  
DLTs have shown promising potential in alleviating these bottlenecks. DLT enables greater transparency, immutability, and decentralization, which are pivotal in enhancing the resilience and reliability of SCM systems [29]. For instance, in IoT-enabled SCs, the integration of Holochain offers improvements in data integrity, provenance, and the authenticity of goods. Its agent-centric model allows each participant to independently validate and verify product-related data, maintaining tamper-proof and trustworthy records across the network. A practical example is demonstrated in [30], where Holochain ensures end-to-end traceability in food SCs, from farm to consumer, enhancing food safety, compliance, and consumer trust.  
Holochain provides a significant advantage for SCM by eliminating the requirement for global consensus, which in turn enables horizontal scalability through independently operating nodes. Its agent-centric architecture empowers each participant to maintain fine-grained control over their data, allowing them to selectively share information.This ensures confidentiality and supports regulatory compliance, particularly important for handling sensitive information such as pricing, supplier agreements, or customer data. Unlike traditional blockchain systems, Holochain does not rely on mining, tokens, or energy-intensive consensus mechanisms, making it lightweight, energy-efficient, and faster. These characteristics translate to lower operational costs, reduced environmental impact, and quicker transaction processing. Holochain's architecture is resilient to localized failures, as nodes operate with eventual consistency and the system is self-healing and context-aware, key benefits for managing complex, geographically dispersed, and fragmented SCs.  
Figure 2: Holochain applications in IoT networks.
# Differential Signalling in Free-Space Optical Communication Systems
## Appendix A  
### BER Expression in Weak Turbulence  
When dealing with the BER expression in weak turbulence in Label (8), the mathematical simplification will result in an expression in the following form:  
\[P(e|1)=\int\limits_{0}^{+\infty}\frac{1}{2h}\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp \Biggl{(}-\frac{(\ln(h/h_{0})-2\mu)^{2}}{8\sigma^{2}}\Biggr{)}Q(\psi h)dh. \tag{10}\]  
By substituting \(h=h_{0}\exp(2X)\), where \(X\) has a normal random distribution, \(dh=2 hdx\) and setting the integral range, we have  
\[P(e|1)=\frac{1}{\sqrt{2\pi\sigma^{2}}}\int_{-\infty}^{+\infty}\exp\Biggl{(}- \frac{(x-\mu)^{2}}{2\sigma^{2}}\Biggr{)}Q(\psi l_{0}\exp(2x))dx. \tag{11}\]  
Next, substituting \(u=(x-\mu)/\sqrt{2\sigma^{2}}\), Label (11) is re-written as  
\[P(e|1)=\frac{1}{\sqrt{\pi}}\int\limits_{-\infty}^{+\infty}e^{-u^{2}}Q\Bigl{(} \psi h_{0}e^{2\mu+u\sqrt{8\sigma^{2}}}\Bigr{)}du. \tag{12}\]  
In Label (12), \(\int_{-\infty}^{+\infty}e^{-x^{2}}f(x)dx\) can be approximated by Gauss-Hermite quadrature formula as \(\sum_{i=1}^{k}\omega_{i}f(x_{i})\)[44], where \(k\) is the order of approximation, \(x_{i}\) is the zero of the \(k\)th-order Hermite polynomial, and \(\omega_{i}\) denotes the \(k\)th-order weight factor.
## 3 Our Methods
### Model Training
#### 3.3.3 Parallel Training  
Training deep learning models with a large amount of training data is not a trivial task, which is performed in a high-performance computing infrastructure with a large number computing nodes or accelerators. Training NMT models also consumes a lot of computing resources. Thus, we choose to train our transformer model in a parallel way.  
Training NMT models comes with many forms of parallelization, including data parallelism, model parallelism, pipeline parallelism, and hybrid forms of parallelism. In data parallelism, a number of workers load an identical copy of the deep learning model. The training data are partitioned into non-overlapping chunks and fed into the model replicas of the works for training. In model parallelism, the NMT model is partitioned, and each worker loads a different portion of the NMT model for training. The workers that hold the input layer of the model are fed with the training data. By contrast, pipeline parallelism combines the two aforementioned forms of parallelism.  
Figure 3: The Transformer model architecture.  
```
1 classTrainOpt:-Optimwrapperthatimplementsrate.\"\"\"
3 def__init__(self_model_size,factor,warmup,optimizer):
5 self_optimizer=optimizerself_step=0
7 self_warmup=warmupself_factor=factor
9 self_model_size=model_sizeself_rate=0
11 defstep(self):-Updateparametersandrate\"\"\"
12 self_step=1
13 rate=self_rate()forpinself_optimizer.param_groups:
14p[r]=1
15 self_rate=rate
16 self_optimizer.step()
17 defrate(self,step=None):-Implement'rate'above\"\"\"
18 ifstepisNone:-step
19 returnself_factor*(self.model_size**(-0.5)*min(step**(-0.5),step*self.warmup**(-1.5)))
```  
**Figure 4. The implementation of the training optimizer.  
In this work, we aim to train our NMT model on diverse available computing resources such as laptop CPUs, desktop CPUs, and server CPUs with one or multiple GPUs. In addition, we mainly use data parallelism to speed up the training process. As shown in Figure 5, the entire English-Chinese parallel corpus is partitioned into a large number of batches, and each batch of the training data is distributed to a processor of a GPU or multi-core CPU. Thus, we have to ensure that we have sufficient batches so as to fully utilize the whole GPU processor or multiple GPUs. Our implementation is built based on the _DataParallel_ module of the _PyTorch_ framework. Note that we have to use suitable APIs to create the model and perform data movements between CPUs and GPUs. The batch size is set to be 32.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline GA-HDLAD & 99.25 & 98.30 & 99.25 & 98.75 \\ BDC-RSODL [55] & 99.10 & 96.89 & 99.15 & 97.98 \\ P2P-BDS Model [56] & 94.52 & 95.59 & 96.65 & 94.64 \\ FET [57] & 94.98 & 95.85 & 97.75 & 96.14 \\ DT [56] & 97.88 & 94.92 & 95.92 & 95.63 \\ Host-based [56] & 92.88 & 95.32 & 96.82 & 96.57 \\ FL-ANN Model [56] & 98.92 & 96.27 & 97.85 & 97.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison analysis of the GA-HDLAD system with other techniques. computational overhead, making it suitable for resource-constrained devices. The model's architecture also supports real-time anomaly detection, which is crucial for mitigating threats in IoT environments where low-latency response is essential. Future work will focus on further optimizing the model's scalability and ensuring seamless integration with diverse IoT platforms.
## 6 Types and Mechanisms of Removing Xenobiotics from Water
### Types of Lacase- Assisted Removing Xenobiotics from Water
#### 6.1.2 PAHs (Polycyclic Aromatic Hydrocarbons)
ostreatus_ lacase, the presence of as many as four compounds, the derivatives of benzoic acid, catechol, phthal acid and protocatechuic acid, was observed here, and they can be mineralized by fungal enzymes. Lacase from _Ganoderma_ sp. UH-M is a favorable enzyme for the bioremediation systems of PAH-polluted ecosystems [214]. _Lasiodiplodia thebromue_ fungus, isolated from a PAH-contaminated soil sample at the Beijing Coking Plant in Beijing, China, was able to degrade benzo[a]pyrene (BaP). _L. thoebromane_ belongs to the group of bottyrosphaeriaceous fungi and is the causative agent of storage rot in many fruits and tubers and a serious pathogen of many agricultural and horticultural crops [215]. It turned out that this fungus has the potential to use BaP as a sole carbon source. Within 10 days, up to 53% of BaP was degraded. During the biodegradation of BaP, the activity of lignin peroxidase and lacase was detected, and it should be noted that the activity of lacase was four times higher than that of lignin peroxidase. The addition of Tween 80 (TW-80), glucose and salicylic acid slightly increased the biodegradation of BaP. It has been reported in the literature that white rot fungi, which produce ligninolytic enzymes, can oxidize PAHs by generating hydroxyl radicals by donating one electron, which oxidizes the PAH ring and leads to the formation of quinones and acids, which are finally decomposed to CO\({}_{2}\) and H\({}_{2}\)O by hydrogenation, dehydration and so on. _L. thoebromane_ LAC can catalyze the one-electron oxidation of PAHs, such as anthracene (ANT) and BaP. The effectiveness of LAC increases in the presence of mediators, such as 1-hydroxybenzotriabol (HBT) or 2,2\({}^{\prime}\)-azinobis-3-ethylbenzthiazol-6-sulfonic acid (ABTS) [215]. Thus, the affiliation of fungi to different ecophysiological groups and the enzymes they produce, not only lacases, affect the completeness of PAH utilization [216].