esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Attention-Guided Transformer Module (AGTM)
#### 3.2.1 Attention Guiding Block--Top (AGB-T)  
AGB-T, which aims to locate and guide both local and global facial structures for the following transformer module, is illustrated in Figure 3a. It can be roughly divided into three parts: the Feature Distillation Network (FDN), the Hourglass Block, and the Channel Attention (CA) network. FDN aims to distill feature information from multiple levels of respective fields within the input feature maps. Firstly, a \(3\times 3\) convolutional layer is applied to the input feature maps to halve its number and select its internal principal components; then another \(3\times 3\) convolutional layer is used to restore the major information of the input feature maps by doubling the channel number. Then again, the original and the processed input feature maps are concatenated and sent to a full connection layer followed by a \(3\times 3\) convolutional layer to fully utilize the hierarchical features. After that, a CA network is applied to highlight the critical feature map channels, followed by a \(3\times 3\) convolutional layer to refine the distilled feature maps. Finally, a residual learning mechanism is applied to avoid the gradient vanishing problem. Following the distillation process of the FDN, the Hourglass Block [54], which has demonstrated its efficacy in generating spatial attention maps [13], is employed to capture landmark features of the human face, such as the eyes, nose, and mouth. Once the feature information is appropriately processed, the CA network [38] is utilized to select and emphasize feature map channels that contain a higher number of features. Thanks to the well-designed structure that wisely distills internal principal features and mutualizes spatial and channel attention, the proposed AGB can successfully guide the following transformer block to capture the essential part of the face images for better reconstruction results.  
After all the above, the final attention map for CMTB is generated by applying a \(3\times 3\) convolutional layer followed by a sigmoid function. Then, input feature maps are element-wise multiplied with the attention map and fed to the following transformer block with better extracted spatial features and promoted channel information. Moreover, a residual connection with a full connection layer is also applied between the input and the CA network output to stabilize the training process.
## 5 Discussion
### Experiment 3  
Tests 4 and 5 produced anchoring strength data under angled loading of the type that would be experienced in a current pushing the tethered AUV some lateral distance away from the anchor plug (as depicted in Figure 11), thus enabling analysis in support of question 3. The wire root array was specifically designed to address this situation; thus, the hypothesis was that the array would aid in anchoring strength. Experiment 3 testing consisted of ten angled pulls for each sediment condition, for a total of 30 pulls, for both configurations. Test 4, which was conducted using the full wire root array, produced mean anchoring strengths for conditions 1-3 of 21.9 lbf, 22.5 lbf, and 3.2 lbf, respectively. Test 5, which was conducted without the wire root array, provided average failure tensions of 15.5 lbf, 12.1 lbf, and 1.2 lbf, respectively.  
Three two-sample \(t\)-tests, assuming unequal variances, were conducted comparing the anchoring strength data for conditions 1-3 of Tests 4 and 5, with a significance level of 0.05. The first \(t\)-test, comparing condition 1 samples, resulted in a t-statistic of 3.365 and a two-tailed \(p\)-value of 0.00345. The second \(t\)-test, comparing condition 2 samples, resulted in a t-statistic of 7.841 and a two-tailed \(p\)-value of 2.786 \(\times\) 10\({}^{-6}\). The third \(t\)-test, comparing condition 3 samples, resulted in a t-statistic of 9.553 and a two-tailed \(p\)-value of 1.165 \(\times\) 10\({}^{-6}\). Thus, we conclude that a significant difference does exist between the mean anchoring strength of the mechanism with the full wire root array vs. without the array, in all three sediment conditions, at a 95% confidence level. When comparing the data to the Experiment 1 data, it is notable that angled anchoring strength with the full wire root array (Test 4) was greater for conditions 1 and 2 than under straight tension (Test 1), but, unexpectedly, was less than under straight tension for condition 3.  
A final observation made during Experiment 3 was that, as expected, anchoring strength increases under angled tension when the tether connection point is closer to the base of the anchor plug, likely due to the decreased moment arm. For convenience in Tests 4 and 5, the scale's hook was connected at the same point (the thumb ring at the top of the plastic screw) during the angled pulls as was used during the vertical pulls. This point is several centimeters higher than the tether would be connected (via the anchor spike swivel) in the proposed design. Eight additional pulls were conducted separately from Tests 4 and 5 in a modified configuration with the scale hook connected on a line tied just above the root ring in condition 1 sediment. This configuration produced a mean anchoring strength of 25.0 lbf, which appears higher than the mean anchoring strength for condition 1 of Test 4 (21.9 lbf), as expected.
# Future Internet Architectures on an Emerging Scale--A Systematic Review
## 4 Metaverse
### Technical Challenges of Blockchain in Metaverse  
As the number of users and transactions in the Metaverse increases, the blockchain network faces challenges with respect to scalability and transaction costs. The number of blocks in the blockchain increases as more users join the network, requiring significant computing resources [83]. Additionally, the shared transactions in the network result in users incurring high transaction costs, which are needed for validation. Future generations of blockchains will be needed to address these challenges and facilitate efficient data sharing in the Metaverse. There are several challenges that need prime focus to achieve this goal:  
1. Scalability  
Firstly, scalability is a major challenge and requires new technologies such as data sharding, sidechains, or off-chain solutions to improvise the network transaction throughput.
2. Data Interoperability  
The lack of decentralized platforms restricts the scope of users in terms of data interoperability. Furthermore, the transfer of NFTs to different environments is also limited. The use of virtual-world applications relies on their interconnectivity, which poses certain limitations. A cross-chain protocol serves as an alternate solution to ensuring data interoperability [84; 85]. This protocol allows for all forms of transactions in the Metaverse, without the need for intermediaries. Blockchains enable applications to connect seamlessly in the Metaverse, simplifying the experience for end-users. Data interoperability is critical, as different Metaverse projects may use different blockchain platforms and protocols, which can make it difficult for users and assets to move between these different platforms. Solutions such as cross-chain bridges must be developed to enable seamless data and asset transfers across the different Metaverse platforms.
3. Data Privacy  
The complexity of the Metaverse framework can be daunting for users, as bad actors can gain access to sensitive data. Attackers may use AI bots to pretend to be genuine users during transaction dealings. One potential solution to the challenge of dealing with large volumes of data is integrating reliable Metaverse information. Blockchain technology can give Metaverse users control over their data, using public/private keys that guarantee their ownership and protect them from third-party interference. Blockchain ledgers commonly feature an audit trail that ensures that the transactions in the Metaverse are reliable and absolute. The adoption of zero-knowledge proof technology in blockchains enables users to smoothly access actual data in the Metaverse while maintaining the ownership and privacy of their assets.
4. Data Security  
Finally, as the value of the assets and data in the Metaverse grows, security has become paramount. Robust security measures, such as multi-factor authentication, encryption, and smart contract audits, must be implemented to ensure the integrity and confidentiality of the data and assets in the Metaverse.
## 4 Materials and Methods
### Evaluation of the Angiogenic Effect of DFO-Loaded PLGA-PEG-PLGA NPs on HUVECs  
Briefly, HUVECs were cultured on the lower chamber of 6-well transwell plate at a density of 1 \(\times\) 10\({}^{5}\) cells/well, and DFO-loaded NPs solution of 1 mL was added into the upper chamber. After pipetting 3 mL of DMEM (Gibco) into the lower chamber, the upper inserts were placed in wells and hanging above the HUVECs monolayer. It is envisaged that DFO released from the NPs was able to diffuse through the insert membrane (5 \(\upmu\)m of pore size; Millipore, County Cork, Ireland) towards the HUVECs culture medium, and exerted effect on the cell monolayer. Meanwhile, wells without inserts, or inserts containing blank NPs were used as control. qRT-PCR was used to evaluate the transcript expression of VEGF, ANGPT-2, EGF and PDGF-BB at 24, 48 and 72 h, performed in procedures as mentioned above. The primers were synthesized as follows: VEGF: forward, 5\({}^{\prime}\)-CTACCTCCACCATGGCAAGT-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-ATGTTGACTCTGGG-3\({}^{\prime}\); ANGPT-2: forward, 5\({}^{\prime}\)-GGATGAGACAACGACAATG-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-GGACCACATGCATAAAACC-3\({}^{\prime}\); EGF: forward, 5\({}^{\prime}\)-AAGAATGGGGTAACCAGT-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-TGAAGTTGACTGCATTGAACC-3\({}^{\prime}\); PDGF-BB: forward, 5\({}^{\prime}\)-CTGGCATGAAGTGTGAGC-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-CGAAATGGTACCCCGATT-3\({}^{\prime}\); GAPDH: forward, 5\({}^{\prime}\)-GTCAGGTGACCCTGACT-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-AGGGGGTTCACATGCCAACTG-3\({}^{\prime}\).  
Afterwards, we investigated the tube formation activity of the HUVECs in the existence of DFO-loaded NPs. Briefly, 500 \(\upmu\)L of thawed Matrigel (BD Bioscience, San Jose, CA, USA) was transferred onto 24-well plate and incubated at 37 \({}^{\circ}\)C for solidification. Then, the solidified Matrigel was overlaid with HUVECs which had been starved in serum-free medium for 24 h, at a density of 3 \(\times\) 10\({}^{4}\) cells/well. Next, the blank or DFO-loaded NPs were placed into the wells. Microscopic images were taken from 5 randomly chosen fields of each sample 24 h later, and the tube formation activity was determined by calculating the number of complete capillaries which connected individual points of the polygonal frameworks.
# Probabilistic Modeling of Dust-Induced FSO Attenuation for 5G/6G Backhaul in Arid Regions
## 2 Experimental Setup
While particle concentration per unit volume was not directly measured, the visibility-based classification provides a practical proxy for dust density levels in the controlled environment. The visibility range \(V\) under atmospheric conditions can be estimated from the extinction coefficient \(\alpha\) and the visual threshold \(T_{\text{th}}\), as expressed by the following equation [20]:  
\[V=-\frac{10\log T_{\text{th}}}{\alpha}\quad\text{(km)}. \tag{1}\]  
The threshold value \(T_{\text{th}}\) is commonly set to 0.02 according to the Koschmieder law. The extinction coefficient \(\alpha\), which characterizes signal loss due to scattering and absorption, is computed using the following equation:  
\[\alpha=-\frac{10\log T}{4.343L},\quad\text{(dB/km)} \tag{2}\]  
where \(T\) denotes the transmittance of the optical signal and \(L\) (\(L\) = 90 cm in this work) is the physical length of the transmission path.
# The Effect of Meat and Bone Meal on Yield and Herbage Quality in Silage Maize
## 3 Results and Discussion
### Nutritional Value of Maize Herbage
In the first year of the experiment (2014), characterized by precipitation deficiency, the concentration of NDF was lowest, and the content of CP and NFC was highest (Table 3). Similarly to the findings of [PERSON] et al. [45], the NDF content was higher, and the CP content was lower in years with higher precipitation levels, i.e., higher water availability to plants. Different results were presented by [PERSON] et al. [47], who noted an increase in the concentration of CP and water-soluble carbohydrates in sorghum forages with increasing irrigation levels. Differentials in the chemical composition of plants could result from physiological processes that occur in response to different availability of water and fertilization levels, mostly the associative effects of chemical constituents, the rate of photosynthesis, nutrient transport, and N metabolism in plants. After maize flowering, considerable amounts of nutrients are gradually translocated to the leaves [45; 48]. The duration of photosynthesis is associated with N uptake from the roots and N concentration in plants, i.e., the CP content of maize herbage [5; 48]. Similarly to yield, the chemical composition of herbage varies across plant growth stages. In this experiment, the CP content was higher, and the CF content was lower than in the work of [PERSON] et al. [3]. An interaction was found between the type of fertilizer and the year of the study for CF content (Figure 3). In the growing season of 2019, maize yield was higher than in 2014, and herbage had higher CF content. However, the observed increase in CF content varied depending on the type of fertilizer. The CA content of herbage was lower in the present study than in experiments conducted by other authors [3; 41]. According to [PERSON] et al. [2], a delay in maize harvest from the early milk stage to the dough stage leads to a decrease in crude ash content because plants harvested in earlier growth stages contain higher concentrations of mineral and organic nutrients. Figure 2: Interaction between fertilization and year of study for the dry matter (DM) content of maize herbage; a, b, c within a year (\(p\leq 0.05\)).
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 4 Results of the Proposed Algorithms  
In this section, we present the results of the optimized algorithms for RIS-assisted MISO and MIMO systems.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 2 Traditional Methods of Modulation Recognition
### Feature-Based Recognition Algorithms
According to Table 1, we can see that both the LB-based recognition method and the FB-based recognition method need to rely on experts' experience to extract signal features and need to set thresholds and rules when designing classifiers. Such methods are difficult to meet the recognition requirements of modern, complex communication systems. Therefore, how to improve the modulation recognition rate in complex communication systems has become an important problem that scholars need to solve. In recent years, the development of deep learning has brought a turning point to solving the problem of low accuracy of traditional modulation recognition. Many researchers have tried to apply deep learning algorithms in the field of modulation recognition, and now they have achieved many excellent results. Section 3 focuses on deep learning-based modulation recognition methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline Modulation identification & Have a complete theoretical foundation & The derivation of the likelihood function is complicated, and the amount of calculation is large \\ method based on the & The classification effect is very good & Poor applicability \\ likelihood ratio & Better performance at low & Requires a small amount of prior knowledge \\  & signal-to-noise ratio & Requires a lot of prior knowledge \\ \hline Feature-based modulation & Simple theoretical analysis & The identification system is complex \\ identification & Features are easy to extract when the & There is no complete theoretical basis \\  & signal-to-noise ratio is high & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of advantages and disadvantages of traditional methods for automatic modulation identification. Figure 4: Summary of signal features and classifiers.
## 2 Problem Description and Formulation  
There are two critical things that need to be completed between receiving a user request and completing a task. The first one is observation scheduling, which arranges the observation scheme according to the relationship between the available satellite resources and the observation target requested by the user. The second one is downlink scheduling, which transfers observation results from multiple satellites to ground stations as quickly as possible. In many observation tasks, multiple satellites will obtain a large amount of observation data once the observation task completed. Nevertheless, restricted to the number and location of ground stations, it is necessary to schedule the multi-satellite downlink to complete the delivery of the observation results.
# A Study on the Design of Fog Computing Architecture Using Sensor Networks
## 3 Proposed System
### Communication Control by Frequency of Use  
When data is transferred from a sensor to the proxy server, data information is processed in the fog area. The processed data is transmitted to the cloud server and printed to a device or a user by an order created by the cloud server or the proxy server. Data processed in fog computing is directly transmitted to cloud communication. However, such communication with the cloud server wastes cloud server capacity and power consumption due to unnecessary data communication.  
To solve the issue, this paper proposes a structure, on the basis of frequency of use in data information processing process, as indicated in Figure 11. First, data is received. Next, we measure frequency of data use. If the measured frequency of use meets the standard, we skip communication with the cloud server, create control information and the proxy server and transmit it to sensors or devices. If the measured frequency of use does not meet the standard, we communicate with the cloud server and receive information from the cloud server, creating control information accordingly and sending sensor control information to the proxy server. If the standard is drawn from calculations, information satisfying the standard is directly transmitted to the proxy server, without communication with the cloud server. Therefore, the number of communications with the cloud server and power consumption can be reduced. In addition, as communications with the cloud server happen only when data does not satisfy the standard and has a problem, unnecessary communication does not occur. As a result, the amount of data (load) received by the cloud server is reduced.
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
## 1 Introduction
Irradiation may be conducted under various conditions, including the presence of an inert gas atmosphere, room temperature, or cryogenic conditions. Two types of ionizing radiation are commonly used in the sterilization process, i.e., \(\gamma\)-irradiation and EB. The main advantage of irradiation with \(\gamma\) photons is its high penetration power. Gamma irradiation may be applied for non-uniform and high-density products. Like \(\gamma\)-ray, sterilization with EB is also instantaneous with high-dose delivery. However, it should be considered that sterilization efficiency varies depending on the material used. Overall, radiation sterilization may be suitable for heat-sensitive drugs or drug carriers. It is a fast and effective process, providing high penetration power through the material. Nevertheless, specific undesirable changes caused by ionizing radiation may occur. Radiation-induced chain scission may lead to polymer fragmentation and consequently a decrease in average molecular mass (\(M_{\text{n}}\)) [14]. Moreover, the diminution in \(M_{\text{n}}\) is expected to increase with increased irradiation dose. In turn, this may alter the physicochemical properties of drug carriers [15]. It is, therefore, essential to properly validate the sterilization procedure concerning any potential effects of irradiation, irrespectively, for each type of sterilized product. In this study, we investigate the influence of \(\gamma\)-rays and EB (25 kGy) on the sterilization process of PTX-loaded polymeric nanoparticles based on self-synthesized polymer matrices. For this purpose, biodegradable copolymers of L-lactide (L-LA) and glycolide (GA) were synthesized in the presence of a bismuth catalyst system, and the effect of irradiation on this polyester DDSs was examined as well. There is a number of already-published studies on the influence of ionizing radiation on polymeric matrices. However, only a few studies have dealt with the use of \(\gamma\) and EB irradiation for the sterilization purposes of polyester micro-/nanoparticles containing PTX (e.g., [PERSON] et al. [16], who analyzed PTX-loaded PLGA microparticles (LA:GA = 50:50), and [PERSON] et al. [17], who analyzed PTX-loaded polylactide microspheres). Our study tries to fill this gap. What is more, these articles are based on the polymers of different monomer composition, therefore cannot be directly compared.