esa-sceva/satcom-chunk-collection huggingface dataset:
# A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems
###### Abstract  
Central data systems require mass storage systems for big data from many fields and devices. Several technologies have been proposed to meet this demand. Holographic data storage (HDS) is at the forefront of data storage innovation and exploits the extraordinary characteristics of light to encode and retrieve two-dimensional (ZD) data from holographic volume media. Nevertheless, a formidable challenge exists in the form of 2D interference that is a by-product of hologram dispersion during data retrieval and is a substantial barrier to the reliability and efficiency of HDS systems. To solve these problems, an equalizer and target are applied to HDS systems. However, in previous studies, the equalizer acted only as a linear convolution filter for the received signal. In this study, we propose a nonlinear equalizer using a convolutional neural network (CNN) for HDS systems. Using a CNN-based equalizer, the received signal can be nonlinearly converted into the desired signal with higher accuracy. In the experiments, our proposed model achieved a gain of approximately 2.5 dB in contrast to conventional models.  
convolutional neural network (CNN); deep learning; detection; equalizer; holographic data storage; machine learning; remove interference +
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: _Applied Sciences_
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## 2 System Model
### Uplink Transmission Model of Cooperative OMA Scheme  
To fully utilize the satellite resources of the heterogeneous SATCOM network, the beam-edge user has an option to access the GEO satellite. That is, the beam-edge user and beam-center user are served by GEO and LEO nodes with different spectral resources, which can be viewed as cooperative OMA. In this case, the received signals of \(x_{1}\) and \(x_{2}\) can be written as  
\[y_{1}=h_{1}x_{1}+n \tag{6}\]  
and  
\[y_{2}=g_{2}x_{2}+n. \tag{7}\]  
Then, the SNR of both users can be calculated as  
\[SNR_{1}^{{}^{\prime}}=\frac{|h_{1}|^{2}(1-\alpha)P}{\sigma^{2}} \tag{8}\]  
and  
\[SNR_{2}^{{}^{\prime}}=\frac{|g_{2}|^{2}\alpha P}{\sigma^{2}}. \tag{9}\]
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 9 Discussion
### Extension to Hyperspectral Applications  
Although the work reported so far was conducted on multispectral rather than hyperspectral data, the number of spectral bands (dimensionality) is not a limiting factor for either CloudScout or the domain adaptation methods. For example, adding one spectral band requires adding one input channel to the first convolutional layer of CloudScout, although more convolutional layers and weights per layer may need to be added to maintain the same performance. The proposed domain adaptation methods will work on any CNN architecture and are independent of the number of bands used. The preceding statements, however, are not meant to downplay the challenges associated with the infamous \"curse of dimensionality\" or Hughes phenomenon [85]. Furthermore, as energy is spread out over more bands, the signal-to-noise ratio (SNR) per band suffers [86]. Depending on the sensor characteristics and mission requirements, denoising (i.e., noise reduction) may be a necessary preprocessing step before cloud detection [15]. In summary, enhancing the proposed methods to process hyperspectral data requires a straightforward albeit nontrivial extension of said methods. Nevertheless, representative or real-world performance of the enhanced/extended methods on hyperspectral data can only be evaluated using a more capable VPU than what is available for our current investigation.
## 2 **Introduction**
[PERSON] [11] found the structure and operating parameters of a waste-heat-recovery vertical tank corresponding to a sintering machine with an annual output of 2.6 million tons through an analytical calculation, using the values of hot flue gas exergy and gas resistance loss as evaluation indicators. The above studies were often based on the porous media model; they assumed that the particle size and voidage distribution of the sinter bed were uniform and regarded the sinter bed as static or uniformly moving down in the vertical direction. However, in the actual production process, the particle size and voidage distribution in vertical cooling furnaces are not uniform due to distribution segregation, and it is difficult to realize the overall downward movement of sinter. This makes the above hypotheses different from the actual situation. The operational result [12; 13] of a Meishan Steel vertical cooling furnace also shows that the boundary effect is serious due to the segregation of sinter distribution in the vertical cooling furnace. The sinter in the sidewall area is cooled transiently due to a greater quantity of cooling gas, while the sinter in the central area is not cooled due to a lower quantity of cooling gas, resulting in the uneven cooling of the sinter, which seriously affects the improvement of the sensible heat recovery rate of the sinter. Based on this, the authors' team proposed a Venturi furnace type [14] from the perspective of optimizing the shape of the vertical cooling furnace cavity and found that this furnace type can improve the gas-solid contact characteristics, strengthen the gas-solid convective heat transfer, and improve the uniformity of the temperature distribution of sinter and cooling air along the width direction at the cost of increasing the pressure drop of cooling gas. Further research has been conducted on the improvement effect of this furnace type on the distribution of sinter and the gas-solid flow inside the furnace [15]. It has been found that this furnace type can increase the porosity of the sinter bed in the middle zone of the furnace cavity, maintain the integrity of the sinter flow, and improve the uniformity of the cooling airflow distribution. In order to further increase the heat transfer between gases and solids while controlling the increase in the gas pressure drop, this study takes the Meishan Steel vertical cooling furnace as the research object based on the team's previous research. Firstly, combined with the site conditions, the structural parameters and value range of the Venturi furnace were determined. Then, the orthogonal experiment was designed according to the relevant factors and levels. Finally, taking the sensible heat recovery of the sinter and the pressure drop of the cooling gas as the main index and the integrity of sinter flow as the secondary index, the structural parameters of the Venturi furnace suitable for the Meishan Steel vertical cooling furnace were studied in order to provide a theoretical basis and design scheme for optimizing the Meishan Steel vertical cooling furnace.
## 2 Comparative Transcriptomic Responses of _L. plantarum_ WCFS1 to Different PPCs  
Several studies have addressed the transcriptomic responses of _Lactobacillus_ spp. to individual PPCs [23; 24; 25; 26; 27; 28; 29]. The transcriptomes of the model strain _Lactiplantiobacillus plantarum_ WCFS1 exposed to different representative PPCs, including a hydroxycinnamic acid (\(p\)-coumarin acid (\(p\)-CA)) [25], a hydroxybenzoic acid (gallic acid (GA)) [26], a stilbene (resveratrol (RSV)) [27], a seciridoid (oleuropein (OLE)) [28], and a phenylethanol (hydroxytyrosol (HXT)) [29] have been revealed using DNA microarrays. The standardized experimental setup, applying stresses for 10 min to the same strain, permitted a reliable comparison among the different responses to these PPCs. The presence of PPCs affected hundreds of genes, and the largest transcriptome variations were observed upon exposure to OLE (358 genes involved), while exposure to GA had the least impact (40 affected genes). Figure 1 shows an overview of the extent of similarity among the responses of _L. plantarum_ WCFS1 to \(p\)-CA, HXT, OLE, and RSV using the Venny 2.1 tool [30]. The assembled diagram shows the percentages of _L. plantarum_ WCFS1 genes that overlap between the responses to these PPCs. When considering paired responses, the highest degree of coincidence was between OLE and \(p\)-CA where 25% and 32% of the genes, respectively, overlapped with respect to each other. The response to GA was also compared to the mentioned four phenolic compounds (not shown) displaying the best match with the response to \(p\)-CA (52.5% of the GA responsive genes were also regulated by \(p\)-CA). Comparative transcriptomic analysis of the specific responses to these individual PPCs revealed functional gene categories that highlighted the importance of adaptation of _Lactobacillus_ cell surface properties, carbon and nitrogen metabolism, stress responsive pathways, and transport functions. The differentially expressed genes are incorporated and discussed in more detail as part of the sections below.
## 5 Results  
The produced SUHI maps were fitted to a Gaussian surface after masking out the cloudy pixels, and then the parameters describing the magnitude (\(a_{0}\)), the spatial extent (\(a_{x}\), \(a_{y}\)), the orientation (\(\phi\)) and central location (\(x_{0}\), \(y_{0}\)) of the heat island pattern were computed. An area around the Milan city center of 39 km\({}^{2}\) was selected, comprising the outskirts, neighboring municipal districts, industrial estates and rural areas. The application of the algorithm to both MODIS and Landsat TM images was analyzed visualizing the 2D (maps with footprint) and the 1D (along the two footprint axes) fitting results.  
First, two issues have to be pointed out in the performance assessment:  
- The initial guess parameter estimation performed by an image-based multiple linear regression was advantageous with respect to _a priori_ fixed parameters. In fact the latter choice was implemented as a test with different initial parameter combinations and instable results for several images were found: for instance, the iterative optimization was found to occasionally fail to converge, returning infinite or undefined results. This never occurred with the regressed initial parameters. Also, another possible choice of fixing the initial parameters by means of visual inspection of each image is not a straightforward way, yielding to a not automatic tool.  
- Cmputational time: instantaneous for the MODIS image (39 \(\times\) 39 pixels), few seconds for the Landsat TM image (1307 \(\times\) 1307 pixels). These performances were obtained by using a 3 GHz CPU.
## 6 PLGA Nanoparticles Functionalization
### BBB Transcytosis
#### 6.2.3 Carrier-Mediated Transport  
Besides receptors, active transport in the BBB may also be exploited for brain targeting. The glutathione (GSH) transporter is highly expressed at the BBB [97]. This endogenous tripeptide thiol acts as an antioxidant and helps to protect cells from ROS [179].  
Since there is a large number of GSH transporters at the BBB, GSH conjugated onto the PLGA NP surface is expected to bind to these transporters and increase the number of NPs at the BBB interface [179]. However, although the mechanism of GSH transportation through the brain cells was proven to be performed through a specific mechanism [180], the exact molecular mechanism remains to be elucidated.  
[PERSON] et al. [105] developed GSH-functionalized PLGA-PEG NPs to be loaded with curcumin. The resulting NPs, of mean size between 149 to 180 nm and coated with both PEG and GSH, increased improved drug uptake in vitro. Moreover, the internalized curcumin was found localized in almost every cell and their components, meaning that the GSH surface-modification allowed better cellular trafficking of the formulation. Novel insights into the development of effective delivery systems able to escape lysosomal degradation were described, therefore increasing the therapeutic effect of drugs useful for the treatment of AD [105]. However, in vivo experiments are still required to draw more general conclusions.
## 3 Overview of IoT System Security Applications in the Industrial Field
### Comparative Analysis in the Case of Worker Safety
Kenzen also factors in personal details like height, weight, age, medical history, and environmental conditions to assess each worker's risk of heat-related issues, calculating their individual heat susceptibility for more tailored safety measures. Heat susceptibility refers to an individual's sensitivity to heat, which increases their risk for heat-related injuries or illnesses. Workers can be categorized as low, moderate, or high risk. Machine learning algorithms can detect, adapt to, and notify EHS managers of site-specific changes that may not be easily noticeable daily. Moreover, they can monitor vulnerable workers more closely during extreme heat and adjust workloads based on each person's risk level [94]. Recently, Garney Construction, a leading water and wastewater contractor, used these insights to help managers create customized safety procedures for their worksites across the U.S. [95]. To compare the different solutions proposed in the literature, Table 2 highlights the main characteristics of the IoT platforms used for monitoring workers' health in a synthetic way. The table presents five main elements, among which are the IoT entity or the object or the set of elements that contribute to creating the smart platform, the communication technology (therefore, the basis with which all the elements interconnect for the exchange of data), the recipient of the IoT device, the algorithm used by the authors for the processing of the data previously acquired, and finally, the cost-benefits and implementations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **IoT Entity** & **Communication Technologies** & **Detected/Inferred Quantity** & **Detection Algorithm** & **Cost-Benefits and Implementations** \\ \hline X. [PERSON] et al. [54] & Sensor node & BLE & \begin{tabular}{c} Worker stationary \\ time \\ \end{tabular} & SAE & \begin{tabular}{c} Plug-and-play system, \\ cost-effective fashion \\ \end{tabular} \\ \hline [PERSON] et al. [55] & Sensor node with camera & N.A. & \begin{tabular}{c} Machine trajectory \\ and applied \\ pressure \\ \end{tabular} & \begin{tabular}{c} Swarm \\ optimization–Elman \\ neural network (PE.) \\ ADI-LSTM \\ \end{tabular} & \begin{tabular}{c} Challenging to fulfil \\ global performance \\ optimization standards \\ \end{tabular} \\ \hline [PERSON]. [PERSON] et al. [58] & \begin{tabular}{c} Helmets for \\ mining workers \\ \end{tabular} & BLE & \begin{tabular}{c} Hand gestures and \\ environmental \\ parameters \\ \end{tabular} & \
\begin{tabular}{c} Python code and \\ ARM Cortex-M \\ \end{tabular} &
\begin{tabular}{c} Low cost, rapid \\ implementation of \\ smart PPE \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of references previously discussed related to the IoT-based workers’ safety applications.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Preliminary Formation for the Proposed Work
#### 3.1.6 Scores  
In the network, each node governs a routing table that is used for sending its data. For each source to its destination, a sequence of nodes is kept in this table. The routing table keeps the sequence of nodes in each possible route for a destination. A set, \(R\), can be defined to show all the possible routes for node \(i\) via the presence of intermediate nodes. Each route can be denoted by \(r\) with a sequence of nodes.  
\[R_{i}=\left\{r_{1},r_{2},r_{3},\ldots,r_{j}\right\} \tag{5}\]  
The nodes present in the routes can be distinguished by their importance using a scoring mechanism. The routes can be determined by evaluating the scores of the nodes.  
The efficiency of a route can be calculated by considering the nodes' density, that is, the number of CNs and their energies at each hop level towards the sink. Each transmission of the data packets from a source consumes the energies of all involved nodes. The deduction of energies induces a variation in the scores of nodes. The score of each node \(i\) can be calculated by its energy level, the sum of the energies of its CNs, and their size \(m\) at time \(t\). The score of node \(i\) at time \(t\) can be derived by Equation (6) as follows:  
\[\lambda_{i}^{t}=\frac{E_{i}^{t}}{E^{max}+\sum_{j=1}^{m}E_{CNj}^{t}}\times(m+1) \tag{6}\]  
The high level of energy of node \(i\) and/or a higher number of CNs leads to a higher value of \(\lambda\). Additionally, CNs' energies also influence this value. The higher-level CN energies reduce the value of \(\lambda\). For an exceptional case in which a node does not have any connected or dead CNs, \(\lambda\) is calculated by dividing the energy of node \(i\) by the maximum level of energy, as follows:  
\[\lambda_{i}^{t}=\frac{E_{i}^{t}}{E^{max}} \tag{7}\]  
Since each route may have multiple nodes, the \(\lambda\) of relay nodes is considered at each level. The main aim of considering the node density is to reduce the load on single or scarce nodes. Once the system starts putting the load on the densely located nodes, it is obvious that at some following stages, some of the nodes will exhaust their energies and will ultimately no longer operate. The load is finally diverted to other nodes.  
Algorithm 2 is used to calculate the \(\lambda\) values for the nodes. This algorithm uses a subroutine, CALCULATE_CN(ID), to get the list of all the CNs for a specific node. The number of all CNs is retrieved and then according to Equation (6), these CNs are processed using a simple loop.
# A LoRaWAN Network Architecture with MQTT2 MULTICAST
## 5 MQTT Enhancements in the SDN Segment
### MQTT Proxies  
As described in Section 4, we introduced MQTT proxies in the edge nodes of the SDN network in our architecture. MQTT proxies reduce the amount of MQTT traffic within the SDN network and its latency.  
For this, MQTT proxies act as MQTT brokers with respect to the clients directly connected, implementing the same security mechanisms, e.g., using MQTT over TLS/SSL. In this architecture, several entities of the LoRaWAN network will use the proxies to replace the broker: the gateway bridge, the network server and the application server. In addition, the data published through MQTT can be queried by external entities (\"external agent\" in Figure 3). To do this, these MQTT subscribers will in turn connect to another MQTT proxy that will act as their broker and exchange the necessary information with the other proxy using UDP. The advantage of using UDP is that latency is reduced (several Round-Trip Times (RTT) in case of using TCP) and it will allow us to multicast the messages. Furthermore, although it has not been included in our proof of concept, the solution given in [13] can be used to guarantee the reliability of UDP over SDN. Moreover, MQTT over UDP may utilize DTLS (Datagram Transport Layer Security) in order to secure the communications, as done for MQTT-SN [14].  
Specifically, the proxies will forward subscription messages (_Subscribe_)--when a new topic appears that did not have previous subscribers in that proxy; publication (_Publish_)--for all messages published in topics that have a subscriber in the destination proxy; and _Disconnect_--when the last subscriber to that topic in that proxy is disconnected. In this way, the proxies will have the necessary information to forward the messages of active topics.  
Additionally, they will employ our MQTT2 MULTICAST protocol (see Section 5.3), which allows the proxies to map the MQTT topics to their corresponding multicast IP addresses.
# Collagen Nanoparticles in Drug Delivery Systems and Tissue Engineering
## 2 Methods of Collagen Nanoparticle Preparation
### Emulsification/Solvent Extraction  
The drug is dissolved in an organic protein or polymer solution and an emulsion system is formed by mechanical agitation. The polymer and drug are precipitated in droplets and NPs are formed by extracting the solvent by evaporation [33; 34]. The double emulsion method has been used currently for greater efficiency of encapsulation [35]. In this method, a surfactant is used in dispersion for emulsion stabilization. The organic solvent is then taken out to conserve the NPs in an aqueous buffer. The double emulsion method of nanoparticle preparation is quick and economical. A stabilizing agent and surfactant are required to ensure the emulsion is stable as it is normally unstable thermodynamically. They affect interactions between the drug and matrix along with the drug release rate [31]. More specifically, at room temperature, an aqueous phase of collagen along with water and a hydrophilic surfactant, an organic phase consisting of a surfactant of lipophilic nature and a solvent miscible in water are mechanically agitated, forming a homogeneous emulsion system. This emulsion combined drop by drop with hot oil results in collagen nanoparticle fabrication [36]. An experiment conducted by [PERSON] et al. was to develop new stable Pickering emulsions that are stabilized with chitosan/collagen peptide NPs for possible cosmetic applications. Their study aims to understand the stability, microstructure and rheological properties of the Pickering formulations produced. Since both the polymers utilized in nanoparticle production have skin benefits, the study investigates the fate of the NPs after skin application by tracking their skin distribution following the penetration of the emulsion droplets. During the complex chitosan/collagen peptides nanoparticle formation, the long chitosan chains fold around the collagen peptides and form a new phase complex having different properties from that of the individual components [37]. The process of Emulsification/Solvent Extraction has been illustrated in Figure 1.