esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization and Selection of the Prototype MCs
#### 3.1.2 Proof of Homogeneous Gelatin Distribution on MC Surfaces  
Gelatin is essential for cell attachment in a serum-free medium. Therefore, it was crucial to establish how the porcine gelatin proteins are distributed on the surface of the MC prototype _BR44_. For this purpose, the CF633 fluorescent dye was used to conjugate the proteins to check their presence and distribution (porcine gelatin) on the surface of _BR44_ (Figures 3 and 4). The BR13C MC prototype, which does not contain proteins and is made of pure PLGA (see Supplementary Material Figure S4), was chosen as a negative control for this test. Various commercial MCs either with surfaces (peptides or primary amines) that react to the CF633 dye or with a protein coating (Collagen I, Fibronectin, Gelatin) on their outer shell were used as a positive control. The analysis of these MCs was also intriguing because it demonstrated how some companies solved the problem by coating their microspheres' surface layer to allow adhesion and cell growth. The results obtained with these commercial MCs are shown in Supplementary Material Figure S5. The CF633 fluorescent dye (molecular weight \(\sim\)820 g/mol) reacts with the accessible primary amines of proteins, highlighting their presence and distribution on the MC _BR44_ prototype's surface. As shown in Figure 3, the porcine gelatin proteins are homogeneously distributed and present at a high density on the carrier's surface. Furthermore, the images demonstrate and confirm that this MC is also highly porous on the inside.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Concentration (g/mL)**} & \multicolumn{2}{c}{**Particle Size Distribution (PSD) (\(\upmu\)m)**} & \multirow{2}{*}{**Theoretical Surface (cm\({}^{2}\)/g)**} \\  & \(\mathbf{X_{10}}\) & \(\mathbf{X_{50}}\) & \(\mathbf{X_{90}}\) & \\ \hline
0.034 \(\pm\) 0.0002 & 116.76 \(\pm\) 1.93 & 166.41 \(\pm\) 3.44 & 221.55 \(\pm\) 3.56 & 355.77 \(\pm\) 6.04 \\ \hline \end{tabular}
\end{table}
Table 1: Biophysical properties of _BR44_. Data represent the mean of six different batches of _BR44_.  
Figure 2: Particle size distribution (PSD) of _BR44_. The figure shows the cumulative and density distribution of one batch of _BR44_ as a representative example. \(\mathbf{X_{10}}\) = 116 \(\upmu\)m, \(\mathbf{X_{50}}\) = 166 \(\upmu\)m, \(\mathbf{X_{90}}\) = 220 \(\upmu\)m.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 6 Experimental Setup  
### Implementing Details for All-Bands BadNets Attack  
We applied the all-bands BadNets attack to the EuroSat dataset, poisoning 10% of the training samples and choosing 'River' as the target class. The DCNN model was trained for 100 epochs. We embedded a square trigger in the bottom right corner of all image bands, with a size of \(n_{t}\times n_{t}\) and varied \(n_{t}\) from 2 to 10 to test the impact of the trigger size on the performance of the attack. The structure of the DCNN was defined in [57] and used with publicly available deep learning backbones, such as ResNet-56 [21] and EfficientNet [38]. It consists of four convolutional layers, as shown in Figure 4, with 32, 64, 128, and 256 2D convolutional kernels in the first through fourth convolutional layers, respectively. All the convolutional kernels have a spatial size of \(3\times 3\), and all the convolutional layers contain a max-pooling layer with a stride of \(2\times 2\). These layers are followed by a flattened layer and a fully-connected layer with 512 neurons. Each layer employs the \"ReLu\" activation function [53], with the final layer using a Softmax activation function comprising ten neurons to classify the ten classes.
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 4 Radar and Remote Sensing Applications
### Monitoring Earthquake Damages  
[PERSON] et al. described the main features of remote sensing and optical satellite techniques for damage detection in rural areas. Different methods have been used either for damage assessment classification or for Earth surface measurements [73]. Satellite remote sensing can provide information that can be used to create a systematic framework providing deep knowledge of the Earth's surface. The geospatial information produced is then used as an input for addressing decisions in relation to comprehensive management, risk, and disaster assessment [74]. SAR can record the backscattering coefficient and to measure the condition of the outer Earth's surface. SAR can also measure damaged areas in disasters caused by earthquakes, flood, and forest fires [75; 76]. [PERSON] et al. used Interferometric Synthetic Aperture Radar (InSAR) to find clear maps of three-dimensional surface displacement fields during the 1999 Mw 7.1 hecr mine earthquake in Southern California [77]. Radar imaging capability features usually represent a significant aid to the rescue teams in the localization of the survivors in an earthquake [78].
## 5 Concluding Remarks  
This communication aims to draw academic and industrial attention on the recent surge of research interest in reconfigurable intelligent surfaces (RIS), which inception has occurred a few years ago but without acknowledging the backbone technologies that originated from a few decades ago, i.e., liquid crystal on silicon (LCOS) and liquid crystal reflectarray antenna (LCRA). As such, a host of valuable insights can be drawn from existing knowledge in LCOS and LCRA, thus inspiring the technical realization of LC-RIS for 6G networks.  
By leveraging LC pixels to control the phase of reflected waves continuously, the potential applications of LC-RIS technology are vast, e.g., improving wireless communication systems (including 5G and beyond) and imaging systems (cameras and sensors) and building smart homes and smart cities with intelligent surfaces that can adapt to changing environmental conditions. Therefore, it is also expected that LC technology will gain more global traction due to the expanding portfolio of the \"new\" RIS concept and the booming RIS industry. Overall, the new paradigm for a future internet roadmap with LC-RIS will require collaboration and innovation not only within the academia but also across a wide range of stakeholders, including governments, industry, and civil society.  
This research received no external funding.  
Not applicable.  
The author is grateful for the support from the Beijing Institute of Technology Research Fund Program for Young Scholars.  
The author declares no conflict of interest.
# Simulation Study of Microwave Ablation of Porous Lung Tissue
## 2 Materials and Methods
### Calculation Method  
A two-dimensional axisymmetric model of the microwave antenna and lung tissue is established in this paper. The electromagnetic wave equation, heat transfer equation, and transient momentum equation are coupled and solved by using COMSOL Multiphysics 5.5 software. The dielectric, thermal properties and porosity of the lung tissue were assumed to remain constant. In the simulation, according to the characteristics of the electromagnetic field and temperature field, the boundary conditions are set, the grid is divided, and the solver is set.  
For transient numerical simulation, it is necessary to determine the number of meshes used in the calculation, independent of the calculation results. Therefore, the mesh independence is verified, and the optimal mesh number is determined. Temperature is a key parameter in the numerical simulation of microwave ablation of lung tissue. Therefore, we take the temperature distribution at 5 mm of the antenna to verify the grid independence. The grid is divided into 8451, 14,976, 20,471, 27,234, and 32,371 by automatic partition. Finally, we choose the 20,471 grid number for calculation. The electromagnetic field module is set to a steady state, and the frequency domain, and the output microwave frequency is set to 2450 MHz. For the heat transfer model, the temperature changes with time, so it is set as a transient solver, the solution time is 360 s,the time step is set to 1 s. Table 1 lists the values of the relevant properties of lung tissue, tumor tissue and blood in the simulations [21; 24]. Table 2 lists the values of the relevant parameters of the microwave antenna used in the simulations.
# Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm
## 6 Discussion
On the other hand, a small number of clusters leads to worse MAR values, which can be justified because a smaller number of clusters implies that each cluster is bigger in radius. This leads to more data being considered normal, as it is easier for new data to lie inside the learned clusters. Additionally, the added features allowed for higher dimensional centroids, and more information to be used, which resulted in a slightly better \(F_{1}\) score (a 0.01 increase) with a decrease in MAR at the expense of an increase in FAR. The timing results for CDCA were also analyzed in _SKAB_, which leads us to the conclusion that an online application is possible, but the real-time classification is not. Despite presenting a fast classification, it is not suitable for a streaming problem. CDCA cannot immediately classify data points, with each point requiring on average 7-14 more data points to be received in the classification of any given point. Despite this limitation, the millisecond delay is promising, in that, in the worst-case scenario, the algorithm requires, on average, 3.8 ms to classify a point for an average of 14 points delay. This timing is obtained by considering only processing time, i.e., discarding the time between processing in one data point and receiving the next. For the _M2M using OPC UA_ dataset, the results are promising, given that the obtained AUC of ROC is slightly better when compared to the iDCA [41]. Furthermore, CDCA required no expert knowledge when compared with the iDCA. However, as mentioned before, CDCA requires an initial training phase to define what is a standard system behavior, whereas iDCA does not. This is a limitation since, during training, the algorithm does not perform any detection. Additionally, it may be challenging to have only normal data available for training purposes in a real case scenario.
# When 5G Meets Deep Learning: A Systematic Review
###### Abstract  
This last decade, the amount of data exchanged on the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high-speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements, and each of these technologies brings its own design issues and challenges. In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contribution, this paper presents a systematic review about how DL is being applied to solve some 5G issues. Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration.  
**Keywords:** the next generation of telecommunication (5G); deep learning; reinforcement learning; systematic review; cellular networks +
Footnote †: journal:
# Bioactive Compounds in Breast Meat of Broiler Chickens Fed with Black Soldier Fly Wholesmeal
## 2 Materials and Methods
### Birds, Diets, and Experimental Design
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Ingredients (\%)** & **Grower Ration** & **Finisher Ration** & **BSFL** \\ \hline  & **(0–21 Days)** & **(22-35 Days)** & **(22-35 Days)** \\ \hline Corn & 61.00 & 65.45 & \\ Soybean meal & 26.89 & 29.95 & \\ Wheat bran & 10.05 & 2.75 & \\ Dicalcium phosphate & 1.45 & 1.30 & \\ Salt & 0.30 & 0.30 & \\ DL-methionine & 0.11 & 0.05 & \\ Vitamin Premix * & 0.10 & 0.10 & \\ Mineral Premix ** & 0.10 & 0.10 & \\
**Minerals, \%** & & \\ Calcium & 1.60 & 0.85 & 1.47 \\ Phosphorus & 0.60 & 0.48 & 0.95 \\ Magnesium & 0.25 & 0.20 & 0.54 \\
**Chemical composition, \%DM** & & \\ Crude Protein & 21.20 & 18.89 & 44.74 \\ Crude Fat & 5.60 & 4.5 & 19.80 \\ Crude Fiber & 2.85 & 3.06 & 22.70 \\ Crude ash & 5.5 & 5.0 & 12.76 \\ \hline \hline \end{tabular}  
* Composition of the vitamin premix (per kg of diet): vitamin A = 5500 IU; vitamin D3 = 1100 IU; vitamin E = 10 IU; riboflavin = 4.4 mg; vitamin B12 = 12 mg; nicotinic acid = 44 mg; mz mendone = 1.1 mg; biotin = 0.11 mg; thiamine = 2.2 mg; ethoxyquin = 125 mg. ** Composition of the mineral premix (per kg of diet): Mn = 120 mg; Zn = 100 mg; Fe = 60 mg; Cu = 10 mg; Se = 0.17 mg! 0.46 mg; Ca minimum = 150 mg Ca maximum = 180 mg.  
\end{table} 
Table 1: Ingredients and chemical composition (%) of the growing and finishing diets of broiler chickens. \begin{table}
\begin{tabular}{l c} \hline \hline
**Fatty Acids** & **Standard Diet** \\ \hline
**C12:0** & 37.63 \\
**C14:0** & 8.42 \\
**C14:1** & 0.29 \\
**C15:0** & 0.21 \\
**C16:0** & 16.74 \\
**C16:1** & 0.40 \\
**C17:0** & 3.64 \\
**C17:1** & 0.30 \\
**C18:1** cis 6 & 0.23 \\
**C18:0** & 3.02 \\
**C18:1** trans 9 & - \\
**C18:1** trans 11 & 0.44 \\
**C18:1** cis 9 & 12.31 \\
**C18:1** cis 10 & - \\
**C18:1** cis 11 & 0.05 \\
**C18:2** cis n6 & 8.38 \\
**C20:0** & 0.14 \\
**C20:1** & 0.11 \\
**C18:3** n3 & 1.86 \\
**C22:0** & 0.64 \\
**C22:1** & 0.15 \\ \(\sum\) SFA 1 ** & 70.53 \\ \(\sum\) MUFA 2  & 14.28 \\ \(\sum\) PUFA 3  & 10.24 \\ \(\sum\) n-6 & 9.01 \\ \(\sum\) n-3 & 1.86 \\ \hline \hline \end{tabular}  
* SFA, saturated fatty acids; 2 MUFA, monomusaturated fatty acids; 3 PUFA, polyunsaturated fatty acids.  
\end{table} 
Table 2: Fatty acid profile of Black Soldier Fly (BSF) meal fed a standard diet. The values are expressed as percentages of the total lipid fraction.
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models/Scenarios** & **Methodologies** & **Advantages** & **Important Results** & **References** \\ \hline Large-scale & Omnidirectional path loss & & & Accurate large-scale path & \\ high-density path loss & & model with directional & & politing angles deceases & loss model (CI free space) & \\ model for ultra-dense & & antenna are utilizes for 28 & & for distance and frequency & \\ indoor environment & & 73 GHz frequency band & & & \\ channel measurement & & delay spread & & \\ \hline Omnidirectional & Utilizing 3D ray-tracing & & & \\ propagation large-scale & simulators to conduct the & Proposed model already & models using CI 1 meter & \\ path loss for & experiment at 28, 38, and & & & free-space reference & \\ high-density urban & 73 GHz frequency band for & & known companies (Nokia & distance measurements are \\ scenarios & LOS and NLOS scenario & & and Samsung) & presented at 28, 38, and & \\ \hline Probabilistic & & & & \\ omnidirectional & Uses CI and FI path loss & & & \\ propagation model for & model at 28 and 73 GHz & & & \\ outdoor scenario & & & & \\ \hline
73 GHz mmWave & E-band propagation & & & \\ Propagation & measurement for both & Use of beam combining & & \\ measurements for & backhaul and mobile & and beam forming lower & & microwave cellular & [48] \\ outdoor Urban & scenario by using directional & the path losses & & \\ scenario & antennas & & & \\ \hline Angle-dependent & Utilizes narrow beam & & Higher SNR and low & \\ peer-to-peer RF & antennas at mmWave carrier & & root-mean-square delay is & Tx-Rx distance has an & \\ wideband channel & frequencies at 38 and 60 GHz & & achieved by shaping & inverse relation on Path & [49] \\ measurements & for both LOS and NLOS & & optimum antenna & loss values & \\ \hline Modification of & & & & \\ industry-standard path & & & & \\ loss models & Beam forming at 28 and 38 & & & \\  & GHz based on arbitrary & & & \\ loss models & & & & \\ \hline Height dependent path & & & & \\ loss model at 28 and & & & & \\
38.6 GHz & urban micro & 3.5 GHz & & \\  & outdoor scenario & & & \\ \hline Large-scale path loss & & Co and cross antenna & & \\ model at the 32 GHz & polarizations for the & & & \\ frequency band & directional Tx and Rx & & & \\ antenna for CI and FI model & & & \\ \hline Wideband spatial & & & & \\ Channel propagation & & & \\ analysis at 28 GHz & & & \\  & statistics and pathloss & & & \\ \hline New measurement & & & & \\ methodology for & & & \\ indoor channel at & & & \\ 28 GHz & & & \\ \hline Highly directional path & & & \\ loss measurement for & loss measurement for both & & \\ urban microcell & azimuth and elevation & & \\ scenario at 28 GHz & & & \\ frequency band & & & \\ \hline Propagation & The space-alternating & & & \\ characteristics at 11, 16, & generalized expectation & & \\
28, and 38 GHz & (SAGE)- maximization & & & \\ mmWave & algorithm is applied to & & \\ frequency band & obtain multipath component & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work summary.
## 3 Checkpointing Scheme for Clustering Routing Protocols
### Clustering Routing Protocol  
The main aim of clustering routing protocols (hierarchical protocols) is to efficiently maintain the energy consumption of sensor nodes by involving them in multi-hop communication within a particular cluster and by performing data aggregation in order to decrease the number of messages transmitted to the BS [4]. Since the Low-Energy Adaptive Clustering Hierarchy (LEACH) [10] protocol was proposed, there have been many studies on clustering routing protocols such as PEGASIS [11], TEEN [12], ATEEN [13] and OEDSR [14]. These protocols form clusters of sensor nodes based on received signal strength, and they use cluster heads as routers to send the collected information to the BS.  
Figure 1 shows the concept of the clustering routing protocol. The depicted network is divided into four clusters, and it elects cluster heads based on the residual energy within each cluster. Normal nodes only communicate with their cluster head, which in turn, aggregates the collected information and sends it to the BS. In this scheme, cluster head failures are more critical than those of normal nodes. When a cluster head fails, re-election of the cluster head is performed within the cluster. Such a recovery scheme is a time and energy consuming process. Therefore, to improve the quality and reliability of sensor networks, a fault tolerant mechanism is needed for such cluster heads.  
Figure 1: The concept of the clustering routing protocol.
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 4 Experiments and Discussion
For the PESQ metrics, the model trained on our synthetic training set and tested on real noisy recording achieves a score of 0.2 higher than the model trained using the \"limited\" training set. For the CSIG, COVID, and CBAK metrics, the scores are 0.77, 0.93, and 0.79 higher, respectively, while the maximum SNR difference is 2 dB higher. From the two \"GAN-train\" and \"GAN-test\" experimental groups, we conclude that the NM-GAN-prepared training sets can better train DNN-based SE models than the \"limited\" training sets synthesized directly from limited noise samples.
# Low-Computational Extended Orthogonal Matched Filter Structure for Multiuser Detection
## 3 Proposed EOMF Structure
### Structure of an EOMF with Multiuser Detection  
Figure 2 shows the proposed system structure for two-user detection (desired users 1 and 2). When the number of desired users is two, MF\({}_{1}\) and MF\({}_{2}\) have the same sequences as the spreading sequences of desired users 1 and 2, respectively. Then, the beamformed antenna outputs for desired user 1 and desired user 2 are input to MF\({}_{1}\) and MF\({}_{2}\), respectively. MF\({}_{3}\) to MF\({}_{N}\) have sequences that are orthogonal to the spreading sequences of desired users 1 and 2, as generated by Gram-Schmidt orthonormalization. To improve the error convergence characteristic in the adaptive algorithm, the null-steered antenna outputs for desired users 1 and 2 are input to MF\({}_{3}\) to MF\({}_{N}\). In LC 1, the output of MF other than MF\({}_{1}\) is weighted and added to generate a replica of interference for desired user 1, which is included in the output of MF\({}_{1}\). By using this approach, the interference is removed from the MF\({}_{1}\) output. The same approach can be used for LC 2. The features of the proposed structure are described from the viewpoint of IoT applications. The proposed structure reduces the number of MFs, which has the largest amount of calculation in the system, compared to the conventional EOMF. That is, the energy consumption is reduced by the  
Figure 2: Proposed Structure of the EOMF. This schematic shows an example of two-user detection. Unlike the conventional EOMF, MF\({}_{2}\) have the same sequences as the spreading sequences of desired 2 \(\mathbf{c}_{2}\). amount of the reduced computational complexity. On the other hand, the number of LCs and the number of adaptive array antenna taps are not changed significantly compared to the conventional EOMF. The LMS algorithm to update the array antenna and the LC weights is sufficiently energy efficient to be used in wireless sensor networks due to its simplicity [20, 21, 22]. Hence, it can be said that the proposed structure is more suitable for IoT applications that require energy savings than the conventional EOMF. The operation of the proposed system is explained using mathematical expressions. Letting \(\mathbf{U}^{1}= \begin{bmatrix}U_{1}^{1},&U_{2}^{1},&\dots,&U_{K}^{1}\end{bmatrix} ^{T}\), \(\mathbf{U}^{2}= \begin{bmatrix}U_{1}^{2},&U_{2}^{2},&\dots,&U_{K}^{2}\end{bmatrix}