esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### X-Ray Diffractometry  
The crystalline structures of silver oxynitrate and Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\) were determined by X-ray diffractometry (XRD). In brief, approximately 0.5 g of either the silver oxynitrate or Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\) powder was spread evenly into the depression in the powder XRD sample holder and placed into the X-ray diffractometer and measured from 10 to 90 \({}^{\circ}\)2\(\Theta\) recorded over 30 min. From crystal lattice structure diffraction patterns, the crystalline solid-state composition of the samples was determined. The silver oxynitrate and other solid-state compounds were identified using XRD spectra deferring the Crystallography Open Database (COD) [21; 23; 29]. The relative compositions of the primary silver oxynitrate species and by-product or degradation products were approximated using the percent relative peak height of silver oxynitrate (36.3 \({}^{\circ}\)2\(\Theta\), COD Card 2310073) versus the additional solid-state compounds identified: AgO (32.4 \({}^{\circ}\)2\(\Theta\), COD Card 1509488), Ag\({}_{2}\)O (32.8 \({}^{\circ}\)2\(\Theta\), COD Card 4318188), Ag (38.0 \({}^{\circ}\)2\(\Theta\), COD Card 1100136), AgNO\({}_{3}\) (29.7 \({}^{\circ}\)2\(\Theta\), COD Card 1509468), and Ag\({}_{2}\)SO\({}_{4}\) (28.1 \({}^{\circ}\)2\(\Theta\), COD Card 1509700). Over a four-month time course, the relative solid crystalline composition of each of the samples was observed. In this manner, the stability of the higher oxidation state silver compounds was evaluated under both ambient or room temperature and at elevated temperature, 40 \({}^{\circ}\)C, in a temperature regulated incubator.
## 2 DEM Simulation of Direct Shear Test
### Calibration of PFC3D Model  
The values of the micromechanical parameters of the FRS sample must be determined through a numerical calibration process, in which the macro-properties of the model are compared to the relevant measured response in the laboratory. The laboratory experimental data of [PERSON] et al. (2019) were used to calibrate DEM parameters in PFC3D. In the experiments, sand particles consisting mainly of quartz were clean and had mainly semi-circular, semi-angular shapes. Polypropylene fibers with specific gravity of 910 kg/m\({}^{3}\) were used in this study, and their tensile strength was 570-660 MPa. The fibers were short (19 mm) and mixed into the dry sand at a concentration of 0.5% per dry weight of sand.  
Figure 6 shows comparisons of the shear stress-horizontal displacement curves of unreinforced and reinforced sands at different normal stress levels obtained from DEM simulations and laboratory measurements. It is seen that the predicted DEM responses match well with the laboratory data, thus indicating that the proposed DEM model can capture the shear behavior of FRS. The set of micromechanical parameters selected for DEM simulation in the current analysis is shown in Table 1.  
Figure 6: Comparisons of stress–strain responses of pure sand and FRS between experiment and DEM simulation.
## 3 Results and Discussion
### In Vitro Release of Insulin-Loaded MPs  
Figure 3 shows the cumulative release of insulin from the MPs at pH 1.2 and pH 7.4. All formulations showed a gradual release of insulin. Interestingly, there was a minor release of insulin (>12%) at pH 1.2 (Figure 3a), indicating that the MPs coated with Eudragit RS-100 resisted dissolution in the acidic medium, thereby preventing the expulsion of the entrapped insulin. The slight amount of insulin observed in this case could be the result of insulin molecules physically adhered to the surface of the MPs.  
Interestingly, higher release of insulin was observed at pH 7.4 (Figure 3b), as compared to the release in acidic medium. Maximum and minimum release were observed in batch A5 (92%) and batch A1 (68%), respectively. The results showed a strong dependence between the insulin release and the PEG concentration used in the formulation. For instance, batch A1 (mucin/PEG = 1:1) showed low release compared to A5 (mucin/PEG = 1:5). This can be correlated to the viscoelastic behavior of the formulation provided by the mucin component, which would create better adherence to the gastrointestinal mucosal wall, resulting in increased drug residence time, site-targeted drug release, and improved drug bioavailability. Furthermore, the mucous gel layer has been shown to have a mean turnover time that varies between 47 and 270 min; this is an important parameter in designing mucoadhesive drug delivery systems [27,28]. It has been noted that the mucous aqueous gel layer blocks drug diffusion and adsorption through the epithelium. The insulin release kinetics followed the order A1 > A2 > A3 > A4 > A5. The maximum drug release observed for the MPs with the highest PEG concentration was ascribed to the capability of PEG chains to disseminate across the mucus and facilitate drug solubilization. This resulted in faster release at the absorption site compared to that of mucin, which is good agreement with previous reports [29]. From the extrapolation of the graph, the T\({}_{40}\) (i.e., the time taken to release 40% of the drug) values of batch A1, A2, A3, A4, and A5 MPs were 8, 4, 5, 4, and 4 h, respectively. This was in agreement with the significantly delayed release and higher retention time of batch A1 compared to the other batches.
## 4 Discussion  
We propose a simple laser scatterometer having a cylindrical photo-receiving geometry, which can serve as a prototype for a compact and inexpensive sensor of fat content and concentration levels of large particles in milk. Our experiments showed that the light-scattering indicatrix of a cow-milk-filled cylindrical tube has a monotonic dependency on the fat percentage of milk. The scatterometric parameter that was defined as the logarithm of the decrease in scattering intensity from backscatter to side scatter is highly sensitive to fat content of milk in the range 0-10%. In the studied range of milk flow rates 0-100 mL/sec, the scatterometric parameter can be considered constant with good accuracy, which ensures the stability of fat content measurements. We have studied the characteristic behavior of the scattering indicatrix of milk upon the addition of somatic cells. It turned out that the scatterometric parameter at the side-scatter angles is not perturbed, and thus the fat percentage is measured correctly even in the presence of somatic cells in milk. At the same time, the scatterometric parameter at the angles of direct scattering increases due to the presence of somatic cells, reaching a relative change of about 0.1 at a cell concentration of 10\({}^{6}\) cm\({}^{-3}\). However, it should be taken into account that the presence of large bubbles in the milk will also affect the forward-scatter intensity and, accordingly, will prevent the correct measurement of the level of somatic cells in milk.  
Figure 7: Scattering indicatrix measured for milk samples having a nominal fat percentage of 1.5%: without large-scale impurities (black circles), with the addition of somatic cells (SC) at a concentration level of 10\({}^{6}\) cm\({}^{-3}\) (red circles). Milk flow velocity in the tube is zero. The temperature is 20 \({}^{\circ}\)C. Laser wavelength is 532 nm.  
It is more advantageous to use a CCD camera instead of the photodiode P1 located at 0\({}^{\circ}\) to provide greater angular resolution at small angles in forward scattering, and thus increase the sensitivity to the presence of large particles so that the level of their concentration can be more accurately determined.  
An important design feature of the sensor is a ring holder that allows one to change the number and position of laser modules and photodiodes around the milk tube. In particular, a UV emitter can be installed in the sensor to excite the fluorescence of casein, which makes it possible to additionally determine the protein content.
## 5 Simulation Results
### Comparison between Algorithms  
Figure 7 represents the energy consumed is shown _vs._ time, and in Figure 8 the number of live nodes _vs._ time is shown, for all three protocols in the scenarios mentioned above.  
**Figure 7.** Energy consumption over the first 1,200 s for 100 nodes and areas from 100 to 260 m.  
As expected, neither the network diameter nor the number of nodes significantly affected the duration of the Simple Star protocol in these scenarios, as shown in Figure 7. In larger scenarios, however, the energy used to transmit to the sink would not be negligible and nodes that were far away from the sink would die sooner. Moreover, in practice, a network of say, 1 km\({}^{2}\) would not be feasible using the Simple Star protocol because of its lack of scalability.  
On the other hand, in the LEACH protocol, as the number of nodes increases, the number of CHs increases as well. The CH role was found to consume more power in this protocol.  
In EDETA, however, as the number of CHs increases, the TIME_CONFIG parameter also increases in order to form the CHs tree to the sink. In the first half-TIME_CONFIG period, leaf nodes are awake and receiving from the radio channel, learning which CHs can be found around them; but in 10 s they enter sleep mode until they have to join their clusters, so there is a small overhead for leaf nodes at the beginning of the network formation but it is a price that must be paid in order to build the whole network structure. This can be seen in Figure 7, where EDETA starts with more energy consumption than the other two. But, after that, its growth is similar to Simple Star protocol.  
These results can also be seen in Figure 8. Nodes in EDETA-e die faster than nodes in Simple Star, because of the energy that is consumed in network formation. However, the graphs show that the first node to die in EDETA-e does so 32,000 s later than in LEACH and the last node to die in EDETA only does so 1,700 s before Simple Star.  
Therefore the simulations demonstrate that the techniques implemented in EDETA are effective in reducing energy consumption overall and in enhancing system lifetime. Our experiments show that EDETA can achieve reductions in energy consumption of up to factor of 8 when compared to LEACH. Furthermore the last node death in EDETA occurs over 10 times later than the last node death in LEACH. EDETA also provides added features that are more advanced, such as fault-tolerant mechanisms and bounded time [13, 14, 15].  
Figure 8: Live nodes for a population of 100 nodes in a scenario of 100 \(\times\) 100 m.
# An Overview of Biopolymers for Drug Delivery Applications
## 2 Types of Biopolymers Used in Drug Delivery Systems
### Proteins
#### 2.2.1 Albumin  
Albumin plays a crucial physiological role in the human body and is the protein found in plasma with the largest amount (it comprises 50% of all plasma proteins) [89; 90]. More specifically, albumin is an internal source of amino acids. It also contributes to preserving the constant osmotic pressure of plasma. Albumin has up to 40% water solubility at a pH of 7. Thus, albumin can be combined in vivo with various insoluble chemical compounds and inorganic ions to generate soluble complexes using albumin as a non-specific transport protein. Among the advantageous characteristics of albumin are its biocompatibility, non-immunogenicity, and biodegradability [91].  
Lately, research on protein-based delivery systems with albumin for use in ocular applications has shown an increase. However, proteins require certain designing strategies to deliver at specific places because they are always susceptible to enzymatic destruction [92; 93]. Interestingly, albumin is a highly charged protein that is suitable for the electrostatic adsorption of charged bioactive compounds. Thus, drug delivery with albumin is intensely researched and explored. Among various albumin, and compared with ovalbumin and human serum albumin, bovine serum albumin was demonstrated to have therapeutic and medicinal applications [94].  
The use of mixtures of albumin and hyaluronic acid was demonstrated to be suitable nanocarriers with significant advantages such as efficient targeting, pH- and/or hyaluronidase-sensitive drug release, reducible particle size, mixing capacity for different drugs, and great stability. Additionally, skin tissue, joints, cancers, and the vitreum have all been treated with drugs delivered via albumin and hyaluronic acid-based nanoparticles. Additionally, this combination has prospects for both theranostics and combined therapy [95].
## 2 Results and Discussion
As noted, the values of \(T\) generally increased with increasing of the temperature and polymerization time. For example, the \(T\) was 0.13 (temp. process 40 \({}^{\circ}\)C), 0.46 (temp. process 60 \({}^{\circ}\)C), 0.74 (temp. process 80 \({}^{\circ}\)C) for **PLA 24**, **PLA 27** and **PLA 28**, respectively. Furthermore, when ROP of _rac_-LA was carried out at, e.g., 80 \({}^{\circ}\)C within 48 h (the presence of ZnEt\({}_{2}\)/GAc catalytic system), stereocontrol was not observed and an atactic material was obtained (Table 1, **PLA 14**, Figure 4). The analysis of the intensities of the lines due to tetrads and the calculated transesterification coefficient \(T=0.85\) indicated strong intermolecular transesterification of PLA chain (e.g., **PLA 6**), that in consequence leads to the formation of the atactic polymer (Figure 5). It is worth noting that, when ROP of _nc_-LA was carried out in the presence of a ZnEt\({}_{2}\)/PGAc catalytic system at 40 \({}^{\circ}\)C within 16 h, the microstructure of the examined polyester almost corresponded to a \"completely disridiotactic\" polymer (**PLA 23**, **PLA 29**) (Table 3, Figure 6). In this instance, the values of \(p_{2}\) were roughly 0.92 and 0.90. During the reaction progress, the transesterification process tended to reduce the chain's microstructure regularity. An analogous trend was observed by [PERSON] when ROP of _nc_-LA was carried out in the presence of lithium tert-butoxide [55]. Figure 1:4 Homonuclear decoupled \({}^{1}\)H-NMR spectra of the methine region of polylactide (**PLA 14**). Figure 1:5 \({}^{13}\)C-NMR spectra of atactic PLA (methine region) (**PLA 6**). The results of ROP of _rac_-LA in the presence of ZnEt\({}_{2}\)/PGAc have also demonstrated that, depending on the conditions, \"predominantly isotactic\", dis syndiotactic or atactic PLA can be obtained. It is also worth noting that, when the process was carried out in the presence of a ZnEt\({}_{2}\)/PGAc catalytic system (40 \({}^{\circ}\)C within 16-48 h or 60 \({}^{\circ}\)C within 16-24 h), intermolecular transesterification was not observed. Generally, we can find that when the temperature and the reaction time have been increased, the microstructure of obtained PLA has been changed in the following way: dis syndiotactic, \"predominantly isotactic\" and \"completely atactic\". We assume that ROP of _rac_-LA catalyzed by ZnEt\({}_{2}\)/GAc or ZnEt\({}_{2}\)/PGAc probably follows a coordination-insertion mechanism. The acidic metal center loosely binds and activates the lactide to attack by the -ZnO- group. The intermediate undergoes acyl bond cleavage of the lactide ring to generate a -ZnO- species and a growing chain end capped with an ester group (Scheme 3). However, it is difficult to obtain molecular zinc complexes (from the reaction of ZnEt\({}_{2}\) with GAc or PGAc), due to the strong association tendency of the products in the reaction medium [40]. However, the relevant kinetic and mechanistic studies are underway and will be presented in our next paper. Figure 1:6 \({}^{13}\)C-NMR spectra of dis syndiotactic PLA (methine region) (\(p_{2}\) = 0.92, **PLA 23**).
## 2 Green Routing Algorithms in WSNs
### Optimizing Data Flow-Based Routing Algorithms
#### 2.3.1 Single Path-Based Schemes  
In single path-based routing schemes, routing algorithms try to achieve a near-optimal or optimal path to the sink through local optimization. Inspired by the concept of potential application in the physical field, [PERSON] et al. [59] proposed the energy-balanced routing protocol (EBRP). EBRP sets up a hybrid field consisting of three virtual fields: depth field, energy density field, and residual energy field. The potential difference \(U_{m}(i,j,t)\) from node \(i\) to \(j\) at time \(t\) is defined as:  
\[U_{m}\ (i,j,t)\ =\ (1-\alpha-\beta)U_{d}^{\prime}\ (i,j)\ +\ \alpha U_{d}^{ \prime}\ (i,j,t)\ +\ \beta U_{e}^{\prime}\ (i,j,t), \tag{2}\]  
where \(0\leq\alpha\leq 1\), \(0\leq\beta\leq 1\), \(0\leq\alpha+\beta\leq 1\), weights \(\alpha\) and \(\beta\) determine how much impact the energy density potential field and residual energy potential field impose on the routing decision, respectively. The depth field means the depth of minimum hops from source node to the sink. Under the hybrid field, the packets are forwarded to the sink along the energy-intensive area to protect those low-energy nodes. EBRP finds an energy balancing path to forward packets and guarantee delay performance.  
Energy-efficient speed routing protocol (EESPEED) [60] is an improved energy-efficient routing protocol based on SPEED [63]. In SPEED, each node records the location information and forward speed of all its neighbors and sets a threshold. When a node receives a packet, it chooses the neighbors nearest to the destination than to the node itself as the candidate relaying set. The nodes with the highest forwarding rate are selected as the relaying node. The routing restarts when there is no node with a forwarding rate higher than the threshold. Different from SPEED, EESPEED considers three factors of forwarding delay, residual energy, and relaying rate to optimize the next hop. The weight function \(f_{j}\) of the \(j\)th sensor is formally defined as:  
\[f_{j}=\max(\alpha E_{n}+\beta Sp*\phi(De)), \tag{3}\]  
where \(\alpha+\beta=1\), \(E_{n}\) is the ratio of residual energy on node \(j\), \(De\) is the ratio of delay on node \(j\), \(Sp\) is the relay speed its packet transfers from the present node to node \(j\), and \(\alpha\) and \(\beta\) are the coefficients of these factors.
# Adaptive Noise Exploration for Neural Contextual Multi-Armed Bandits
## 3 Problem Definition  
In the contextual multi-armed bandit problems, in each round \(t\in T\), the user needs to interact with the contextual multi-armed bandit. The bandit presents the user with \(K\) arms, which are each represented by a feature vector \(\{X_{i}\in\mathbb{R}^{d}|i\in[K]\}\). After choosing an arm \(a_{t}\), its reward \(r_{a_{t},t}\) is assumed to be generated by the following function:  
\[r_{a_{t},t}=h\big{(}X_{a_{t}}\big{)}+\eta_{t} \tag{1}\]  
The reward function \(h(X_{a_{t}})\) is unknown. \(\eta_{t}\) is random noise with a mean of 0 [17, 21].  
We aim to design a strategy that minimizes cumulative regret, which is equivalent to maximizing the expected cumulative rewards [28]. \(r_{t}^{*}\) is the reward brought by always choosing the optimal strategy in a given context. Thus, the definition of cumulative regret is  
\[\mathbb{E}\left[\sum_{t=1}^{T}(r_{t}^{*}-r_{a_{t},t})\right] \tag{2}\]  
Our algorithm models the reward distribution using a neural network and integrates a dynamic noise adjustment mechanism to balance exploration and exploitation. This mechanism enhances early exploration and gradually shifts focus to exploiting high-reward arms, effectively reducing regret and improving long-term returns. An inverse relationship exists between long-term returns and regret: higher long-term returns correspond to lowerregret. Regret serves as a complementary metric to long-term return, representing the gap between achieved and optimal returns over time.  
To reflect the potential nonlinear relationships of the reward function \(h(X_{a_{t}})\), we use a deep neural network  
\[f(x;\theta)=\sqrt{m}\mathbf{W}_{L}\phi(\mathbf{W}_{L-1}\phi(\cdots\phi(\mathbf{W}_{1}X))) \tag{3}\]  
as a function approximator to learn the unknown reward function \(h(X_{a_{t}})\). Here, \(\text{ReLU}(x):=\max\{x,0\}\), \(m\) is the width of the neural network, \(\mathbf{W}_{1}\in\mathbb{R}^{m\times d}\), \(\mathbf{W}_{l}\in\mathbb{R}^{m\times m}\), \(\mathbf{W}_{L}\in\mathbb{R}^{1\times m}\), \(\mathbf{\theta}=(\text{vec}(\mathbf{W}_{1});\cdots;\text{vec}(\mathbf{W}_{L}))\in\mathbb{ R}^{p}\) is the collection of parameters of the neural network, and \(p=dm+m^{2}(L-2)+m\).
# Outage Probability Analysis in Relaying Cooperative Systems with NOMA Considering Power Splitting
## 3 Performance Analysis
### Throughput in Delay-Limited Transmission Mode  
In fact, the throughput is examined by \(OP\) at a fixed transmission rate. For simplicity, we assume that the source transmits data at a fixed rate, \(R_{0}\) (bits/sec/Hz), and the \(OP\) is important to the throughput defined as \(R_{0}=2^{2\gamma_{0}}-1\) (\(\mathrm{bps/Hz}\)) with \(1/2\) being the effective communication time. Therefore, the throughput, \(\tau\) at \(D\) is written by  
\[\tau_{D,x_{1}}=\frac{1}{2}R_{0}(1-OP_{x_{1}}),\] (32a) and \[\tau_{D,x_{2}}=\frac{1}{2}R_{0}(1-OP_{x_{2}}). \tag{32b}\]
# Energy Harvesting over Rician Fading Channel:
## 2 System Model
### Energy Harvesting and Information Transfer Protocols
In fact, the received signal \(y_{1}\) can be rewritten as  
\[y_{1}=\beta|g_{1}|^{2}s_{1}+\beta|g_{1}|^{2}\eta_{1}+\beta\left|g_{1}\right| \left|g_{2}\right|s_{2}+\beta\left|g_{1}\right|\left|g_{2}\right|\eta_{2}+ \beta\left|g_{1}\right|n_{r}+\left|g_{1}\right|\eta_{r}+n_{1}. \tag{6}\]  
This signal contains both messages \(s_{1}\) and \(s_{2}\), while only \(s_{2}\) is the desired signal at \(s_{1}\). Since node \(S_{1}\) perfectly knows its own transmitted symbol \(s_{1}\), it can eliminate the corresponding the self-interference term \(\beta|g_{1}|^{2}s_{1}\) from \(y_{1}\). From this fact, Equation (6) can be rewritten as  
\[y_{1}=\underbrace{\beta\left|g_{1}\right|\left|g_{2}\right|s_{2}}_{signal}+ \underbrace{\beta\left|g_{1}\right|^{2}\eta_{1}+\beta\left|g_{1}\right|\left|g _{2}\right|\eta_{2}+\beta\left|g_{1}\right|n_{r}+\left|g_{1}\right|\eta_{r}+n _{1}}_{noise}. \tag{7}\]  
Therefore, the end-to-end signal-to-noise-ratio at \(S_{1}\) for detection of the symbol \(s_{1}\) is given by  
\[\gamma_{1}=\frac{\mathrm{E}\left\{\left|signal\right|^{2}\right\}}{\mathrm{ E}\left\{\left|noise\right|^{2}\right\}}=\frac{\beta^{2}\left|g_{1}\right|^{2} \left|g_{2}\right|^{2}P}{\beta^{2}\left|g_{1}\right|^{4}P\kappa^{2}+\beta^{2} \left|g_{1}\right|^{2}\left|g_{2}\right|^{2}P\kappa^{2}+\beta^{2}\left|g_{1} \right|^{2}\sigma_{r}^{2}+\left|g_{1}\right|^{2}P_{r}\kappa_{r}^{2}+\sigma_{1 }^{2}}. \tag{8}\]  
After doing some algebra and using the fact that \(\frac{\sigma_{1}^{2}\sigma_{r}^{2}}{P_{r}}\approx 0\), combining with Equation (6), we obtain:  
\[E_{r}=\left(P|g_{1}|^{2}+P|g_{2}|^{2}\right)\eta\alpha T, \tag{1}\]0  
where \(a=\kappa^{2}+\kappa_{r}^{2}(1+\kappa^{2}),b=\frac{(1+\kappa_{r}^{2})}{P/\sigma _{r}^{2}}\), \(c=\frac{(1+\kappa^{2})}{\psi P/\sigma_{r}^{2}}\) (assume that \(\sigma_{1}^{2}=\sigma_{2}^{2}=\sigma_{r}^{2}=N_{0}\)).