esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Functional Properties and Applications of Nanocomposites
### Lithium-ion batteries
#### 6.5.1 Polymer-nanocomposite electrolytes applying passive ceramic nanofillers
Bifunctional (hydrophilic/hydrophobic) surface modified SiO\({}_{2}\), added to a gel consisting of (PVDF-HFP)-LiPF\({}_{6}\) in ethylenecarbonate/diethylcarbonate, caused an improved electrochemical electrolyte stability up to 5 V, which can be attributed to electrode surface protection and impurity scavenging [216]. Acrylate-based gel homopolymer electrolytes have been hardly investigated. [PERSON] and coworkers generated a MMA-LiClO\({}_{4}\)-Al\({}_{2}\)O\({}_{3}\)-nanocomposite by _in situ_ polymerization in propylenecarbonate [217]. After polymerization an ionic conductivity value around \(3.7\times 10^{-4}\) S/cm have been measured in the gel. As a resume passive nanosized fillers are mainly used for the suppression of crystallization in case of PEO-based electrolytes enabling a higher ionic conductivity under ambient conditions or for an improvement of the mechanical stability of polymer gels. The impact of nanofillers like clay, carbon nanotubes and spherical particles on the crystallization properties of different polymers like PVDF, nylon, PS, PEO, and others, are reviewed in 2006 [218]. The addition of nanosized ceramics to amorphous polymers or polymer gels does not affect significantly the ionic conductivity, but a better cycling stability by forming a protection layer at the electrode's surface has been observed.
## 5 Nanocarriers as a Strategy to Fight Biofilms
### Synergy with Antimicrobial Drugs  
Several materials possess intrinsic antibacterial properties, enhancing the effectiveness of the drugs they carry or encapsulate. Chitosan is a high-molecular-weight cationic polymer known for its biocompatibility which is extensively used as a possible carrier for drug delivery [90]. Moreover, several studies have reported the antibacterial and anti-biofilm properties of chitosan and some of its derivatives [91; 92]. As drug delivery systems, nanocarriers possess features such as nanoscale dimensions, a high surface-to-volume ratio, and excellent biocompatibility [93]. These attributes give nanocarriers certain anti-biofilm capabilities, and numerous studies have documented their use in managing biofilm infections [94; 95]. Several notable nanocarriers have been identified, including liposomes [96; 97; 98; 99; 100; 101], quatosomes [102; 103; 104; 105], solid lipid nanoparticles (SLNs) [106; 107; 108; 109; 110; 111; 112; 113], micelles [114; 115], polymeric nanoparticles [116; 117; 118; 119; 120; 121; 122; 123; 124], polymeric micelles [125], dendrimers [126; 127; 128; 129; 130; 131; 132], metallic nanoparticles [133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148], magnetic nanoparticles (MNPs) [134; 136; 140; 141; 145; 149], silica nanoparticles [150; 151; 152; 153; 154; 155], and quantum dots [156; 157; 158]. Each type of nanocarriers has unique features and benefits. Table 3 presents the various nanocarriers and their anti-biofilm properties which will be detailed in the following sections. This review evaluates the advantages of nanocarriers in fighting biofilms by examining the transport process of nanoparticles within biofilms. Additionally, it discusses the challenges of translating nanocarriers formulations from the laboratory to clinical applications. Details of the several strategies are discussed in Sections 6-10. Pharmacokinetics and biodistribution are critical factors in the development and effectiveness of nanocarriers for drug delivery _in vivo_. Nanocarriers, such as liposomes, polymeric nanoparticles, and dendrimers, are designed to enhance the delivery of therapeutic agents by improving their stability, targeting ability, and controlled release profiles.
## 5 Experiments of Roughness Surface Shot Peening
### Measurement and Analysis of Residual Stress before and after Shot Peening  
#### 5.3.1 Analysis of Residual Stress Variation before and after Shot Peening  
Due to the different surface polishing methods that were used to polish the surface to workpieces before shot peening, the residual stress values introduced during the polishing process are different, so the value and increment of residual stress before and after shot peening were analyzed. The PANalytical X-ray diffractometer was used to measure the residual stress on the surfaces of the workpiece before and after shot peening, which could be used to analyze the influence of surface topography and surface roughness on residual stress caused by shot peening. The value and the increment of surface residual stress before and after shot peening are shown in Figure 13 and Table 8.  
It can be seen in Figure 13 and Table 8 that, with the decrease of polishing accuracy, the surface residual stress and the increment of residual stress are both increased after shot peening: the residual compress stress on the surface polished by 1200# sandpaper increased by 28.3 MPa after shot peening, which means the residual compress stress relatively increased by 17.6%; and the residual compress stress on the surface polished by 80# sandpaper increased by 16.8%, which is no noticeable change in the increase rate compared with that on the surface of polished by 1200# sandpaper. Mainly it is because  
Figure 13: Surface residual stress of test pieces before and after shot peening. (**a**) Surface residual compressive stress; (**b**) variation of residual compressive stress.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Pre-Processing Method**} & **Residual Stress before** & **Residual Stress after** & **Increasement/MPa** & **Increase Rate** \\  & **Shot Peening/MPa** & **Shot Peening/MPa** & & \\ \hline
1200\# sandpaper & 146.2 \(\pm\) 13.29 & 174.5 \(\pm\) 9.68 & 28.3 & 17.6\% \\
80\# sandpaper & 399.5 \(\pm\) 10.38 & 466.8 \(\pm\) 9.34 & 67.3 & 16.8\% \\ file & 314.3 \(\pm\) 7.69 & 538.8 \(\pm\) 11.24 & 224.8 & 71.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Residual stress of workpieces in experiment.  
the residual compress stress caused by polishing is relatively large, however, the residual compress stress on the surface polished by 80# sandpaper increased by 67.3 MPa after shot pening still indicates that the decrease of surface polishing accuracy may cause the increase of residual stress after shot peening. However, the residual compress stress on the surface polished by file increased by 224.8 MPa, which increased by 71.5% before shot peening; both the increment and increase in the rate of residual compress stress are the largest of the three groups of the workpiece. The experimental result is consistent with the regulation of \"with the increase of surface roughness, the residual stress increase after shot peening\" obtained from the simulation result, which verifies the validity of the rough surface single-shot peening simulation model.
## 4 Mitigating Quantization Quality Degradation Based on the Observation
### Quantization-Free Prefix (QFeP)
#### 4.3.1 Prefix Search  
To form a prefix of an explicit activation spike, we first identify a candidate token that represents the activation spike at the linear layer with the highest max-median ratio \(r^{(m)}\). For instance, the candidate token can be an apostrophe (') token for the LLaMA-2-70B model, as highlighted in red in Figure 2. Once the candidate token is identified, we search the middle context token between the BOS token and the candidate token in the prefix. This middle context provides a dummy context, which is required to activate the candidate token. To find the middle context, we design a template \([B,T_{1},C_{1},T_{2},C_{2}]\) where \(B\), \(T_{i}\), and \(C_{i}\) denote the BOS token, context token, and candidate token in the vocabulary \(V\), respectively. Then, we select the context token \(T\) where \(C_{1}\) triggers an activation spike, whereas the latter instance of the same token \(C_{2}\) does not. When the context token for the activation spikes is varied, we choose the token that maximizes the activation scale ratio between \(C_{1}\) and \(C_{2}\). Finally, we prepare the KV cache for the searched prefix of \([B,T,C]\). Note that the latter sequence in the template can be replaced with sequences from the dataset instead of a repetition.
## 6 Conclusions  
In this paper, a collaborative centralised platooning control system has been obtained through two different control techniques, i.e., the linear quadratic controller and the reinforcement learning-based controller.  
The main outcomes of this research activity are:  
* The proposed model of the truck platoon includes the dependency of the aerodynamic drag with the inter-vehicle distance; it is vital to quantify the fuel savings of each vehicle in the platoon.
* The virtual environment that has been developed enables one to tune and train classical and AI controllers and assess platoon performance under different driving cycles.
* The LQC controller is string stable across the entire frequency range, while the RL-based controller may have a limited bandwidth of string stability.
* Regardless of the type of controller, a linear spacing policy proved to be a suitable choice to meet all the requirements (dynamic performance and energy savings).
* The training of RL provides satisfactory results even in the case of driving cycles different from the ones used for the learning phase of the agent.
* The simulation results of an RL-based controller are affected by nonlinearities; this control-related nonlinear behaviour comes from the combined operation of the safety-related penalty on the reward function and the activation functions of the neural network.
* The comparison through the selected performance indices (i.e., acceleration and jerk, final SOC, and energy consumption) during a standard driving cycle showed that, by properly selecting the reward function, RL and LQC achieve similar dynamic and energetic targets.
* The comparison during a cut-in simulation scenario showed that both controllers properly reduced the vehicle speed when the disturbance occurred, avoiding accidents.  
**Author Contributions:** Methodology, A.B., L.Z., F.M., E.G., D.A.M. and A.T.; Software, A.B. and L.Z.; Writing--original draft, A.B. and L.Z.; Writing--review & editing, F.M., E.G. and D.A.M.; Supervision, E.G. and D.A.M.; Project administration, E.G. and D.A.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methodology
### Model Performance Evaluation  
One way to evaluate the performance of trained models is to use the confusion matrix [28]. For instance, one data set consists of a group of data with two predefined classes: positive (P) and negative (N), and they are the true classes. A classifier will classify this data set as two predicted classes as well. If one data point is positive and it is classified as positive, it is counted as a true positive (TP), but if it is classified as negative, it is counted as a false negative (FN). Similarly, if one data point is negative and classified as negative, it is counted as a true negative (TN), but if it is classified as positive, it is counted as a false positive (FP). In this way, a two-by-two confusion matrix (Figure 4) can be used to evaluate the performance of a classification model. More TPs and TNs mean better performance of the classifier. The accuracy is the rate of all data, how much is correctly classified as Equation (1):  
\[\text{accuracy}=\frac{\text{TP}+\text{TN}}{\text{P}+\text{N}} \tag{1}\]  
For a certain class, the true positive rate (TPR, also called hit rate and recall) of a model [28] is Equation (2):  
\[\text{TPR}=\frac{\text{correctlyclassifiedpositives}}{\text{totalpositives}}= \frac{\text{TP}}{\text{P}} \tag{2}\]  
Similarly, the false-positive rate (FPR, also called false alarm rate) of a model [28] is Equation (3):  
\[\text{FPR}=\frac{\text{incorrectlyclassifiedpositives}}{\text{totalnegatives}}= \frac{\text{FP}}{\text{N}} \tag{3}\]  
For the negative class, the true negative rate (TNR, also called specificity) and false negative rate are given as \(\text{TNR}=\frac{\text{TN}}{\text{N}}\) and \(\text{FNR}=\frac{\text{FN}}{\text{P}}\). These ratios, TPR, TNR, FPR and FNR, describe the percentages of correctly and incorrectly classified data for each true class. For each predicted class, there are positive predictive values (\(\text{PPV}=\frac{\text{TP}}{\text{TP}+\text{FP}}\)), negative predictive values (\(\text{PPV}=\frac{\text{TN}}{\text{TN}+\text{FN}}\)), false discovery rates (\(\text{FDR}=\frac{\text{FP}}{\text{TP}+\text{FP}}\)) and false omission rates (\(\text{FOR}=\frac{\text{FN}}{\text{TN}+\text{FN}}\)) used to indicate the correctly and incorrectly classification  
Figure 3: Overview of the process of the method. The text above the arrows is the methods used for that procedure. The noise discrimination model and the classification model are the two trained models.  
of each predicted class. In Figure 4, these ratios are shown in the row/column summary in percentage. For the data set with more than two classes, we can treat each class as a \"positive\" class to evaluate, and then the row summary will be TPR vs. FNR, and the column summary will be PPV vs. FDR.
## 5 Experimental Results
### Comparative Analysis between HAS-PSO and SA-MCSDN Algorithms  
As shown in the table presented, our current work can be compared with our previous work; the results presented above confirm the existence of a large discrepancy in terms of performance improvement. According to the analysis of the schematics represented by the proposed SA-MCSDN, it achieves superior network performance relative to the previous hybrid HSA-PSO algorithm and the SA-FFCCPP algorithm in terms of scalability, reliability, delay in spread, and response. Furthermore, the quantitative amounts of the average qualitative metrics are mentioned according to the results extracted from the previous Figures for the proposed methods and respectively (propagation delay is 4.80 ms; round-trip time (RTT) is 6.05 ms; the matrix of time session (TS) is 113.13; the average delay is 42.57 ms; reliability is 99%; throughput is 395.57 Kbps; the cost is 31.27; and, finally, the fitness value is 19.16). Simulation of the multi-console mode is performed by the NS3 network simulator, and the simulation results show that the proposed work outperforms it. Table 2 shows the performance measures of the three algorithms in terms of scalability and other metrics.
# Comprehensive Investigations on Fluid Flow and Cavitation Characteristics in Rotating Disk System
## 2 Numerical Analysis  
### Cavitation Inception Mechanism  
#### 2.1.1 Pressure Coefficient  
Microscopically, cavitation inception is the process by which molecules with higher kinetic energy in liquid overcome the gravitational force of liquid surface and escape from the liquid. The key impetus for cavitation is the formation of the cavitation nucleus, and  
Figure 1: Irregular surface of flow field boundary (The arrow points in the direction of fluid flow).  
only low pressure of a sufficient duration will cause cavitation. [PERSON] described the phenomenon by a dimensionless quantity \(\sigma\), the cavitation number [35]:  
\[\sigma=\frac{p_{\infty}-p_{v}}{\frac{1}{2}\rho u^{2}_{\infty}} \tag{1}\]  
where \(p_{\infty}\) is the static pressure of the fluid in undisturbed reference section, in Pa; \(u_{\infty}\) is the fluid velocity undisturbed reference section, in m/s; \(\rho\) is the fluid density, in kg/m\({}^{3}\). \(p_{v}\) is the fluid saturated vapor pressure, in Pa. The cavitation number can quantitatively describe the cavitation phenomenon, and from Equation (1) that the cavitation number is mainly determined by the pressure and fluid velocity and is related to the material parameters.
# Stimuli-Responsive Polymeric Nanosystems for Controlled Drug Delivery
## 2 Typical Polymers for Controlled Drug Delivery  
### Poly(caprolactone)  
Most synthetic polymers are programmable and their properties can be easily modulated by chemical modification. However, most of their backbones are composed of amide or carbon bonds that are difficult to be degraded in physiological conditions, and their biocompatibility is not good. Poly(caprolactone) (PCL) is the widely used synthetic aliphatic polyester for developing nanoparticles for drug delivery applications. It is synthesized from ring-opening polymerization based on \(\epsilon\)-caprolactone by employing tin octaate as catalyst. Up to now, PCL has not only been applied in developing nanosystems for drug delivery applications but also commonly utilized as scaffold matrix material in tissue engineering [41]. As the degradation rates and solubility of PCL is low, the strategies such as synthesis of block copolymers and modification with other polymers have been employed to improve its solubility and reactivity properties [42; 43]. The PCL-based amphiphilic copolymers synthesized by modifying the hydrophobic PCL with hydrophilic polymers such as PEG, poly(2-ethyl-2-oxazoline) (PEtoOz), poly (acrylic acid) (PAA) or poly(\(N\),\(N\)-dimethylamino-2-ethyl methacrylate) (PDMAEMA) can be used to establish micelles [44]. For example, the diblock copolymers PCL-PEG that are functionalized with integrin-targeting peptide RGD have been used to design nanomicelles for tumor-targeted doxorubicin delivery [45]. Furthermore, a number of PCL-based block copolymers have been used to formulate stimuli-responsive nanomicelles for tumor-specific drug release applications [46].
## 3 Our System Model and Primer
### Channel Capacity for Communication Requirements (\(C,\ W,\ SNR\))  
Because of the signal attenuation, the increase in the channel distance requires greater transmission power so that the received power is sufficient. The required power delivery on the receiver depends on both the channel quality (noise power) and the communication system requirement (data rate). To move away from the specific digital modulation, coding, and signal processing, we use Shannon's channel capacity, which characterizes the theoretical upper bound of the reliable data rate the communication channel can support with respect to the bandwidth (\(W\)) and the received signal-to-noise ratio (SNR). SNR is the received signal power divided by the noise power. The communication channel used by the communication system has a noise power spectral density of \(N_{0}\), thus making the channel noise power \(W\cdot N_{0}\). The communication system supports the communication rate of \(R\) in bps. We do not control these parameters given by the communication system and the channel environment (\(R\), \(W\), \(N_{0}\), \(\alpha\), and \(\gamma\)). To meet the reliable bit delivery performance of \(R\), the channel capacity \(C\) must be \(C\geq R\):  
\[C=W\cdot\log_{2}(1+\text{SNR})\geq R \tag{1}\]  
The channel capacity from the Shannon-Hartley Theorem is highly used in communication theory and captures the reliable communication rate that the channel _can_ support, assuming an additive white Gaussian noise channel (AWGN). While independent of physical-layer implementations, including modulation, coding, and the receiver feedback protocol, channel capacity provides a theoretical bound that communication system designers and builders can adhere to when selecting modulation/coding implementation choices and operational constraints.  
In addition to enabling performance quantification and analyses in communication theory, the channel capacity limit has provided the upper-bound benchmark for signal processing research and development for decades. Capacity-achieving codes and signal processing research emerged from the 2000s (e.g., [25; 26; 27]) to the 2010s (e.g., [28; 29]) to the 2020s (e.g., [30; 31]).  
The theoretical channel capacity is appropriate for our research to study and analyze flying base stations because the development of flying base stations is currently ongoing, and there are no standardized or dominant protocols or algorithms to control the operations of flying base stations. We anticipate the flying base station channel capacity--building on the channel capacity from the Shannon-Hartley Theorem but adding aerial mobility and dependency on the stationary base station--to drive the research and development of the signal and digital/code processing of the flying base station communication.
# Study on Low Temperature Drying Characteristics of Chicken Manure in a Humid Environment
## 3 Results and Discussion  
### Drying Characteristic at Different Drying Temperatures  
The decreasing trend of manure water content in the drying process at different temperatures of 15, 25, and 35 \({}^{\circ}\)C is shown in Figure 3a. In this experimental group, the thickness of the manure layer was maintained at 40 mm, the air velocity was maintained at 1.2 m/s, and the relative humidity was maintained at 85%. As can be observed from the curves, increasing the drying temperature can reduce drying time, and the curves demonstrated smooth diffusion-controlled drying behaviors under the three conditions. The drying times required to reduce the wet basis moisture content of chicken manure from the initial 76.4% to 30.0% were 36, 28, and 24 h, respectively. When the moisture ratio of poultry manure was below 30.0%, most microorganisms lost their activity [19]. It took 62, 50, and 44 h to reduce manure water content (wet basis) from the initial 76.4% to equilibrium moisture ratio, respectively. It can be seen from the experimental results that the drying time in a high-humidity environment was much longer than the conventional drying results obtained by published papers [19; 20].  
The relationship between drying rate and air temperature is shown in Figure 3b. It is clearly evident from Figure 3b that the low-temperature drying process in a humid environment had only a falling rate period, with no accelerated or constant rate period. This was probably because the drying process would be mainly controlled by the internal resistance to the diffusion of water in the humid environment, which means the water diffusion rate would determine the drying rate. As shown in Figure 3b, the initial drying rates at different temperatures were significantly different, which indicates that the initial  
Figure 3: (**a**) Drying curves of chicken manure at different drying temperatures; (**b**) drying rate curves of chicken manure at different drying temperatures. Note: The dotted line in the figure indicates that the wet basis moisture content is 30%.  
drying rate was greatly affected by the drying temperature. During the drying period, a high initial drying rate was observed in the early period, followed by a gradual decrease. Then, the drying rate at a higher temperature was gradually surpassed by a lower temperature in the late period. This was because the evaporated water came from the regions near the manure surface in the early period, and the evaporated water came from the inner structure of the manure in the middle and late stages, as the surface water of the chicken manure evaporated. Moreover, it was mainly the capillary water, adsorption water, and bound water that remained in the chicken manure in the later period. [PERSON] came to a similar conclusion when he studied the drying process of poultry manure at a medium temperature [8].