esa-sceva/satcom-chunk-collection huggingface dataset:
# The Antimicrobial Effectiveness and Cytotoxicity of the Antibiotic-Loaded Chitosan: ECM Scaffolds
## 3 Results
### Duration of Antimicrobial Effects
#### 3.3.1 Duration of Antimicrobial Effect Against _Staph. Aureus_  
When _Staph. Aureus_ was exposed to supernatants from rifampicin-loaded chitosan:ECM scaffolds or control discs (Figure 3), no differences between scaffolds and control at 1 h were seen. All the chitosan:ECM formulations showed smaller zones of inhibition at 2 h, except for the 1% (_w/v_) ECM scaffolds. Zones of inhibition were smaller for 100% chitosan, 80/20 and 50/50 chitosan:ECM scaffolds at all subsequent time points with the ability to inhibit bacterial growth diminishing completely between 4 and 8 h. The 1% and 10% (_w/v_) ECM scaffolds all retained antimicrobial activity up to 24 h. The 1% (_w/v_) ECM scaffolds showed no difference to controls except at 8 h. The 10% (_w/v_) scaffolds showed no difference to controls except at 24 h where the zone of inhibition was larger than the control.  
A similar response pattern was seen when _Staph. Aureus_ was exposed to supernatants from minocycline-loaded chitosan:ECM scaffolds (Figure 3). The 1 h samples all showed no difference in the diameter of the zone of inhibition compared to control except for the 1% (_w/v_) ECM which was smaller. At 2 h the 80/20, 50/50 chitosan:ECM scaffolds and 1% (_w/v_) ECM scaffolds had significantly smaller zones of inhibition compared to control. All the chitosan:ECM formulations had smaller zones  
Figure 3: Graphs showing the duration of antimicrobial effects. Supernatant samples from the different scaffold formulations were taken at 1, 2, 3, 4, 8, and 24 h and incubated using a punch plate agar assay. Zones of inhibition were measure for both _Staph. Aureus_ and _E. Coli_ bacteria.  
of inhibition at all subsequent time points up to 24 h. Again, the antimicrobial effect of the 80/20 and 50/50 chitosan:ECM scaffolds was lost between 2 and 3 h and after 4 h in the 100% chitosan:ECM scaffolds. The 1% (_w/v_) ECM scaffolds showed some antimicrobial activity up to 24 h but this was not as pronounced as in the controls. The 10% (_w/v_) ECM scaffolds had the longest duration antimicrobial activity and, although the zone of inhibition were smaller than the control, the zone of inhibition remained a consistent size at each time point and was no different to the control at 24 h suggesting that the scaffolds containing powdered ECM had a more prolonged the release of the antibiotic from the scaffold compared to the scaffolds made from chitosan and ECM hydrogel.
## 4 Simulation Analysis  
### The Stimulated Signal  
To verify the validity of the proposed method, the simulated fault signals of the rolling bearing are analyzed in this section. The simulated signal of the early faults of ball bearings can be calculated by Equations (14), (15), (21) and (22), where \(I(t)\) is the interference signal, \(x(t)\) is the transient impulse fault signal, and \(n(t)\) is the noise signal.  
\[S(t)=x(t)+I(t)+n(t) \tag{21}\] \[I(t)=A_{h}\sum_{i=1}^{n}\sin(2\pi f_{hi}) \tag{22}\]  
The simulation result of the fault signal is shown in Figure 4; Figure 4a is the transient periodic impulse fault signal, Figure 4b is the interference signal, and Figure 4c is the noise signal. The impulse period is set to 0.01 S, and the resonance frequency is 2000 HZ. Moreover, 6000 sampling points of the signals are obtained. The sampling frequency is set at 12 KHz. The signal-to-noise ratio (SNR) is set to \(-13.2\) dB, and the signal-to-interference signal ratio (SIR) is set to \(-4.5\) dB. In order to show the signal clearly, only 3000 sample points are plotted in Figure 4. The mixed signal and its envelope spectrum are plotted in Figure 5. It can be seen that the informative features in the spectrum of the original signal cannot be identified. Therefore, the proposed method is performed on the simulated signal to detect the incipient fault features.  
Figure 4: The stimulated fault signals of rolling element bearings. (**a**) The transient impulse fault signal; (**b**) The interference signal; (**c**) The noise signal.  
Figure 3: The framework of the proposed method.
# Prediction of Grain Porosity Based on WOA-BPNN and Grain Compression Experiment
## 2 Porosity Experiment Determination Method  
### Materials  
Four common grains found in granaries were chosen for this experiment: wheat, corn, soybean, and paddy. The initial wet basis moisture content of the grains was measured as 10.70%, 11.58%, 10.14%, and 12.93%, respectively, after drying at 105 \({}^{\circ}\)C for 72 h, in accordance with the ASAE standards [26]. Broken grains, immature seeds, and impurities were filtered out using a sieve. Then, 100 seeds were randomly selected for measuring their triaxial dimensions with vernier calipers for all the filtered grains [27], and their mean values were calculated. According to the code for the Inspection of Grain and Oils--Determination of Test Weight [28] and for the Inspection of Grain and Oil--Determination of Relative Density of Grain and Oilseeds [29], the material parameters of each type of grain were measured, as shown in Table 1.  
This paper investigated the effects of grain type, moisture content, and vertical pressure on grain porosity. Distilled water was sprayed to formulate three different moisture contents for each grain. The samples were sealed in polyethylene bags and stored at 4 \({}^{\circ}\)C for 48 h to allow the moisture to equilibrate. The moisture content of various grain samples is presented in Table 2. The purpose of formulating multiple sets of moisture contents for each grain is to simulate the occurrence of different moisture contents in the grain in the silo. Different moisture contents will affect the porosity of the grain.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Grain Type**} & **Initial Bulk** & **Particle** & **Grain** & \multirow{2}{*}{**Grain Width**} & **Grain** \\  & **Density** & **Density** & **Length** & **W/mm** & **Thickness** \\  & \(\rho_{\text{g}}\)**/(kg/m\({}^{3}\))** & \(\rho_{\text{s}}\)**/(kg/m\({}^{3}\))** & **L/mm** & **L/mm** & **T/mm** \\ \hline Wheat & 806.8 & 1383.75 & 6.53 & 3.41 & 2.91 \\ \hline Corn & 735.0 & 1256.68 & 11.84 & 8.62 & 4.72 \\ \hline Soybean & 717.1 & 1219.87 & 7.72 & 7.58 & 7.46 \\ \hline Paddy & 599.0 & 1164.49 & 7.38 & 3.07 & 2.13 \\ \hline \hline \end{tabular} Note: Initial bulk density \(\rho_{\text{b}}\) is the density of the grain pile (cell) when it is not under vertical load; particle density \(\rho_{\text{s}}\) is the ratio of the mass of a single grain particle to its volume.  
\end{table}
Table 1: Material parameters for each grain type.
# Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios
## 3 Materials and Methods  
### Elements of Testing Scenarios  
Previous research has shown that the test scenarios for autonomous vehicles (AVs) should comprehensively cover all relevant elements to accurately simulate real-world conditions. The hierarchical structure of the scenarios presented in the previous paper is more mature, but due to the limitations in vehicle states and interactions, we propose the use of an updated five-layer structure to represent these elements.  
The first layer focuses on the ego vehicle, which is the AV being tested. This includes its motion (such as speed, acceleration, and direction), driving tasks (like turning, lane changes, and stopping), and the vehicle type, which is based on its physical properties and geometry. The second layer covers road characteristics, detailing elements like road section types (such as intersections or straight roads), the number of lanes (which impacts road width), road surface conditions (including how weather affects surface adhesion), road smoothness, and slope. These factors are critical as they influence the vehicle's dynamics and handling. The third layer includes the traffic infrastructure, such as traffic signals and physical obstacles, which guide and influence the AV's behavior. These elements contribute to the complexity of the scenario by fostering interactions among different traffic participants. The fourth layer addresses the objects around the AV, including other vehicles,pedestrians, and cyclists. It considers their behaviors, quantities, relative directions, and positions, which are crucial for accurately modeling real-world interactions. The fifth layer represents the environment, encompassing various external conditions such as weather and lighting. These environmental factors significantly affect both the AV's sensors and its driving behavior. The elements of the scenario are shown in Table 2.  
By combining these five layers, the study creates a comprehensive virtual environment that closely replicates real-world scenarios, allowing for a thorough assessment of the AV's performance under diverse conditions.
# A Novel Optimal Control Strategy of Four Drive Motors for an Electric Vehicle
## 2 Simulation Platform and Optimal Energy Management
### Electric Vehicle Simulation Platform
#### 2.1.3 Drive Motor Model  
In this study, the drive motor model, utilizing the demanded torque from the driver model, calculates the torque based on the maximum physical limit of the drive motor speed for protection. It then outputs the appropriate drive motor torque for propulsion. Figure 4 shows the mechanical efficiency curve of the drive motor, with test data provided by the ZEPT company, where the efficiency of the drive motor is determined based on the current speed and torque of the drive motor through a two-dimensional table. It is shown that at a torque of 60 Nm and a rotational speed of approximately 6500 rpm, the motor achieves a peak efficiency of 94.99%. At the same rotational speed, applying a negative input torque indicates that the motor is operating in generator mode. The motor efficiency is shown in Formula (2):  
\[\eta_{m}(t)=f(T_{m}(t),\ N_{m}(t)) \tag{2}\]  
where \(\eta_{m}\) is the efficiency of the drive motor; \(T_{m}\) is the output torque of the drive motor; \(N_{m}\) is the wheel speed of the drive motor.
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 4 Quantum Entropy as a Service (QEaaS)  
Ensuring the scalability of quantum-secure key solutions across diverse IoT environments is a significant challenge due to the presence of millions of legacy devices that lack the hardware to support advanced quantum technologies. While modern IoT devices can integrate quantum technologies, upgrading or replacing legacy devices is costly and disruptive. Legacy devices remain vulnerable to future quantum-based attacks, making it critical to develop solutions that enable both modern and legacy devices to utilize quantum-secure keys without requiring extensive hardware upgrades.  
QEaaS can be designed to enhance the security of legacy IoT systems and devices. QEaaS is a practical approach to address the challenge of obtaining high-quality quantum entropy that can be delivered to IoT devices and systems on demand. QRNG can serve as a source to provide high-entropy random numbers, while IoT devices can request them through a cloud environment and use them to perform local security functions. By leveraging quantum entropy, IoT networks can secure cryptographic operations such as key generation, authentication, and secure communications with unprecedented levels of randomness and unpredictability. Quantum entropy services can be deployed in cloud-based or distributed IoT environments, ensuring resource-constrained IoT devices benefit from the security advantages of quantum technology. While direct integration of QRNG hardware into each IoT node remains costly, QEaaS offers a scalable alternative. By centralizing entropy generation, QEaaS can significantly reduce costs, simplify maintenance, and scale well across large resource-constrained IoT networks.  
Figure 2: Quantum cryptographic module design: integrating QRNG with FPGA for secure key generation.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion
### BER Measurements and Relation with the FI Path Loss Model
#### 5.2.3 5G QPSK SS-RSRQ  
For 5G transmission, SS-RSRQ was used as the quality parameter instead of BER. Relating this quality parameter at each point with the received power prediction of each model, we obtain the results shown in Figure 17. From this figure, it can be seen that to have a value of SS-RSRQ less than \(-18\) dB requires a lower power value of \(-81\) dBm for narrowband transmission and \(-114\) dBm RSSI for LoRa transmission. The third row of Table 4 shows these results.  
Using Figures 15-17, an estimation can be made of the possible BER and SS-RSRQ that a LoRa, Zigbeee and/or 5G transmission may have by means of a received power measurement of a narrowband, LoRa, Zigbeee and/or 5G one. Table 5 shows, as an example, the value of arbitrary powers received in each technology and their respective BER and SS-RSRQ estimation.  
Figure 16: Comparison of empirical Zigbee BER with the estimated received power for each technology.  
Figure 17: Comparison of empirical 5G SS-RSRQ with the estimated received power for each technology.  
Figure 15: Comparison of empirical LoRa BER with the estimated received power for each technology.  
Hence, we can conclude that it is possible to predict the BER of a LoRa and Zigbee transmission, and the SS-RSRQ of a 5G transmission, using only the received power in any of the technologies used in this work. Similarly, it is possible to predict the received power value in each technology using the BER value of LoRa, the BER value of Zigbee or the SS-RSRQ value of 5G.
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 5 Performance and Evaluation
### The Trace Based Simulation  
The variation of wireless channels with space and time is attributed to both large-scale effects (path-loss and shadowing) and small-scale effects (multipath fading and Doppler fading), and only the former is accounted for in the ns-2 channel model. To obtain more realistic results, we adopted a trace-based approach to compensate for the inaccuracy of ns-2 simulation. Specifically, we obtained physical layer packet loss traces using a pair of universal software radio peripheral (USRP) programmable transceiver [28]. Compared with existing 802.11 traces, the advantage of USRP is that it has no MAC protocol, and therefore it can isolate the channel variation from the MAC layer protocol overhead. We modified the ns-2 physical layer module by intercepting all packet transmissions, and then fed the traces into the module, allowing it to determine whether a packet could be successfully received (Figure 7). To improve the granularity of traces, each USRP packet had only 32 bytes, which was shorter than the data packet (1.5 KB) and ACK packet (46 bytes) in our simulation. Whenever the airtime (duration of transmission) of a simulated packet overlapped an erroneous packet in the trace, it was declared as loss.  
We collected the packet loss traces for all links in the 3-node (AP, A, B) topology in Figure 3, and plot the results in Figure 8. We observed that most links tended to have a stabilized loss rate. However, some links may have experienced a large loss variation and asymmetric loss characteristics. For example, the (A\(\rightarrow\)B) and (B\(\rightarrow\)A) links demonstrate a very different loss rate and variation. Such properties were not modeled in the ns-2 simulator, either.
## 2 Materials and Methods
### Theory of Infrared Radiation  
The radiation obtained by an infrared thermal imager mainly includes three parts: the self-radiation of the measured target, reflected environmental radiation, and atmospheric  
Figure 1: Workflow for MODTRAN software using measured atmospheric parameters. radiation [17]. The equivalent radiance from the surface of an opaque target by an infrared thermal imager can be expressed as:  
\[L_{\lambda}(T_{r})=\tau_{a\lambda}\epsilon_{\lambda}L_{b\lambda}(T_{0})+\tau_{a \lambda}(1-\alpha_{\lambda})L_{b\lambda}(T_{u})+\epsilon_{a\lambda}L_{b\lambda} (T_{a}) \tag{1}\]  
where \(T_{r}\) is the radiation temperature measured with the infrared thermal imager, \(T_{0}\) is the surface temperature of the object, \(T_{u}\) is the ambient temperature, \(T_{a}\) is the atmospheric temperature, \(\epsilon_{\lambda}\) is the surface emissivity of the measured object, \(\epsilon_{a\lambda}\) is the atmospheric emissivity, \(\tau_{a\lambda}\) is the atmospheric transmittance, \(\alpha_{\lambda}\) is the surface absorptance of the object, and \(L_{b\lambda}\) denotes the radiance emitted by the blackbody at wavelength \(\lambda\). The corresponding irradiance of entrance pupil for the infrared thermal imager is:  
\[E_{\lambda}=A_{0}d^{-2}L_{\lambda}=A_{0}d^{-2}[\tau_{a\lambda}\epsilon_{ \lambda}L_{b\lambda}(T_{0})+\tau_{a\lambda}(1-\alpha_{\lambda})L_{b\lambda}(T_ {u})+\epsilon_{a\lambda}L_{b\lambda}(T_{a})] \tag{2}\]  
where \(A_{0}\) is the visible area of the target corresponding to the minimum space angle of the thermal imager, \(d\) is the distance between the target and the thermal imager, and usually \(A_{0}d^{-2}\) is considered as a constant. Radiation power of the infrared thermal imager received for a certain wavelength is:  
\[P_{\lambda}=E_{\lambda}A_{r} \tag{3}\]  
where \(A_{r}\) is the area of lens of the thermal imager. Without considering stray radiation, which can be minimized via high projection material and cold aperture, the total radiation of the thermal imager received \(P_{T}\) is:  
\[P_{T}=\tau_{op}P_{\lambda} \tag{4}\]  
where \(\tau_{op}\) is the transmittance of optical systems. The response voltage signal \(V_{s}\) is referred according to [18] as:  
\[V_{s}=g(R_{\lambda}P_{T}+V_{0}) \tag{5}\]  
where \(R_{\lambda}\) is the spectral responsivity of the infrared thermal imager, which is a constant for a certain infrared thermal imager; \(V_{0}\) is the DC bias voltage; and \(g\) is the response gain used to amplify the signal. \[ \begin{array}{ll}V_{s}=&A_{r}A_{0}d^{-2}\tau_{op}g\{\tau_{a\lambda}[\epsilon _{\lambda}\int_{\lambda_{1}}^{\lambda_{2}}R_{\lambda}L_{b\lambda}(T_{0})d \lambda+(1-\alpha_{\lambda})\int_{\lambda_{1}}^{\lambda_{2}}R_{\lambda}L_{b \lambda}(T_{u})d\lambda]\\ &+\epsilon_{a\lambda}\int_{\lambda_{1}}^{\lambda_{2}}R_{\lambda}L_{b\lambda}( T_{a})d\lambda\}+gV_{0}\end{array}
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 2 Methods  
### Data  
The data used in this study were provided by the Department of Neurology at the University of Texas McGovern Medical School. Four epochs of intracranial EEG (awake, sleep, sleep to awake, and seizure) sampled at 200 Hz over various parts of the right hemisphere of a single individual's brain were used in the analysis. The recordings were obtained from subdural grid and strip electrodes, placed directly over the brain surface through an operative procedure for the clinical investigation of temporal lobe epilepsy. The grids and strip electrodes were recorded from a total of sixty-six discrete channels over different brain regions. In the days following electrode placement, seizures were identified over contacts 2 and 3 on the strip electrode labeled RAST (Right Anterior Subtemporal) and over contact 4 on the strip electrode labeled RMST (Right Mid Subtemporal). The awake, sleep, and sleep to awake segments were approximately 10 min in length, with waking occurring in the sleep to wake signal at approximately 8 min; the segment with the seizure was approximately 15.5 min in length, with six minutes of pre-seizure data, 2.5 min of seizure data and seven minutes of post-seizure data. The onset of seizure was determined by conventional visual analysis by a clinical neurologist. All four segments were referenced to an external zero voltage reference. The data used in the analyses were de-identified.
## 3 Theoretical Comparisons Between Estimators
\]  
Thus, \(\hat{\omega}\Lambda^{-1}-\hat{\omega}\Lambda^{-1}_{k^{+}}\Lambda_{k^{-}} \Lambda^{-1}_{k^{+}}\Lambda\Lambda^{-1}_{k^{+}}\Lambda_{k^{-}}\Lambda^{-1}_{k^{ +}}\) is positive definite, which means that \(\hat{\alpha}^{PIG-MkLE}\) is better than \(\hat{\alpha}^{PIG-MkLE}\) since it has a smaller covariance matrix. **Theorem 2**. : _The estimator \(\hat{\alpha}^{PIG-MkLE}\) is superior to the estimator \(\hat{\alpha}^{PIG-ISE}\) in the sense of MMSE criterion, i.e., \(MMSE\big{(}\hat{\alpha}^{PIG-ISE}\big{)}-MMSE\big{(}\hat{\alpha}^{PIG-MkLE} \big{)}>0\) if and only if_  
\[ \begin{array}{c}\mathrm{bias}\Big{(}\hat{\alpha}^{PIG-MkLE}\Big{)}^{\prime }\Big{[}\hat{\alpha}^{2}\hat{\omega}\Lambda^{-1}-\hat{\omega}\Lambda^{-1}_{k^{ +}}\Lambda\Lambda^{-1}_{k^{+}}\Lambda\Lambda^{-1}_{k^{+}}\Lambda_{k^{-}}\Lambda^{- 1}_{k^{+}}\Lambda_{k^{-}}\Lambda^{-1}_{k^{+}}\Big{]}\mathrm{bias}\Big{(} \hat{\alpha}^{PIG-MkLE}\Big{)}<1\end{array} \]  
Proof. : The difference between \(\mathrm{cov}\big{(}\hat{\alpha}^{PIG-MkLE}\big{)}\) and \(\mathrm{cov}\big{(}\hat{\alpha}^{PIG-MkLE}\big{)}\) is as follows:  
\[ \begin{array}{c}\mathrm{cov}\big{(}\hat{\alpha}^{PIG-JSE}\big{)}-\mathrm{ cov}\big{(}\hat{\alpha}^{PIG-MkLE}\big{)}=\hat{\omega}\Lambda^{-1}-\hat{\omega} \Lambda^{-1}_{k^{+}}\Lambda\Lambda_{k^{-}}\Lambda^{-1}_{k^{+}}\Lambda\Lambda^{- 1}_{k^{-}}\Lambda_{k^{-}}\Lambda^{-1}_{k^{+}}\\ \mathrm{cov}\big{(}\hat{\alpha}^{PIG-JSE}\big{)}-\mathrm{cov}\big{(}\hat{ \alpha}^{PIG-MkLE}\big{)}=\hat{\omega}\ \mathrm{diag}\bigg{\{}\frac{\hat{\alpha}}{\hat{\alpha}}-\frac{\lambda_{j} \big{(}\lambda_{j}-k\big{)}^{2}}{\big{(}\lambda_{j}+k\big{)}^{2}}\bigg{\}}_{j= 1}^{q}\end{array} \]Since \(\text{bias}\left(\hat{\alpha}^{PIG-MKLE}\right)\text{bias}\left(\hat{\alpha}^{ PIG-MKLE}\right)^{{}^{\prime}}\) is non-negative definite, then the difference matrix \(\text{cov}\left(\hat{\alpha}^{PIG-ISE}\right)-\text{cov}\left(\hat{\alpha}^{ PIG-MKLE}\right)\) is positive definite if and only if  
\(c^{2}\left(\lambda_{j}+k\right)^{4}>\lambda_{j}^{2}\left(\lambda_{j}-k\right)^{2}\), for \(k>0\) and \(0<c<1\). Thus, \(c^{2}\hat{\omega}\Lambda^{-1}-\hat{\omega}\Lambda_{k^{+}}^{-1}\Lambda_{k^{+}} \Lambda_{k^{+}}^{-1}\Lambda_{k^{-}}\Lambda_{k^{+}}^{-1}\) is positive definite, which means that \(\hat{\alpha}^{PIG-MKLE}\) is better than \(\hat{\alpha}^{PIG-ISE}\) since it has a smaller covariance matrix. \(\square\)  
**Theorem 3**. : _The estimator \(\hat{\alpha}^{PIG-MKLE}\) is superior to the estimator \(\hat{\alpha}^{PIG-RRE}\) in the sense of MMSE criterion, i.e., \(MMSE\left(\hat{\alpha}^{PIG-RRE}\right)-MMSE\left(\hat{\alpha}^{PIG-MKLE} \right)>0\) if and only if_  
\[\text{bias}\left(\hat{\alpha}^{PIG-MKLE}\right)^{\prime}\left[\hat{\omega} \Lambda_{k^{+}}^{-1}\Lambda\Lambda_{k^{+}}^{-1}-\hat{\omega}\Lambda_{k^{+}}^{ -1}\Lambda_{k^{-}}\Lambda_{k^{+}}^{-1}\Lambda_{k^{+}}\Lambda_{k^{-}}\Lambda_ {k^{+}}^{-1}\right]\text{bias}\left(\hat{\alpha}^{PIG-MKLE}\right)<1\]  
Proof.