esa-sceva/satcom-chunk-collection huggingface dataset:
# Biodegradable Polymeric Architectures via Reversible Deactivation Radical Polymerizations
## 2 Biodegradable Polymeric Architectures
### Biodegradable Biomolecule-Polymer Conjugates  
Bioconjugates refer to a category of polymer conjugates with widespread biomolecules, which have attracted increasing interest as they have numerous potential applications in biotherapeutics, bioseparation and functional materials field. The importance of bioconjugates lies in the fact that the biomolecules will exhibit prolonged circulation time in biofluids [130; 131] and their immunogenicity and antigenicity can also be reduced by the incorporation of biocompatible polymer fragments [132; 2; 133]. When the bioconjugates are designed with biodegradable linker between the biomolecules and the polymer fragments these biomolecules can be released in vivo, therefore, their bioactivities can be reversed [20; 134]. On the other hand, most biomolecules, e.g., proteins and enzymes, consist of peptides that are linked by biodegradable disulfide bonding. In this case, these biomolecules are also biodegradable, making the whole bioconjugates biodegradable.  
[PERSON] and coworkers delivered elegant research on the preparation of biodegradable conjugates. Free thiol tethered biomolecule, e.g., bovine serum albumin (BSA), has been successfully modified with several polymers to afford biodegradable homo- or hetero-bioconjugates under ambient condition using room temperature initiation via RAFT polymerization (Figure 12d) [135; 136; 137; 138]. By tailoring the bioconjugates with disulfide linkage between lysozyme and the polymer chains, the bioactivity of lysozyme can be reversed during the biodegradation process [134]. They have also successfully modified lysozyme with well-defined poly-\(N\)-(2-hydroxyethyl) methacrylate via surface modifications through amide bonding to tailor the enzyme's bioactivity [139]. A latest study reported the modification of fragile glucose oxidase (GOx) with biocompatible polymer, poly(ethylenediycl) acrylate (polyPEG-A) and thermoresponsive copolymer of poly(ethylenediycl) acrylate and di(ethylenediycl) ethyle ether acrylate [poly(PEG-A-_co_-DEG-A)] to afford biodegradable enzyme-polymer conjugates. Bio-cleavage of the polymer chains from the GOx surface obviously recovered the enzymatic activity [62]. These smart enzyme-polymer conjugates would envision promising applications in biotechnology and biomedicine. [PERSON] and coworkers also achieved significant advances in the preparation of bioconjugates using either ATRP or RAFT polymerization. They successfully modified si-RNA with a biodegradable polymer fragment. Since _si_-RNA is considered an effective targeting molecule, its biodegradable polymer conjugates could be good candidates for potential bio-therapeutics [140]. In addition to RAFT polymerization, ATRP has also been successfully applied to prepare biodegradable polymer conjugates with BSA [141; 142] and engineered lysozyme [143].
## 2 Governing Equations and Boundary Conditions  
In the present study, a submerged bare hull of a submarine was modeled in a viscous and incompressible fluid so that the fluid flow was considered turbulent. Accordingly, _RANS_ are used as the governing equations [19]:  
\[\
abla.(\rho\underline{V})=0 \tag{1}\]  
\[\frac{\partial}{\partial t}[\rho\underline{V}]+\
abla.[\rho\underline{V} \underline{V}]=-\
abla\underline{p}+\
abla.[\underline{\tau}-\rho\underline{V ^{\prime}}\underline{V^{\prime}}]+\underline{f_{b}} \tag{2}\]  
The nomenclature is presented at the end of the paper. To solve the _RANS_, the Pressure Implicit Method with Pressure Linked Equations (_PIMPLE_) algorithm was applied. Different terms of the discretized equations, such as derivative terms, gradient parameters, Laplace derivative terms, and divergence terms, were discretized using 1 st order implicit Euler, 2 nd order centered Gauss linear, skewness corrected centered Gauss linear correction, and Upwind schemes, respectively [20]. Using block mesh and refinement techniques, a cylindrical domain was generated by using a Cartesian structured grid. Different boundary conditions summarized in Table 1 were used for the velocity, pressure, kinetic energy, and dissipate rate on the boundaries shown in Figure 1. According to [PERSON] [21], if the submarine moves at a depth greater than five times the diameter of its hull, the effect of surface and wave interactions can be ignored.  
In addition, the _K_-Epsilon two-equation model was used to account for turbulence [22].  
\[\frac{\partial}{\partial t}[\rho k]+\
abla.[\rho Vk]=\
abla.[\mu_{eff,k}\
abla k ]+P_{k}-\beta\rho k\varepsilon \tag{3}\]  
\[\frac{\partial}{\partial t}[\rho\varepsilon]+\
abla.[\rho V\varepsilon]= \
abla.[\mu_{eff,k}\
abla\varepsilon]+C\varepsilon_{1}\frac{\varepsilon}{k}P _{k}-C_{\epsilon 2}\rho\frac{\varepsilon^{2}}{k} \tag{4}\]  
where  
\[C_{\epsilon 1}=1.44C_{\epsilon 2}=1.92\beta=0.09\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Boundary** & **Velocity** & **Pressure** & **Kinetic Energy (k)** & **Dissipation Rate (\(\epsilon\))** \\ \hline Inlet & Fixed Value & Zero Gradient & Fixed Value & Fixed value \\ Outlet & Inlet Outlet & Fixed Value & Inlet Outlet & Inlet Outlet \\ Body & Moving wall velocity & Zero Gradient & Wall Function & Wall function \\ around & Symmetry & Symmetry & Symmetry & Symmetry \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different boundary conditions used for different parameters.  
Figure 1: Schematic sketch of the domain and boundary conditions.  
\[\mu_{eff,k}=\mu+\frac{\mu_{t}}{\sigma_{k}}\quad\mu_{eff,k}=\mu+\frac{\mu_{t}}{ \sigma_{k}}\sigma_{k}=1.0\sigma_{\omega}=1.3\]  
The resistance force and coefficient were calculated by integrating pressure and shear stress over the surface of the submarine as follows [23]:  
\[R=\int_{S}pn_{x}dA+\int_{S}\tau_{xy}n_{y}dA+\int_{S}\tau_{xz}n_{z}dA \tag{5}\]  
\[C_{R}=\frac{R}{\frac{1}{2}\rho SV_{f}^{2}} \tag{6}\]  
where \(S\) is the surface area of the submarine's bare hull.
## 2 Materials and Methods
### Synchronized Data Collection and Biomechanical Modeling  
The application of 3D motion capture and biomechanical modeling represents a reliable and widely employed methodology for elucidating the motor control intricacies inherent in various sports skills, specifically through kinematic quantification [18; 19; 20; 21]. This approach, complemented by the measurement of ground reaction forces, facilitates the determination of joint forces and moments/torques--commonly referred to as kinetic quantification--based on 3D joint kinematics, ground reaction forces, and segmental inertial properties [22], as established by inverse dynamic analysis [3; 4]. In this study, synchronized data collection from a 3D motion capture system and a ground reaction force platform was implemented to collect data from the 12 varsity players.  
The 3D motion capture employed a twelve-camera VICON V8 motion capture system (VICON Motion Systems, Oxford Metrics Ltd., Oxford, UK) operating at a frequency of 200 frames per second, while the synchronization of ground reaction force measurement (a function in VICON system) was achieved using two KISTLER platforms (model 9287C, 900 \(\times\) 600 \(\times\) 200 mm, KISTLER AG, Winterthur, Switzerland) operating at 1000 Hz (Figure 2a). A prevalent practical simplification in biomechanical quantifications of running, cutting, and jumping activities involves the utilization of lower limb modeling rather than full-body modeling [23; 24; 25]. In alignment with this standard practice, the current study adopted lower limb modeling. Eighteen reflective markers (diameter = 14 mm) were utilized in constructing a 7-segment biomechanical model, outlining segments such as the pelvis, thighs, shanks, and feet. This model is one of the human body models provided by the VICON system. For this model, the required 18 markers were placed on the anterior superior iliac crest, the posterior superior iliac crest, the lateral condyle of the tibia, the lateral malleolus of the fibula, the calcaneal tuberosity, the tuberosity of the fifth metatarsal, and the head of the hallux, as well as on the upper and lower leg. The four markers on the upper and lower leg specifically contributed to determining segmental rotations, as they did not influence segmental translations and did not necessitate specific anatomical positioning. Stringent calibration procedures were executed in strict adherence to established VICON guidelines, resulting in minimal calibration residuals and a precision level within 1.5 mm. Additionally, all trials were contemporaneously recorded by a CASIO video camcorder (100 Hz) in conjunction with the VICON system to obtain visual references for performance assessment. Figure 2b,c illustrate the synchronized data collection with video reference.
###### Abstract  
In order to quantitatively analyze the electromagnetic exposure dose of an inverter in a pure electric vehicle to the driver's body and assess the safety of the electromagnetic exposure, based on a real human anatomy model in the virtual home project, a real human model with several organs and tissues, including muscles, bones, a heart, lungs, a liver, kidneys, a bladder, a skull, a scalp, white matter, and a cerebellum, was constructed. The inverter of a pure electric vehicle is considered to be the electromagnetic exposure source; for this study, an equivalent electromagnetic environment model composed of a real human body, an inverter, and a vehicle body was built. The distribution of induced fields in the driver's tissues and organs was calculated and analyzed using the finite element method. The results show that the distribution of the magnetic flux density, induced electric field, and induced current density in the driver's body was affected by the spatial distance of the inverter. The farther the distance was, the weaker the value was. Specifically, due to the different dielectric properties of the different tissues, the induced field in the different tissues was significantly different. However, the maximum magnetic flux density over the space occupied by the driver's body and induced electric field in the driver's trunk and central nervous system satisfied the exposure limits of the International Commission on Non-Ionization Radiation Protection, indicating that the electromagnetic environments generated by the inverter proposed in this paper are safe for the vehicle driver's health. The numerical results of this study could also effectively supplement the study of the electromagnetic environments of pure electric vehicles and provide some references for protecting the drivers of pure electric vehicles from electromagnetic radiation.  
type: electric vehicle; electromagnetic exposure; real human body; inverter; numerical calculation; the safety assessment
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Forwarding Packets via a Single Parent
#### 4.1.5 Ca-Of  
The congestion-aware objective function (CA-OF) [28] proposed by [PERSON] et al. also uses ETX and buffer occupancy both as routing metrics. The design of CA-OF is based on the assumption that ETX and buffer occupancy need to be weighed differently depending the degree of traffic load. The authors argue that ETX is a more effective metric for light traffic load because the majority of packets are lost at the wireless channel. On the other hand, buffer occupancy is a more important metric for heavy traffic because the majority of packets are lost due to buffer overflow.  
CA-OF combines ETX and buffer occupancy (BO) using a simple weighted sum as follows:  
\[\text{Combined\_Metric}=w_{1}*\text{ETX}+w_{2}*\text{BO} \tag{9}\]  
where the weights \(w_{1}\) and \(w_{2}\) represent the percentage of the free buffer space and the occupied buffer space, respectively. For example, \(w_{1}\) is set to 100% if the buffer is empty. When the buffer is full, \(w_{2}\) is set to 100%.  
Performance was evaluated using Cooja simulator, and ContikiMAC RDC was used. The authors compared CA-OF to OF0, ETX-OF, and energy-OF. For two different network scenarios, CA-OF outperformed the other OFs in terms of packet losses, throughput, PDR, and energy consumption.
## 2 Materials and Methods
### SAXS-WAXS Experiments  
X-ray diffraction patterns were acquired using a microfocus X-ray tube (IuS, Incoatec, Geesthacht, Germany), selecting the Cu K\(\alpha\) radiation. It was used with an intensity of 1000 uA and a voltage of 50 kV. The incident beam was focused at the detector with multilayer Montel optics and 2D Kratky block collimator. Small-angle (SAXS) and wide-angle (WAXS) X-ray scattering analyses were performed simultaneously using two position-sensitive linear detectors (Vantec-1, Bruker, Billerica, MA, USA) set perpendicular to the incident beam direction, up to 7\({}^{\circ}\) (26) and at 19\({}^{\circ}\) to 28\({}^{\circ}\) (20) from it, respectively. The direct beam was stopped with a W-filter. The scattered intensity was reported as a function of the scattering vector q = 4\(\pi\) sin \(\theta\)/\(\lambda\) where \(\theta\) is half the scattering angle and \(\lambda\) is the wavelength of the radiation. The repeat distances d, characteristic of the structural arrangements, were given by q (A\({}^{-1}\)) = 2\(\pi\)/d (A). Silver behenate and tristearin (\(\beta\) form) were used as standards to calibrate SAXS and WAXS detectors, respectively.  
All samples (-10 mg) were introduced into thin-walled glass capillaries (GLAS, Muller, Berlin, Germany) of 1.5 mm external diameter which were then placed in a specially designed temperature-controlled sample holder (Microcalix, Setaram, Lyon, France). For static measurements at 20 \({}^{\circ}\)C, the acquisition time was 10 min. For measurements with temperature, samples were heated at 2 \({}^{\circ}\)C/min and acquisition time was 1 min, leading to frame recording every 2 \({}^{\circ}\)C.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 2 System Design
### Hardware Components
#### 2.1.4 Temperature Sensor  
The DS18B20 temperature sensor can be used to quantify the water temperature, operating with the 1-wire communication protocol [20], accurately measuring water temperature in the range of \(-\)55 \({}^{\circ}\)C to \(+\)125 \({}^{\circ}\)C with an accuracy of \(\pm\)0.5 \({}^{\circ}\)C. The typical measurement error is \(\pm\)0.5 \({}^{\circ}\)C within the recommended operating range of 0 \({}^{\circ}\)C to 85 \({}^{\circ}\)C. At extreme temperatures (close to \(-\)55 \({}^{\circ}\)C or 125 \({}^{\circ}\)C), the error may slightly increase. For optimal accuracy, the sensor should be used within its recommended range.
# Application of a MEMS-Based TRNG in a Chaotic Stream Cipher
## 3 Seed Generator
### Mechanical Response of the Accelerometer  
Although these measurements were made with the accelerometer at rest, the mechanical response of the accelerometer in an IoT device can often surpass the noise signal. To solve this problem, three methods are proposed.  
Figure 8: Conceptual block diagram of the processing performed.  
Figure 9: (**a**) NIST test results for bitstream obtained by sampling the sensor at 2.5 kSps; (**b**) NIST test results after applying the SHA-512 algorithm to that bitstream. The test numeration is the same as in Table 1.  
First, since it is not necessary to generate the keys constantly, the system can generate keys only when the accelerometer is at rest. Currently, some algorithms exist that can detect when the accelerometer is at rest. Although these algorithms are usually used to put the device in sleep mode, they could also be used to activate the key generation process [24].  
Second, signal acquisition in inertial MEMS (such as an accelerometer) is usually based on the synchronous demodulation technique, which requires an output low-pass filter to demodulate the output signal and reduce the high frequency noise. Similarly, another path with a high-pass filter could be included. This way, when the device was in a normal operation mode, the low-pass filter path would be used while the system would switch to the patch with the high-pass filter in order to generate the keys.  
Third, most of the accelerometers include a Self-Test pin that, when set at a certain voltage, the accelerometer presents a null response. Therefore, this feature could be used to stop the accelerometer from generating the keys. For example, capacitive accelerometers use the force-feedback technique to force the proof mass back to its rest position.  
Finally, a combination of these solutions could be used. Further study is needed in order to determine the best solution for each application.
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 3 Overloading Mitigation Strategies
### Parallel PI  
#### 3.1.1 Strategy Description  
This strategy introduces PI regulators in parallel with the PSL to directly control inverter frequency when active power exceeds operational limits. By overriding the PSL output during overload conditions, it effectively disables both its frequency regulation and inertial contributions, ensuring that the inverter power remains within safe bounds. Under overload conditions, the inertial response is governed by the PI dynamics.  
The control diagram is shown in Figure 5. The PI regulators output, \(\omega_{v}\), remains saturated to zero while the active power is inside operational limits. Once these limits are exceeded, the PI adjusts the angular frequency to track power limits.  
Figure 4: Overload of GFM inverters due to inertial response. (**a**) Overload for different grid RoCoF and inertia constants. (**b**) Overload scaling factor.
# Application Characteristics of Bioethanol as an Oxygened Fuel Additive in Diesel Engines
## 3 Results and Discussion
### Combustion  
#### 3.2.1 In-Cylinder Pressure  
The in-cylinder pressure in this study was directly measured by a Kistler piezoelectric sensor, and the pressure curve as shown in Figure 3 was obtained according to the change in crank angle. Important information, such as combustion pressure, HRR, peak heat release rate, IMEP, fuel supply effective pressure, combustion duration, ignition delay and start of combustion (SOC), can be obtained from the in-cylinder pressure value. Therefore, the in-cylinder pressure is recognized as a very important information parameter, especially in the engine development and calibration phase [17]. Figure 3 shows the p-\(\theta\) diagram for the diesel and diesel-ethanol-blended fuels (E5, E10 and E15) at the various engine  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Range** & **Resolution** & **Accuracy** \\ \hline Electrochemical O\({}_{2}\) (\%) & 0–30 & 0.1\% & \(\pm\)0.57\% \\ Electrochemical CO (ppm) & 0–4000 & 1 ppm & \(\pm\)0.62\% \\ Pellistor HC (ppm) & 0–10,000 & 1 ppm & \(\pm\)5\% \\ Electrochemical NOx (ppm) & 0–6000 & 1 ppm & \(\pm\)0.25\% \\ Smoke opacity (\%) & 0–100 & 0.1\% & \(\pm\)1\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of the exhaust emission analyzers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Unit** & **Conditions** \\ \hline Fuel & - & E0, E5, E10, E15 \\ Load & Nm & \(40,60,80\) \\ Speed & rpm & 1600 \\ Fuel injection pressure & MPa & 35 \\ Pilot injection timing & \({}^{\circ}\)CA BTDC & 17 \\ Main injection timing & \({}^{\circ}\)CA BTDC & 7 \\ Intake air temperature & \({}^{\circ}\)C & \(25\pm 3\) \\ Cooling water temperature & \({}^{\circ}\)C & \(85\pm 2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental conditions.  
loads of 40 Nm, 60 Nm and 100%, respectively. From these figures, it is clear that the pressure curves for all the tested fuels show almost the same change trend under the low load of 40 Nm, the maximum pressure in the cylinder of the diesel-ethanol blended fuel is obviously lower than that of diesel under the medium load of 60 Nm and the high load of 80 Nm, and the SOC of all the diesel-ethanol-blended fuels is later than that of diesel. This may be because, under the low load of 40 Nm, the combustion environment in the cylinder, such as pressure and temperature, are relatively low, and the fuel properties, such as cetane number, oxygen content and the latent heat of evaporation, are not effectively released, resulting in almost the same p-o diagram for all the tested fuels. Ethanol with a low calorific value causes the maximum combustion pressure of the blended fuels to be lower than that of diesel, and ethanol with a low cetane number and high latent heat of regeneration causes a late SOC.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 3 Experiment Results and Analysis
### Data
#### 3.1.2 Data Decomposition  
This paper adopts the Prophet algorithm to extract trend features, which illustrates the periodic variation trend in the trend features of electricity load data, as depicted in Figure 3. Simultaneously, the daily and weekly cycle features, along with residuals, are extracted from the electricity load data, as shown in Figures 4 and 5. Specifically, Figure 4 presents the localized view of the daily cycle feature, providing the clearer depiction of the 24 h periodic variation. Figure 5 presents the localized view of the weekly cycle feature, highlighting the 24 \(\times\) 7 h periodic variation. The residuals are obtained after extracting the trend and cycle information. The result of the CEEMDAN decomposition is shown in Figure 6, presenting the decomposition of the residuals into 14 Intrinsic Mode Functions (IMFs).  
Figure 4: Part of daily feature data.  
Figure 5: Part of weekly feature data.  
Figure 6: CEEMDAN decomposition results.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 8 Research Gaps and Challenges
### Optimization Criteria  
Producing scheduling decisions that balance contrasting goals like cost, energy consumption, and time (latency or execution time or response time or Completion time) is a challenge. Only 16% of studies consider the above-mentioned optimization criteria, and other works focused on cost and time or energy consumption and cost. Finally, there are research works that base their scheduling decision only on a single optimization criterion (c.f Table 3). Hence, research efforts should focus on multi-objective scheduling algorithms.  
An important gap to fill is the study of availability-aware scheduling algorithms. Fog nodes are typically unreliable because they could lose network connectivity, because they can fail, or simply because they can decide to quit a fog colony. Despite this intrinsic unreliability, very few works (about 5%) investigate how to maximize availability, and no one proposes solutions for trading off high availability goals with the other optimization criteria (c.f Table 3).  
Another research challenge is to design mobility-aware scheduling algorithms accounting for the dynamic movement of devices within a fog environment, where their locations, connectivity, and processing capabilities are subject to change [14]. To overcome this obstacle, algorithms and strategies must be implemented to handle device mobility efficiently. Tasks can be assigned to the most appropriate resources while accommodating changing network topologies and the impact on real-time application requirements. Mobility-aware scheduling algorithms are essential for maintaining the reliability and efficiency of fog computing systems, especially when device movement and changing conditions are involved.