esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Aerospace missions require that pyrotechnic compositions are able to withstand 180 \({}^{\circ}\)C. Therefore, this paper studies the thermal stability and output performance of boron/potassium nitrate (abbreviated BPN) used in pyrotechnic devices. Firstly, differential scanning calorimetry (DSC) and thermogravimetric (TG) tests are used to analyze the thermal reaction process of KNO\({}_{3}\), boron, and BPN to qualitatively judge their thermal stability. Then, apparent morphology analysis, component analysis, and the p-t curve test, which is the closed bomb test to measure the output power of the pyrotechnic composition, are carried out with BPN samples before and after the high-temperature test to verify BPN stability at 180 \({}^{\circ}\)C. The weight change of boron powder caused by chemical reactions occurs above 500 \({}^{\circ}\)C. When the temperature is lower than the peak exothermic temperature of decomposition, no obvious chemical reaction occurs with KNO\({}_{3}\), and only physical changes (crystal transformation and melting) occur. Combined with a verification test at 180 \({}^{\circ}\)C for two days, it is concluded that boron and KNO\({}_{3}\) components are stable at 180 \({}^{\circ}\)C. With an increase in boron content, the thermal stability of BPN is improved, with the best performance achieved when the ratio is 25:75 (B:KNO\({}_{3}\)). BPN samples without binder have the best thermal stability. In a test at 180 \({}^{\circ}\)C for five days, the binder affects the weight loss and p-t curve of BPN, and BPN with fluorobber binder is better than BPN with unsaturated polyester binder.  
boron/potassium nitrate; thermal analysis; high temperature stability; energetic material 180 \({}^{\circ}\)C
# The Influence of Bit Edge Shape Parameters on Bone Drilling Force Based on Finite Element Analysis
## 1 Introduction
[20] applied a parabolic groove to the surgical bit. They obtained that a helix angle of 36\({}^{\circ}\) is the best geometrical parameter for the minimum drilling temperature and force. Cutting edge radius, also known as edge radius and tooltip radius, can be defined as the nominal blunt circle radii of the cutting edge, measured in the plane of the cutting edge method. The study results by [PERSON] and [PERSON] [21] showed that the tangential and axial cutting forces decrease with the increase in tip radius, but the radial forces increase. In terms of experiments, tests conducted by [PERSON] and [PERSON] [22], [PERSON] and [PERSON] [23], and [PERSON] [24] show that tangential cutting forces increase with the increase in the tip radius. In addition, [PERSON] [24] and [PERSON] et al. [25] presented an empirical model of cutting forces related to the tip radius. The results show that radial and tangential components of the cutting force increase while the feed component decreases with the increase in the tip radius. The effect of tool tip radius on the cutting temperature was demonstrated only by [PERSON] [26], which emphasizes that the cutting temperature decreases with the tool tip radius. Through literature analysis, it is found that there are different opinions on the influence of drill bit shape on drilling temperature and drilling force. As a result, the optimum parameters of helix angle are different. There are also different views on the tip radius. Researchers studied a single geometrical parameter of drill bit and not multiparameter. Therefore, based on the current research, further optimization is necessary to obtain the best parameters to optimize cutting conditions and to achieve low damage bone drilling. Meanwhile, the cutting-edge structure design of the bit is the key to influencing the drilling force, temperature, and positioning. The variation law of the influence of bit structure on drilling force needs further study. Biomechanical finite element simulation techniques have been widely used [27]. However, in the simulation study of bone drilling, the previous studies mainly focus on the influence of drilling parameters on drilling force and temperature [28]. Currently, two problems still need to be further studied: (1) the influence of main bit structure parameters on drilling force; (2) the design and optimization of bit structure using optimization algorithm. Therefore, this work aims at these two problems to do research so that it may provide a theoretical basis for selecting the appropriate drill bit shape in the further study of bone drilling.
## 2 Materials and Methods
### Non-Destructive Measurement Using L-Band EPR Spectroscopy  
An EPR tooth dosimeter obtained from Clin-EPR, LLC was used for EPR tooth dosimetry. This instrument was based on instruments designed for in vivo dosimetry by the EPR Center for the Study of Viable Systems, Geisel School of Medicine at Dartmouth College in  
Figure 1: Radiation dose-rate map obtained by airborne monitoring results and the location of Okuma and Namie Farm as of 31 May 2012. This map was quoted from the website provided by Nuclear Regulation Authority, Japan. Dose rate shows μS/h as a dose equivalent rate including background radiation. ([[http://ramap.jaea.gojp/map/](http://ramap.jaea.gojp/map/)]([http://ramap.jaea.gojp/map/](http://ramap.jaea.gojp/map/))). Note: H*(10) means ambient dose equivalent.  
Hanover, NH, USA [15]. The spectrometer operates in continuous-wave (CW) mode and uses homodyne detection at an excitation frequency near 1.15 GHz (L-band) using a 41 mT dipole magnet weight 30 kg with 17 cm pole separation. The integrated field sweep and modulation coil provides a 4 mT sweep range and 0.4 mT modulation at 20 kHz. It was measured using a specially developed surface loop resonator for maxillary incisors which has a detection loop with an inner diameter of 6.0 mm. The detection loop was brought into contact with the enamel on the labial side of each incisor, and the radicals on that surface were measured.  
For calibration of the L-band EPR response, we used 10 teeth that were irradiated with a \({}^{137}\)Cs \(\gamma\)-rays at Hiroshima University by using a Gammacell 40 Exactor Low Dose Rate Research Irradiator (Best Theratronics Ltd., Ottawa, ON, Canada).  
To adjust the resulting measured radiation-induced signal (RIS) amplitudes, the ratio of differentiated voltage as a radiation-induced signal (RIS) to the differentiated voltage of a stable standard that was simultaneously measured in the detection loop, 4-oxo-2, 2, 6, 6, -tetramethylpiperidine-d16-1-\({}^{15}\)N-1-oxyl (\({}^{15}\)N-PDT) for each measurement was normalized to the same ratio in the referral tooth.  
The EPR spectra were acquired by using our standard measurement parameters of 20 scans, with a scan range of 2.5 mT, scan time of 3 s, and modulation amplitude of 0.4 mT [16]. This process was repeated three times. The spectra from each of the collected datasets were analyzed to estimate the peak-to-peak signal amplitudes of the RIS and \({}^{15}\)N-PDT. The RIS and signals of \({}^{15}\)N-PDT were then averaged to determine the mean amplitude for each tooth. For two cows (08411-03687, 08411-03274), the scan times were increased to 200, to increase signal/noise.
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
## 3 Results and Discussions
### Resistivity  
At present, the resistance value and electro-osmotic value of concrete are used to evaluate its durability. The resistance value measures the conductivity of the concrete surface. The electrical conductivity of concrete materials is carried out by the movement of ions caused by electrolytic reactions in the pores. Concrete with more pore water or a high water-cement ratio has a lower resistivity. The fewer the pores of the concrete, the higher the density, the longer the electrical conduction path, and the higher the resistance value. The resistivity of concrete significantly influences the corrosion rate of steel bars. When the resistance value of concrete is less than 10 k2-cm, the probability of corrosion is high. Still, the resistance value of concrete is greater than 50 k2-cm, which can significantly reduce the corrosion rate of steel bars in concrete.  
As shown in Figure 11, for the curing age from 3 to 7 days with W/B = 0.5, the volume of waste PE increased from 0 to 4%, and the resistivity decreased from 11.3 k\(\Omega\)-cm to 9.3 k\(\Omega\)-cm. After 28 days, the control group had a higher resistivity of 25.1 k\(\Omega\)-cm. When the added volume of waste PE increased to 4%, the resistivity was 22.9 k\(\Omega\)-cm (\(-\)8.8%), which showed the resistivity declined due to the increase in the added volume of waste PE. After 91 days, the resistivity of the control group was 35.2 k\(\Omega\)-cm. When adding 4% waste PE, the resistivity was 31.5 k\(\Omega\)-cm. When the added volume of waste PE rose from 0% to 4%, the resistivity decreased by 10.5%. This result indicated that the sample structure had higher compactness in the late period. After 28 days with the W/B at 0.5, when 1% waste PE was added, together with 0%, 10%, and 20% GGBFS, the resistivities were 24.5 k\(\Omega\)-cm, 25.0 k\(\Omega\)-cm, and 25.4 k\(\Omega\)-cm, respectively (increased by 2.0% to 3.7%).  
Figure 10: The water absorption rate of cement mortars with the addition of different ratios of waste PE and GGBFS replacement.  
This result showed that when the replaced volume of GGBFS increased from 0% to 20%, GGBFS produced a pozzolanic reaction to make the compactness of the internal body of the sample higher than that of the control group. After 91 days, the resistivities were 35.2 k\(\Omega\cdot\)cm, 36.1 k\(\Omega\cdot\)cm, and 38.9 k\(\Omega\cdot\)cm when 1% waste PE was added and when 0%, 10%, and 20% GGBFS replaced the cement, respectively. This result indicated that the groups with different proportions of GGBFS produced pozzolanic reactions in the late period, which made the sample more compact. When 20% GGBFS was added in the late period, the resistivity was higher, followed by the group with 10% GGBFS. After 28 days, the resistivity of all groups with different proportions was higher than 20 k\(\Omega\cdot\)cm, and these groups all showed durability.
## 3 Results and Discussions  
The proposed models are evaluated on a MacBook having specifications of an intel Core i5 1.8 GHz processor, 8 GB 1600 MHz DDR3 RAM, and Intel HD graphics 6000 1536 MB, fully loaded with Anaconda navigator and Spyder V3.28 Python Language programming. The Spyder programming has been installed with Tensorflow and Keras libraries. For each data segment, the computation time is set to 10 min. Table 2 presents the error measurements for the discussed parameters (_MAPE_, _MAE_, and _MSE_) for the bagging ensemble and DL baseline models. These parameters are computed and compared monthly while keeping in view five distinct input features. In general, the proposed model provides lower errors than other training models. According to the results provided in Table 2, the proposed bagging model attains a forecasting accuracy of 6.05% in the case of the _MAPE_ parameter. In contrast, the accuracy for LSTM, CNN+LSTM, and DNN are 6.74%, 6.85%, and 6.79%, respectively. This shows that the proposed model outperforms all other training models for the _MAPE_ measurement. A similar trend could also be observed for other parameters. Considering the _MAE_ parameter, the bagging ensemble achieves 1258.98 MW, followed by LSTM with 1413.74 MW, and DNN with 1424.50 MW. The least performance is shown by the CNN+LSTM model, contributing to 1439.48 MW. Similar behavior is observed for the _MSE_ parameter, where bagging realizes 3099.11 GW\({}^{2}\). After the proposedmodel, LSTM performs better with 3712.15 GW\({}^{2}\). Finally, the MSEs of the DNN and CNN+LSTM models are 3783.01 GW\({}^{2}\) and 3772.21 GW\({}^{2}\), respectively. Although the proposed ensemble gives better performance, January, April, May, and December still get higher errors in all models because these months have long holidays and tourism seasons. Another factor is the highest temperature in April and May. Hence, electricity consumption is higher than usual. In contrast, the proposed model observed the lowest _MAPE_, showing 2.36% _MAPE_, 512.57 MW _MAE_, and 421.70 GW\({}^{2}\)_MSE_ in September. Next, the DNN model performs well, followed by LSTM and CNN+LSTM models. Table 3 also indicates error measurements for all models, training with six input features. Similar to Table 2, the best forecasting accuracy is realized by the proposed model, at 6.08% _MAPE_, 1265.55 MW _MAE_, and 3077.47 GW\({}^{2}\)_MSE_. The second model is the LSTM model, providing 6.75% _MAPE_, 1411.22 MW _MAE_, and 3751.94 GW\({}^{2}\)_MSE_, as opposed to the DNN model with 6.77% _MAPE_, 1420.27 MW _MAE_, and 3750.29 GW\({}^{2}\)_MSE_. The hybrid CNN and LSTM model demonstrates the least forecasting accuracy with 6.83% _MAPE_, 1435.82 MW _MAE_, and 3768.83 GW\({}^{2}\)_MSE_. For the holiday season, there is not much error difference on including temperature as an input feature in the training models.
# Characteristics of HEMP Radiation Field on Typical Transport Aircraft with Frequency Sweep Method
## 2 Models and Setup
### Frequency Sweep Method Based on FEM Model  
Currently, there are some numerical models that can be adopted to solve EMP issues, such as the finite difference time domain method (FDTD) [6], the finite element method (FEM) [7], the method of moments (MOM) [8], the partial element equivalent circuit (PEEC) [9], etc. However, no such numerical model can solve all EMP problems. The FEM model, which is based on the Maxwell's equation as shown in Equation (2), is particularly suitable for solving complex structures, wide frequency domains or electrically large objects [10]. Hence, the FEM model is adopted in this paper.  
\[\
abla\times H_{1}=j\omega_{1}\varepsilon_{1}E_{1} \tag{2}\]  
Figure 1: Illustration of incident angle and polarization direction.  
where \(H_{1}\) is the magnetic field strength of the medium, A/m; \(\omega_{1}\) is the angular frequency, Hz; \(\epsilon_{1}\) is the dielectric constant of the medium; \(E_{1}\) is the electric field strength, V/m.  
Furthermore, the frequency sweep method based on the FEM model is carried out for the HEMP of the typical transport aircraft. For the frequency sweep method, the frequency ranges are determined according to the main characteristics of the field strength. Meanwhile, various parameters including the electric field strength distribution are calculated with Altair FEKO software. Altair FEKO is a comprehensive computational electromagnetic software that is widely used and validated in various sectors including in telecom, aerospace and defense industries. The time-domain EM solver is used to compute the voltages across, and currents through, the terminated cable ports [11].
# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 2 Field Test of Vibration Data Acquisition with Portable Devices
### Working Principle of MEMS Sensors  
A smartphone was used as the vibration acquisition device, with vibration data primarily collected through the MEMS sensors embedded in the phone. Modern smartphones are typically equipped with built-in MEMS sensors, as illustrated in Figure 2a. A MEMS sensor functions as a gyroscopic accelerometer with three mutually orthogonal axes designed to detect acceleration from different directions. The inclusion of a gyroscope allows the smartphone to approximately measure gravitational acceleration regardless of its orientation, whether placed horizontally or vertically, thereby eliminating the need for sensor calibration.  
MEMS sensors operate based on [PERSON]'s second law, functioning as a mass-spring-damper system within a three-dimensional silicon structure. As shown in Figure 2b, the sensor's core consists of a mass block attached to a movable arm, which is connected to a fixed arm through a capacitive structure. When the sensor experiences acceleration, the mass block moves opposite to the direction of motion, changing the capacitance and, in turn, the sensing voltage. This voltage change enables the detection of external acceleration. Key components such as the mass block, springs, and dampers are mechanical elements made from silicon using advanced processing techniques and play crucial roles in the sensor's operation.  
Figure 1: Substation frame in the field test: (**a**) substation frame; (**b**) main dimensions.  
Figure 2: MEMS sensor: (**a**) MEMS sensor; (**b**) principle of operation.
## 6 Results and Discussion
### Primary Twitter Filter  
In this section, maxentropy, random forest, and SVM are implemented. As shown in Table 5 and Figure 8, SVM achieved the best results in terms of recall, precision, and F1-score for most datasets. So, it is selected to be applied in our framework with parameters c = 0.1, kernel = linear, and penalty = 12.  
Figure 8: Roc curve for comparative study for SVM, MaxEntropy and Random Forest algorithms, foreach dataset as the first module for filtering the tweets.
## 3 Discussion
### Space-Borne Radar Interferometry System
Taking advantage of the higher resolution and shorter wavelength of the X-band data, it is possible to use fewer images and to obtain estimations of the mean velocity with a precision comparable to the C-band data [61]. Angle of incidence: The incidence angle is defined as the angle between the direction of the incident radiation and the normal of the local geoid. It is the major factor that influences the radar response of a scatterer and may lead to geometric distortion effects, known as \"shadow\" and \"layover\". No measuring target can be extracted for interferometric processing in the shadowing area. For the \"layover\" effect, this brings major challenges for separating interfering scatterers distributed over different heights, although d-TomoSAR provides a feasible solution. Therefore, it is important to select an InSAR system with an appropriate incidence angle based on the landscape of the heritage site under consideration. Revisit cycle: In satellite repeat-pass interferometry, the revisit cycle has a direct influence on the accuracy of deformation velocity measurements. During a long revisit cycle, temporal decorrelation caused by changes in backscattering characteristics limits the effectiveness and applicability of differential radar interferometry for deformation monitoring. Thus, for rapid deformation monitoring, it is necessary to select a radar interferometry system with a shorter revisit cycle in order to capture more details of the deformation process and to avoid phase ramping (because of the periodic characteristics of the interfering waves, the phase value is recorded only in the principal value range of \([-\pi,\pi]\)). The phase ramp is generally defined as the phase shift in interferograms up to an integer multiple of \(2\pi\) caused by atmospheric effects, orbit errors, deformation, topography, noise _etc_. For slow deformation monitoring, a radar interferometry system with a relatively longer revisit cycle can be used. Nowadays, the satellite constellation techniques are widely used in the new generation of space-borne radar interferometry systems. They greatly shorten the revisit cycle and offer advantages for achieving near-real-time deformation monitoring over heritage sites with high precision. The relationship between satellite flight path and geometric features of archaeological structures: While the satellite flight path is parallel to the linear target, the radar echoes are strong when caused by the dihedral scattering, resulting in the target being seen as a strong scatterer in the radar image [66, 67]. Consequently, for linear heritage sites, such as ancient river courses, city walls and bridges, it is better to use an InSAR system whose satellite flight path is parallel to the target's linear structure. In addition, considering the relatively small width of the linear targets in most cases, applying a radar interferometry system with high spatial-resolution, such as TerraSAR/TanDEM-X or COSMOS-SkyMed, can help to optimize the deformation investigation.
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 4 Implementation and Methodology Overview
### Experimental Setup and Metrics  
For consistency and experimental reproducibility, a dedicated testbed supporting both wired (Ethernet) and wireless (WiFi) configurations was designed. As illustrated in Figure 3, all experiments were conducted exclusively on Raspberry Pi 4 devices (quad-core ARM Cortex-A72, 64 bit, 1.5 GHz) running Raspberry Pi OS Lite (64 bit). Each device was statically assigned a single MQTT role, publisher, subscriber, or broker, with strict role isolation and was continuously connected to a power meter throughout the experiments, enabling the direct measurement of energy consumption for each test scenario.  
Depending on the configuration, the devices were interconnected via either a Layer2 switch (Ethernet; Figure 3a) or a dedicated WiFi access point (Figure 3b). The wired configuration eliminates wireless channel variability, facilitating accurate baseline measurements, while the wireless setup reflects real-world conditions. Consequently, metrics such as CPU utilization, execution time, energy consumption, and goodput are subject to the influence of the communication channel, whereas memory requirements remain largely unaffected by channel conditions.  
The MQTT broker, implemented using the Eclipse Mosquito server, operates as a relay node for message exchange, coordinating communication between clients. The broker settings were adjusted across phases to reflect both TLS 1.3-secured and plaintext sessions. Custom Python 3.11 scripts were developed for the publisher and subscriber roles to allow for the precise specification of payload size, TLS version (1.3), cryptographic primitives (i.e., ECDHE using X25519, Ed25519), and cipher suites (i.e., AES_256_GCM and Chacha20_Poly1305). System orchestration and performance monitoring were automated with shell-based wrapper scripts, invoking external profiling tools such as perf for CPU  
Figure 3: Experimental setups for MQTT communication of Raspberry Pi 4\(B\) broker, publisher, and subscriber over Ethernet and WiFi.  
measurements, and total wall-clock duration and /usr/bin/time -v for resource usage statistics. The metrics used include CPU utilization, peak Resident Set Size (RSS) memory, and client-to-broker and broker-to-client goodput.  
Each experimental scenario was carefully designed to isolate the performance impact of individual TLS phases on a per-role basis. To ensure measurement fidelity, custom message patterns were generated and synchronized across devices, minimizing external interference. The methodology for each TLS phase and the corresponding message pattern details are described in the following sections.