esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Degradation Behaviour and Tuning of Sulfenamide Structure  
Thermal stability of sulfenamides is affected by the \(R\)-groups attached to S (R1) and N (R2, R3). Low basicity and increased number of substitutions of the nitrogen atom increase the stability. Thermal stability decreases, if R1 contains groups capable of anchimeric assistance in cleavage of the S-N bond, whereas electron withdrawing substituents on R increase the thermal stability [33]. Delocalization of the nitrogen non-bonding electron pair decreases the basicity of amine, and therefore electron withdrawing groups on R2 and R3 decrease the likelihood of N protonation. Effective delocalization can lead to inversion of the trigonal pyramidal geometry of N towards planar trigonal. The latter geometry is beneficial also for the stabilization of the nitrogen radical formed during the cleavage of the S-N bond. Heterocyclic aromatics containing nitrogen atoms bonded to carbonyl groups, as in phthalimide, tend to be highly thermally stable. Nitrogen atoms in aromatic heterocyclic structures, as in group V compounds, or bonded to carbonyl groups, as in group VI compounds, tend to have planar geometry. In addition, sterical hindrance can protect the nitrogen. The effect of structure on thermal stability is illustrated in Scheme 2 for a series of SN-compounds.  
**Scheme 2.** Substituent effect on thermal stability of sulfenamide compounds.  
Short S-N bond length correlates to the good thermal stability. If the distance between sulfur and nitrogen atoms is less than normal 1.73 A for a single bond, the bond exhibits some \(\pi\)-character [34; 35; 36]. Thermal decomposition behaviour of sulfenamide accelerators has been extensively studied for rubber vulcanization. To achieve high vulcanization efficiency while simultaneously maintaining long vulcanization delay (scorch time), sulfenamides with calculational S-N bond length less than 1.67 A and high stability of the dissociated aminyl radicals are particularly suitable [37]. Consequently, the thermal stability is high, but once the SN-bond decomposes, it does not easily recombine.  
Thermal weight loss curves for sulfenamides Ic, IIc, IIIa, Vb, VIa and VIb are shown in Figure 3. Suitable decomposition temperature range for flame retardants used in PP formulations has been marked with a grey border line. The sulfenamide should not decompose during PP processing at temperatures up to 230 \({}^{\circ}\)C. However, it should be activated in the temperature range of 250-360 \({}^{\circ}\)C when the pyrolysis products of PP (aliphatic saturated and unsaturated hydrocarbons) from the surface are becoming sufficiently vaporized for ignition to take place [38]. In this context, it can be stated that all of the tested sulfenamide compounds exhibited appropriate thermal properties for potentially being suitable flame retardants for PP. Unlike the other sulfenamides, compounds Vb and VIa had relatively high char residues of 15-20 wt % at 700 \({}^{\circ}\)C, which was related to their structural capability to form polyaromatic condensation products.
# Traffic Light and Arrow Signal Recognition Based on a Unified Network
## 3 Approach
### Preprocessing Based on Map Information  
Conventional image-based traffic light detection methods can misjudge traffic situations due to features in the background that appear similar to traffic lights. Traffic light detection requires a high level of accuracy because it affects subsequent decisions on vehicle control. Accordingly, we propose a map-based detection approach, not to replace image-based detection methods but to supplement them, with the aim of reducing misjudgment and thus enhancing accuracy.  
In our approach, we integrated a HD map for detecting and recognizing traffic lights. We utilized a pre-constructed HD map with annotated traffic lights, including ID, position, and vertical and horizontal angle information. The position between traffic lights and a vehicle can be obtained using the HD map and the LiDAR data when the vehicle is moving. This information is used to crop an image to obtain an approximate position of a traffic light. Due to the nature of the registration of images and LiDAR data, the traffic lights cannot be correctly identified. Then, the segmented ROI is delivered to neural networks for precisely detecting locations and recognizing light states. Figure 2 shows the results of traffic light detection that uses a combination of image and LiDAR data.  
Figure 1: Flowchart of the proposed approach.
## 3 Results and Discussion
### Field-Scale Quantification of Graphite Content  
Built on Equations (7) and (8) derived from SIP measurements with our laboratory samples, we could estimate the graphite content using the SIP profiles under investigation using the fitted Equations (7) and (8) (see also Table 6). In Figure 10, we present the relative RMS error (rRMSE) of the Cole-Cole fit for each pixel. The rRMSE values are mostly below 20%, which is considered a reasonably good model fit [53]. In the case of profile P1 (Figure 10a-c), M and M\({}_{n}\) resolve for a similar low graphite content (<10%). The graphite content is slightly higher (~13%) within the first 100 m along the profile direction, in a layer extending to the surface and ca. 10 m depth. In the case of P7, Figure 10d,e show a significant difference in the estimated graphite content for estimations using M and Mn\({}_{n}\). For the former, the highest graphite content (~20%) is resolved in the second half of the profile (>75 m along profile distance), whereas the highest graphite content (up to 40%) is observed in the first half of the profile if we use the model based on Mn. Significant discrepancies are also observed for P2 and P2b (Figure 10). The estimations based on Equation (8) using M\({}_{\rm n}\) (obtained from the biggest grain size, d4 and the plugs due to their most probable similarities to the field properties) agrees exactly with the expected graphite-rich area, as obtained from geological (i.e., surface) observations. Hence, these  
Figure 9: Results from the measurements in the commercial graphite sample as well as in the particles from the rock sample by varying NaCl content of the pore fluid versus the Cole-Cole parameters in terms of (**a**) chargeability, (**b**) normalized chargeability and (**c**) relaxation time.  
results demonstrate that Equation (8) is best suited to estimate the graphite content at sites where the graphite content is significantly larger than 20%.  
[PERSON] et al. concluded that the graphite content could be estimated with their linear model in the case of low graphite content, while in the case of high graphite content, high conductivity is observable [16]. Our results suggest that chargeability can be used to estimate graphite content in the case of low graphite content. Although [PERSON] et al. suggest that the normalized chargeability is meaningless in the case of ore bodies, our results suggest that we can estimate graphite content with the normalized chargeability [54]. Further work is required to include the relaxation time in the empirical model to achieve a more accurate graphite content estimation.
## 2 Methods and Materials
### Materials
#### 2.2.2 Image Quality Metrics  
To evaluate image quality, the metrics are computed for the envelop images (before log-compression). The mean resolutions in the axial and lateral directions are automatically measured as described in [15]: the full width at half maximum (FWHM) is averaged over the 20 point scatterers for the numerical phantom (Figure 2a-d), and over seven point scatterers embedded in the speckle for experimental phantom (Figure 2i-l). This averaging is necessary as the FWHM is not spatially constant [17]. The contrast is measured according to two criteria: the mean contrast ratio (CR), and the mean contrast-to-noise ratio (CNR):  
\[CR=\frac{1}{K}\sum_{k=1}^{K}20.\log_{10}\left(\frac{\mu_{speckle,k}}{\mu_{cyst,k}}\right), \tag{13}\]  
\[CNR=\frac{1}{K}\sum_{k=1}^{K}20.\log_{10}\left(\frac{|\mu_{speckle,k}-\mu_{cyst,k}|}{\sqrt{\frac{\sigma_{speckle,k}^{2}+\sigma_{const,k}^{2}}{2}}}\right), \tag{14}\]  
where \(k\) is the index of the cyst. \(\mu_{speckle,k}\) (respectively \(\mu_{cyst,k}\)) is the mean pixel amplitude in the speckle ring (respectively inside the \(k^{th}\) cyst). \(\sigma_{speckle,k}^{2}\) (respectively \(\sigma_{cyst,k}^{2}\)) is the variance of the pixel amplitude in the speckle ring (respectively inside the \(k^{th}\) cyst). For simulation, the phantom is composed of \(K=9\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Number of Elements \(N\) & 128 & Excitation (number of cycles) & 2.5 \\ Pitch (mm) & 0.3 & Transmit frequency \(f_{0}=f_{s}/4\) (MHz) & 5.2 \\ Element width (mm) & 0.27 & Sampling Frequency \(f_{s}\) (MHz) & 20.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transmission settings for the PICMUS challenge [15].  
cysts (Figure 2e-h), and, for the experiment, the phantom is composed of \(K\) = 2 cysts (Figure 2m-p). The full details of this dataset are available in [15].
# Link Load Correlation-Based Blocking Performance Analysis for Tree-Type Data Center Networks
## 3 Blocking Performance Analysis for Tree-Type Data Center Networks  
### Network Blocking Probability Definition  
According to [14], the blocking probability of the DCN is defined as the weighted sum blocking of all types of connections:  
\[P_{net}=\frac{\sum_{i}\rho_{c,i}P_{c,i}}{\sum_{i}\rho_{c,i}}=\sum_{i}P_{c,i}r_ {c,i}, \tag{1}\]  
where \(\rho_{c,i}=\lambda_{c,i}/\mu_{c,i}\) and \(P_{c,i}\) are the load and blocking probability of connections \(C_{i}\), respectively, and \(r_{c,i}=\frac{P_{c,i}}{\sum_{i}\rho_{c,i}}\) is the normalized arrival rate of \(C_{i}\), i.e., the ratio of the arrival  
Figure 1: Links and connections in a Two-layer Tree DCN.  
rate of \(C_{i}\) to the total arrival rate of all connections. In a two-layer tree DCN with tree factor \(K\), \(N_{s}\) physical servers are connected, and there are \(N=(\frac{N_{s}}{2})\) connection types in total. The connections can be mainly classified into two main classes, according to whether they go through the core switch: one is the Edge Class (EC) connections, whose transmission path is composed of two \(L_{0}\) layer links under the same edge switch; the other is the Core Class (CC) connections, whose path is composed of two \(L_{0}\) layer links and two \(L_{1}\) layer links. The EC and CC connections include \(N_{1}=K(\frac{K}{2})\) and \(N_{2}=N-N_{1}\) types of connections, respectively. We assume that the arrivals of all connection types are independently and identically distributed, (i.e., a uniform traffic distribution is considered) and, then, in the symmetric DCN, the connections of the same class have the same blocking rates. Thus, the blocking probability for the two-layer tree DCN with factor \(K\) can be simplified as  
\[P_{net}=P_{cc}r_{cc}+P_{cc}r_{cc}=P_{cc}\frac{N_{1}}{N}+P_{cc}\frac{N_{2}}{N}= \frac{P_{cc}}{(K+1)}+\frac{P_{cc}K}{(K+1)}, \tag{2}\]  
where \(r_{cc}\) and \(r_{cc}\) are separately the normalized total arrival rates of EC connections and CC connections, while \(P_{cc}\) and \(P_{cc}\) are the blocking probabilities of EC connections and CC connections, respectively.
# A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
## 2 Radar Architecture Design
### Frequency Spectrum Selection  
In the design of FMCW radar systems, the selection of operating frequency band is the basis of radar device selection and antenna design. The primary concern is the elec  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Index Name** & **Value** \\ \hline
1 & Maximum detection range R\({}_{\mathrm{max}}\) & 150 m \\
2 & Range resolution \(\Delta\)R & 0.15 m \\
3 & Maximum speed range V\({}_{\mathrm{max}}\) & \(\pm\)4 m/s \\
4 & Velocity resolution \(\Delta\)R & 0.0625 m/s \\
5 & Field Angle range \(\theta_{\mathrm{max}}\) & \(\pm\)15\({}^{\circ}\) \\
6 & Angular resolution \(\Delta\theta\) & 3\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scenario parameters.  
Figure 2: Radar installation diagram.  
tromagnetic waves attenuation in the atmosphere. For millimeter-wave, electromagnetic waves attenuation is mainly caused by water vapor and oxygen. Current scientific studies reveal that to ensure optimal performance, it is advisable to avoid atmospheric window frequencies such as 22.4 GHz, 60 GHz, 118 GHz, and 184 GHz [16; 17]. The other consideration is the performance of the frequency band. The advantages and disadvantages of each are listed in the following Table 2. Among the main frequencies, high-band radar sensors (W-band radar) can achieve a range resolution of 3.75 cm (compared to 60 cm for K-band radar) with 3 times better velocity resolution and antenna size than K-band radar. The higher resolution allows better separation of objects, which improves environmental modeling and object classification and helps sensors achieve better distance detection. Based on the scenario parameters given in Section 2.1, the W-band millimeter wave radar is better for blade clearance measurement systems due to its lower atmospheric attenuation and better balanced performance in terms of accuracy and system size.
###### Abstract  
Change of direction (COD) ability is essential for sport performance in high level team sports such as basketball, however, the influence of asymmetries on COD ability is relatively unknown. Forty-three junior and senior level elite basketball players performed isometric hip and trunk strength testing, passive hip and trunk range of motion testing, and unilateral horizontal and vertical jumps, as well as the T-test to measure COD performance. Mean asymmetry values ranged from 0.76% for functional leg length up to 40.35% for rate of torque development during hip flexion. A six-variable regression model explained 48% (\(R^{2}=0.48;p<0.001\)) of variation in COD performance. The model included left hip internal/external rotation strength ratio, and inter-limb asymmetries in hip abduction rate of torque development, hip flexion range of motion, functional leg length, single leg triple jump distance, and peak torque during trunk lateral flexion. Results suggest that the magnitude of asymmetries is dependent of task and parameter, and using universal asymmetry thresholds, such as \(<\)10 %, is not optimal. The regression model showed the relationship between asymmetries and COD performance. None of tests were sufficient to explain a complex variable like COD performance.  
asymmetry; agility; basketball; strength; power; inter-limb asymmetry +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this paper, we propose a simple and fast method to extract accurate COM parameters for designing SAW devices. It is based on the FEM and combines the stationary analysis with modal analysis. We have made a comprehensive comparison between the proposed method and the other two existing methods. Results show that the proposed method holds higher accuracy and more efficiency than the other FEM-based methods. We have also examined the proposed method by extracting COM parameters of some common SAW substrates (ST quartz, ST-25\({}^{\circ}\)X quartz and Y-51.25\({}^{\circ}\)Z LiTaO\({}_{3}\)), and compared our extracted  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**COM Parameter** & **Measured Value**[27] & **Calculated Value**[27] & **Our Results** \\ \hline \(v\) (m/s) & 3239.7 & 3242.1 & **3240.7** \\ \(\kappa_{p}\) (= \(\kappa\lambda\)) & 0.00584 & 0.0049 & **0.00575** \\ Arg(\(\kappa\)) [deg] & 95.68 & 103.17 & **97.96** \\ \(\alpha\lambda/\sqrt{\omega C\lambda}\) & 0.0214 & 0.0205 & **0.0224** \\ \(C_{n}\) (pF/m) & 50.77 & 49.27 & **49.11** \\ \hline \hline \end{tabular}
\end{table}
Table 4: COM parameters for ST-25\({}^{\circ}\)X quartz substrate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**COM Parameter** & **Measured Value**[23] & **Calculated Value**[26] & **Our Results** \\ \hline \(v\) (m/s) & 3146 & 3152 & **3143** \\ \(\kappa_{p}\) (= \(\kappa\lambda\)) & \(-\)0.015 & \(-\)0.021 & \(-\)**0.023** \\ Arg(\(\kappa\)) [deg] & \(-\) & \(-\) & **0** \\ \(\alpha_{n}\) (f/0)(\(\sqrt{1/\Omega\lambda}\)) & 2.71 \(\times\) 10\({}^{-4}\) & 2.84 \(\times\) 10\({}^{-4}\) & **2.85 \(\times\) 10\({}^{-4}\)** \\ \(C_{n}\) (pF) & 1.95 & 1.92 & **1.953** \\ \hline \hline \end{tabular}
\end{table}
Table 3: COM parameters for ST quartz substrate.  
results with those reported in the other studies. Results show that the extracted COM parameters by the proposed method are in an excellent agreement with the reported values in the other literatures, which implies that the method can be utilized in the practical design of SAW devices.  
Conceptualization, X.M., H.L., P.C. and X.J.; methodology, X.M.; formal analysis, H.L.; data curation, X.M.; writing, review and editing, X.M.; supervision, X.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 52075339.  
Not applicable.  
The authors declare no conflict of interest.
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### ISM Band-Based Technologies
#### 2.2.2 LoRa  
Long Range (LoRa) is a proprietary technology from Semtech [8]. Based on Chirp Spread Spectrum (CSS) modulation, it can use several bands of the ISM sub-GHz spectrum depending on the geographical location. LoRa communications are reasonably resilient to detection and jamming, and are immune to Doppler deviation. LoRa offers several parameters that can be modified to adjust the trade-off between range and data rate (from 0.3 to 50 kbps), e.g., the spreading factor. While LoRa is the technology of the physical layer, LoRaWAN [20] which is supported by the LoRa Alliance, is an open protocol for the MAC and network layers. LoRaWAN describes 3 classes of devices. Roughly, class-A is for heavily energy constrained devices, class-B for moderately energy constrained devices and class-C for always-on devices. FotA updates over LoRa may be conceivable, but hard to achieve. They may need to use multicast class-C devices only, which uses a lot of power [21]. Several scientific studies (e.g., [22; 23] show that LoRa performs well in terms of range when a line of sight is available for communications, but the range is greatly reduced when it is not available, like in an urban environment.  
Several MNO are deploying LoRa (e.g., Orange in France [12]), as well as public community initiative such as The Things Network [24]. A map of the current LoRaWAN worldwide deployment is visible in Figure 5. An additional benefit of using LoRa is the possibility to deploy personal LoRa base stations to extend the network.  
Figure 4: Sigfox coverage map [19]—blue is current coverage/purple is future coverage.
## 4 Results and Discussions
### (6)-(8)  
The \(u\) fringes are vertical and slightly curved, and the \(v\) fringes are approximately horizontally parallel like the elastic stages (1) and (2). The \(u\) fringe pattern indicates that the deformation is still plastic satisfying condition \(\
abla\times\mathbf{\omega}\
eq 0\), but the degree of the plastic deformation is much less than that observed near the right-upper shoulder in (5).  
While the \(v\) fringes display the same general pattern (horizontally parallel) as the elastic stages, the number of fringes is much higher than the elastic stages. Because the fringe patterns are formed with the same time interval and the pulling rate is constant, the total elongation at this stage is the same as the elastic stage. The much higher number of \(v\) fringes indicates that the specimen undergoes compression and stretch. This can be explained, as follows. The \(v\) component of the differential displacement exhibits longitudinal wave characteristics as in the elastic stage. However, because the elastic constant is much lower than before the yield point, the wave velocity, which is in the form of the square root of the elastic constant divided by the density, is significantly lower. The frequency of the longitudinal wave is determined by the external load and, therefore, more or less the same as the elastic stage. Consequently, the wavelength is significantly shorter than the elastic stage. This situation results in the higher fringe density. We can intuitively understand that the lower elastic constant shortens the wavelength by imagining pulling two springs of different strengths. First, pull one end of the stronger spring holding the other end stationary. The spring is stretched uniformly. Every part of the spring along its length is elongated at the same time. Now, pull the weaker spring in the same fashion at the same pulling speed. It is easy to imagine that the part of the spring closer to the moving end is stretched first and then the stretching pattern moves towards the other end along the length of the spring. Moreover, as the next section is stretched the first section tends to be compressed due to the recoiling motion of the spring. Consequently, we see a higher number of compression and stretch patterns. In a solid material, the same feature can be observed. The pattern of compression and stretch represents longitudinal wave characteristics. It is well known that the longitudinal wave velocity is the square root of the ratio of the Young's modulus over density. The longitudinal wave travels at a lower velocity in a softer (less stiff) material. Because the wavelength is proportional to the wave velocity, the softer the material the shorter the wavelength. If the wavelength is shorter for a given material length, a higher number of waves are excited by the pulling action. The higher fringe density that is discussed above corresponds to a lower stiffness.
# Phased System Reliability Modeling and Assessment for Construction of Lunar Scientific Base
###### Abstract  
The construction of a lunar scientific base (LSB) is a complex and gradual process that has to be completed in stages according to mission planning. Reliability assessment is critical for quantifying failure risks and planning complex space missions. In space engineering, reliability tests often encounter small samples and zero-failure data, rendering traditional system reliability assessment methods based on maximum likelihood theory inapplicable. This paper introduces a novel reliability assessment method for phased systems, termed building block assessment (BBA), to address existing limitations. The approach involves the development of a phased reliability model, which avoids correlation issues in reliability analysis. The reliability of the phased system is then assessed bottom-up using test data from subtasks, and the scenario where confidence limits are dependent is also discussed. Numerical examples demonstrate that the BBA method can effectively handle zero-failure data compared with traditional methods, and the reliability of LSB construction is verified to reach 0.9544 with a confidence degree of 0.9.  
Keywords:phased series system; lunar scientific base; building block assessment; reliability confidence limit; zero-failure +
Footnote †: journal: Applied Sciences
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 2 System Model
### Symbol and Frequency Synchronization Algorithms
This technique can estimate the carrier frequency offset within the range \([-\Delta f,\Delta f]\).