esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
**Introduction:** Scattering of kernels due to angular dehiscence is a key bottleneck in mechanized harvesting of oilseed rape. **Materials and Methods:** In this study, a dual-track \"genotype-phenotype\" screening strategy was established by innovatively integrating high-throughput KASP molecular marker technology and a standardized random collision phenotyping system for the complex quantitative trait of angular resistance. **Results:** Through the systematic evaluation of 634 oilseed rape hybrid progenies, it was found that the KASP marker S12.68, targeting the cleavage resistance locus (BrSHP1) on chromosome C9, achieved a 73.34% introgression rate (465/634), which was significantly higher than the traditional breeding efficiency (<40%). Phenotypic characterization screened seven excellent resources with cracking resistance index (SRI) > 0.6, of which four reached the high resistance standard (SRI > 0.8), including the core materials NR21/KL01 (SRI = 1.0) and YuYou342/KL01 (SRI = 0.97). Six breeding intermediate materials (44.7-48.7% oil content, mycosphaareella resistance MR grade or above) were created, combining high resistance to chipping and excellent agronomic traits. For the first time, it was found that local germplasm YuYou342 (non-KL01-derived line) was purely susceptible at the S12.68 locus (SRI = 0.86), but its angiosperm vascular bundles density was significantly increased by 37% compared with that of the susceptible material 0911 (_p_ < 0.01); and the material 187308 (SRI = 0.78), although purely susceptible at S12.68, had a 2.8-fold downregulation in expression of the angiosperm-related gene, BnIND1, and a 2.8-fold downregulation of expression of the angiosperm-related gene, BnIND1. expression was significantly downregulated 2.8-fold (q < 0.05), indicating the existence of a novel resistance mechanism independent of the primary effector locus. **Conclusions:** The results of this research provide an efficient technical platform and breakthrough germplasm resources for oilseed rape crack angle resistance breeding, which is of great practical significance for promoting the whole mechanized production.  
**Keywords:** kale-type oilseed rape; crack angle resistance; KASP marker; germplasm resources; random collision method; molecular breeding
## 4 Evaluation Index of ORC System  
Simulink software 8.5 was used to do a thermodynamic simulation of the ORC system. The modeling process and verification of the system model can be found in the reference [17]. Thermal efficiency and the net output power are employed as the key evaluation indices of the ORC system's thermodynamic operation performance in this research, and the ship's fuel savings and carbon emission reduction are used to evaluate the system's energy saving and environmental protection.  
As stated in Equation (5), the net output power of the ORC system is defined as the difference between the output power of the expander and the power consumption of the working fluid pump.  
\[W_{net}=W_{\text{exp}}-W_{pump} \tag{5}\]  
Equation (6) exhibits the definition of the thermal efficiency for the BORC system, which quantifies the proportion of the system's net output power to the heat assimilated by the working fluid within the evaporator.  
\[\eta_{BORC}=\frac{W_{net}}{Q_{vapor}}=\frac{W_{\text{exp}}-W_{pump}}{Q_{vapor}} \tag{6}\]  
For the PORC system, the total heat consumption includes the heat absorbed in the preheater \(Q_{JW}\), as shown in Equation (7), because the system increases the waste heat of cylinder liner cooling water as the heat source of the preheater.  
\[\eta_{PORC}=\frac{W_{net}}{Q_{vapor}+Q_{Jw}}=\frac{W_{\text{exp}}-W_{pump}}{Q _{vapor}+Q_{Jw}} \tag{7}\]  
The fuel savings \(M_{oil}\) can be calculated from the net output work used by the ORC system for power generation, as shown in Equation (8).  
\[M_{oil}=\frac{W_{net}\times SFOC_{AE}\times t}{10^{6}} \tag{8}\]  
where: \(SFOC_{AE}\) is the fuel consumption rate of a diesel engine, 215 g/kWh [20]; \(t\) is the time of system operation in h.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ORC Design Parameters** & **Value** \\ \hline Isentropic efficiency of working medium pump (\(\eta_{\text{pump}}/\%\)) & 80 \\ Isentropic efficiency of expander (\(\eta_{\text{exp}}/\%\)) & 80 \\ Boiler thermal efficiency (\(\eta_{\text{g}}/\%\)) & 90 \\ Evaporator heat transfer area (\(\Delta_{e}/\text{m}^{2}\)) & 370 \\ Evaporator pinch point temperature (T\({}_{e}\)/K) & 5 \\ Heat transfer area of condenser (\(\Delta_{\text{c}}/\text{m}^{2}\)) & 93 \\ Condenser pinch point temperature (T\({}_{e}\)/K) & 5 \\ High-temperature steam inlet temperature (T\({}_{\text{v}}\)in/K) & 453.15 \\ High-temperature steam outlet temperature (T\({}_{\text{v,out}}\)/K) & 443.15 \\ Seawater inlet temperature (T\({}_{\text{w,in}}\)/K) & 293.15 \\ Seawater outlet temperature (T\({}_{\text{w,out}}\)/K) & 303.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ORC system design parameters.  
The fuel saving rate is expressed as the percentage of fuel savings to total fuel consumption of the main engine, as shown in Equation (9).  
\[F=\frac{M_{oil}}{M_{f}}\times 100\% \tag{9}\]  
Carbon emissions are able to be minimized by reusing waste heat from the ship's main engine, as illustrated in Equation (10).  
\[M_{CO_{2}}=M_{oil}\times C_{F} \tag{10}\]  
where is the CO2 emission factor of fuel oil, and the heavy fuel oil is 3.1t-CO2/t-fuel [21].
## 4 Discussion
Accounting for this potential bias is essentially the same as shifting the ionosonde profiles up in altitude, as performed in Sections 3.1 and 3.4, which significantly improves agreement between the two approaches for measuring lower ionosphere EDPs. As discussed in [33], the ionosonde EDP uncertainty increases further down in altitude from the E-layer peak given the assumption of a parabolic shape to describe a Chapman layer. From these uncertainties, we believe that the shifted profile comparison is more appropriate for analyzing differences between the GNSS-RO and ionosonde-derived EDPs. Figure 20: Virtual heights calculated from ionosonde EDPs compared with ionosonde virtual height measurements. Interestingly, there is a negative bias for the virtual heights calculated from the profiles using a ray tracer. Additionally, we ignored ionospheric tilts using the ray tracer as we only have EDP estimates at a single location. This assumption is not valid near dawn/dusk when large ionospheric tilts are present [48], which is a time period included in our analysis due to the lower \(fmin\) values. For sites such as Fortaleza, where almost all of the measurements occur near the dawn/dusk terminator (Figure 2), these ionospheric tilts can be the cause of the altitude differences and biases between the RO/ionosonde EDPs and the direct ionosonde virtual height measurements. Extending this analysis to a larger scale comparison that focuses on \(foE\) and \(hmE\) would allow for the removal of profiles during the dawn/dusk terminator to determine the impact of these ionospheric tilts on the comparison.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 2 Background
### Defence of Identity Privacy
#### 2.4.3 Quic  
QUIC [13] is a transport layer protocol built on top of the User Datagram Protocol (UDP), and is designed to function as an alternative to the Transport Control Protocol (TCP). It was designed with several improved functional and performance characteristics including improvements to privacy and security. However, while transport-layer protocols are able to meet some of the challenges of linkability of packets _within_ a flow, they still rely on the use of network layer IP addresses. So, even with the proposed privacy improvements of QUIC, network-layer identity privacy is still beneficial. QUIC's proposed privacy solutions apply to different concerns from this work, although both solutions could be used together to resolve each of these concerns without disrupting the other defence.
# An Alternative Series Solution for Free Vibration Analysis of Asymmetric L-Shaped Membranes
## 2 Formulations
The displacement continuity conditions across \(S_{h}\) and \(S_{v}\) are, respectively, determined as follows:  
\[u_{1}(x,\,y)=u_{2}(x,\,y),\,y=0,\,\,-d\leq x\leq 0, \tag{15}\]  
\[u_{2}(x,\,y)=u_{3}(x,\,y),\,x=0,\,\,-h\leq y\leq 0\;. \tag{16}\]  
Multiplying Equations (15) and (16) by a succession of sine functions, integrating over appropriate intervals, and utilizing the orthogonal property of sine functions leads to the following relations:  
\[A_{n}=-C_{n}, \tag{17}\]  
\[B_{n}=-D_{n}. \tag{18}\]  
Next, consider the slope continuity conditions across \(S_{h}\) and \(S_{v}\) as follows:  
\[\frac{\partial\,u_{1}(x,\,y)}{\partial\,y}=\frac{\partial\,u_{2}(x,\,y)}{ \partial\,y},\,y=0,\,\,-d\leq x\leq 0\;, \tag{19}\]  
\[\frac{\partial\,u_{2}(x,\,y)}{\partial\,x}=\frac{\partial\,u_{3}(x,\,y)}{ \partial\,x},\,x=0,\,\,-h\leq y\leq 0\;. \tag{20}\]  
Similarly, applying integration and the orthogonality of sine functions to Equations (19) and (20), eliminating the unknown coefficients \(B_{n}\), and rearranging and truncating the summation indices after \(N\) terms produces a system of linear equations written in a matrix form as follows:  
\[\left[\mathcal{R}\;_{ij}(k)\right]\left\{ \begin{array}{c}C_{1}\\ C_{2}\\ \vdots\\ C_{N}\end{array} \right\}=\left\{ \begin{array}{c}0\\ 0\\ \vdots\\ 0\end{array} \right\}, \tag{21}\]  
with  
\[\mathcal{R}\;_{ij}(k)=\sum_{n=1}^{N}\mathcal{F}_{n,i}\;\mathcal{Q}_{n,i}^{h} \;\mathcal{H}_{j,n}\;\mathcal{Q}_{j,n}^{v}-\delta_{ij}\;,\,i=1,\;2,\cdots,N,\, j=1,\;2,\cdots,N, \tag{22}\]  
in which  
\[\mathcal{F}_{n,i}=\left(-\frac{2\;n\pi}{d\;h\;\xi_{i}}\right)\;\frac{\text{ csch}(\varphi_{n}\;d)}{\text{coth}(b\;\xi_{i})+\text{coth}(h\;\xi_{i})}, \tag{23}\]  
\[\mathcal{Q}_{n,i}^{h}=-\frac{i\;\pi d}{\left(i\;\pi\right)^{2}+\left(\varphi_ {n}\;d\right)^{2}}\text{sinh}(\varphi_{n}\;d), \tag{24}\]  
\[\mathcal{H}_{j,n}=\left(-\frac{2j\;\pi}{d\;h\;\varphi_{j}}\right)\;\frac{ \text{csch}(h\;\xi_{n})}{\text{coth}(a\varphi_{n})+\text{coth}(\varphi_{n}\;d )}, \tag{25}\]  
\[\mathcal{Q}_{j,n}^{v}=-\frac{n\pi\;h\;h}{\left(n\pi\right)^{2}+\left(h\;\; \xi_{j}\right)^{2}}\text{sinh}(h\;\;\xi_{j}), \tag{26}\]  
and \(\delta_{ij}\) is the Kronecker delta function. Given that Equation (21) represents a generalized matrix eigenvalue problem (a system of \(N\) equations with \(N+1\) unknowns), the eigenvalues (i.e., the natural frequencies of the membrane) can be evaluated using the determinant equation as follows:  
\[\text{det}\left[\mathcal{R}\;_{ij}(k)\right]=0. \tag{27}\]Once the \(n\)-th eigenvalue has been identified, calculating the corresponding eigenmode (natural mode) becomes a relatively straightforward process. The desired precision determines the number of truncation terms (i.e., \(N\)).
## 3 Analysis of the DQL Method
### Benchmark for Step Strategies
#### 3.2.1 Stopping Conditions  
In order to benchmark these strategy combinations, we need to define the stopping conditions. For our application, we hope to find an approximate solution that is close to an actual solution and stable enough for us to conclude that it is close to a critical point. We therefore stop when both the search step length and infinity norm of the centred simplex gradient are small enough. Three tolerance parameters \(\epsilon_{\
abla}\), \(\epsilon_{\text{MAX\_STEP}}\), and \(\epsilon_{\text{MIN\_STEP}}\) are used to define the stopping conditions.  
The first parameter \(\epsilon_{\
abla}\) defines the tolerance for the infinity norm of the centred simplex gradient. If  
\[\left\|\
abla_{c}f(x_{\text{best}}^{k};\delta^{k}D^{k})\right\|_{\infty}=\max _{i}\Bigl{\{}\left\|\
abla_{c}f(x_{\text{best}}^{k};\delta^{k}D^{k})_{i} \right\|\Bigr{\}}<\epsilon_{\
abla}, \tag{28}\]  
then the current solution meets our stability requirement. However, if the current search step length is too large, then the gradient approximation is not accurate enough to stop. Thus, we use \(\epsilon_{\text{MAX\_STEP}}\) to restrict the search step length. When  
\[\delta^{k}<\epsilon_{\text{MAX\_STEP}}, \tag{29}\]  
the search step length meets our accuracy requirement. When both stability and accuracy requirements (Equations (28) and (29)) are met, the method stops. The last parameter \(\epsilon_{\text{MIN\_STEP}}\) is a safeguard parameter to stop the method whenever the search step length is so small that it could lead to floating-point errors. When  
\[\delta_{k}<\epsilon_{\text{MIN\_STEP}}, \tag{30}\]  
the method terminates immediately. In addition, the methods stop when the number of function calls reaches max_call. This safeguard prevents the method from exceeding the evaluation budget.  
In our benchmark, the parameter settings are shown in Table 2. Since the accuracy of the centred simplex gradient is in \(O(\left(\delta^{k}\right)^{2})\)[17], we take \(\epsilon_{\
abla}\) to be \(\epsilon_{\text{MAX\_STEP}}^{2}\).
## 4 Treatment Advancements
### Diabetes Treatments
As AI and ML technology advances, integration with wearables, genetic data, and decision support systems holds immense potential for personalized and effective diabetes management strategies. _Ultrasound Neuromodulation with liver-targeted peripheral focused ultrasound stimulation (pFUS)_: pFUS shows to be a promising new treatment modality that could be used as a non-pharmaceutical adjunct or even an alternative to current drug treatments in diabetes. The technique has already demonstrated its effectiveness in alleviating obesity and obesity-related complications in an in vitro study on mice [131, 134]. More recently, it was demonstrated in a phase one clinical trial to reduce homeostatic model assessment for insulin resistance (HOMA-IR) scores by lowering fasting insulin [135, 136]. This non-invasive procedure uses ultrasound waves to modulate nerve activity and liver functions, thereby influencing glucose and lipid metabolism, with promising applications in obesity treatment, based on the utilization of a specially designed ultrasound system that is equipped with software allowing for controlled pulsed ultrasound stimulation [137]. The attending physician, upon identifying the area of interest through ultrasound imaging, activates the pulsed stimulus using a control button. Both the imaging and treatment procedures are conducted using a single curved abdominal transducer [136]. By stimulating specific regions of the liver, particularly the hepatic nerves, pFUS can potentially reduce inflammation and insulin resistance and improve metabolic control, which are critical factors in managing obesity and diabetes [135, 136, 137]. These advancements paint a hopeful picture for the future of diabetes management. The integration of these novel treatments with traditional therapies offers the potential for more personalized and effective management strategies, ultimately improving the quality of life for individuals living with diabetes. Furthermore, the ongoing research on cellular therapies holds promise for a future free from the burden of diabetes.
## 1 Introduction
### Motivations  
It is essential to emphasize the critical role of efficient spectrum utilization in CRNs, where unlicensed users seek to leverage unused spectrum bands without causing interference to licensed PUs. As the arrival rate of licensed users increases, the potential for SU deprivation grows, necessitating robust mechanisms to mitigate waiting-time disparities. The proposed models, integrating M/D/1 queuing frameworks with both non-preemptive and preemptive priority mechanisms, aim to address this challenge by optimizing the average total waiting time for both PUs and SUs. By contrasting these models with an M/D/1 system incorporating PU delay, the study seeks to quantify the performance improvements achieved through priority-based scheduling strategies. Through quantitative evaluations and Monte Carlo simulations, the efficacy of the proposed models is rigorously assessed, demonstrating significant enhancements in waiting-time metrics under both non-preemptive and preemptive priority schemes. Moreover, by extending the analysis to real-time systems with finite buffer capacities, the study underscores the practical relevance of the proposed approaches to scenarios wherein resource constraints must be considered. Overall, the study not only contributes to advancing the theoretical foundations of queuing theory in cognitive radio networks, but also offers practical insights for optimizing system performance in real-world deployment scenarios, thereby facilitating more efficient and equitable spectrum utilization for all users.  
Apart from the previously mentioned hypotheses, we propose that there exists a possibility in this cell to disseminate the bandwidth condition to every user. There are two outcomes for this status: either the bandwidth is open for LPUs to sense, or it is temporarily closed off (blocked) to LPUs. This proposal is unrestrictive for radio access networks.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 5 Performance Evaluation  
In this section, we evaluate the performance of the proposed heuristic 5G core slice provisioning algorithms RT-CSP+ and RT-CSP. First, we describe the experimental settings for implementing our algorithms. Then, we present the results obtained from extensive evaluation experiments and analyze the results by comparing them with the state-of-the-art algorithms.
# Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
###### Abstract
[16] developed a wireless and IMU-based system for monitoring patient motion with real-time 3D reconstruction. IMU systems have also been used by practitioners for gait and lower limb rehabilitation [17; 18]. IMUs perform well when tracking the orientation of a moving object, thus, coaches and athletes use them to assess athletic performance [19; 20]. More recently, IMUs have been used in the workplace to quantify specific exposures that may increase risk of injury. A smart garment using two IMU sensors was introduced by [PERSON] et al. [21] to monitor shoulder posture for treating WMSDs. Relative time series data and kinematic data from the wearable sensors (IMUs) are used to summarize the percent time spent in different physical activities and the probability of being at high risk for WMSDs [22; 23]. The IMU system (17 IMUs) with classification models introduced by [PERSON] and [PERSON] [24] and [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [25], classified MMH tasks for real-time applications with a higher accuracy than observational methods. These studies provide support for the use of wearable devices in predicting OPAs actively and continuously in diverse work environments, even using fewer sensors. However, the performance of using wearable devices on classifying a broader range of OPAs such as crouching, kneeling and overhead work has not been evaluated. Further, there is little, if any, work published on predicting OPAs when combined in typical simulated work tasks. Therefore, the objective of this study was to apply deep learning models to data from eight IMUs to predict physical activities performed during simulated occupational tasks. If wearable devices can be used to predict OPAs with higher accuracy, reliability and efficiency than self-report, observational or direct methods, job descriptions and return to work programs can be standardized more effectively.
# A Review of PCI Express Protocol-Based Systems in Response to 5G Application Demand
## 4 Technical Strategy Using PCI Express, CXL, USB, NVMe, and Security
### Technical Strategy through PCIe  
In 2021, PCIe 5.0 was introduced. Related products to PCIe 5.0 started on different product plans. PCIe 4.0 and PCIe 5.0 for NVMe/M.2 storage and graphic devices have promoted in the market since 2021.
## 3 Results and Discussion
### Temperature Reduction  
The test case of hybrid NV with 3 ACH is chosen as a typical representative case for the analysis of the reduction of the operative temperature (OT) towards the favorable application of the spackling material. Note that the analysis is based on a representative office cubicle in the middle of the building. This evidently reveals the essential features of the benefits of the usage of PCM spackling.  
The dynamics of the indoor OT of the representative cubicle is depicted in Figure 7a,b. As expected, a significant cooling effect during night times is observed for the case of \"NV without PCM spackling\" owing to the effect of NV alone. It can be realised that the mechanical cooling starts operating when OT reaches 26 \({}^{\circ}\)C arising from the control strategy. The presence of PCM evidently contributes to additional temperature reduction and one can notice a delay of the occurrence of the peak temperature (\(\approx\)1 h for both cases with PCM layer thickness of 2 mm and 4 mm). The maximum reduction of temperature is found to be \(\approx\)1.1 \({}^{\circ}\)C and \(\approx\)1.9 \({}^{\circ}\)C with increasing thickness of the PCM layers. In Figure 7b, it can be seen that the effect is consistent for the whole simulated period, where daily variations in the OT is noticeably attenuated by the PCM. Also, with the increase in the thickness of the PCM layer a narrower band of high and low temperature peaks is formed across the simulation period. This reveals the effectiveness of the PCM, essentially augmenting the thermal mass and inertia of the buildings to resist temperature fluctuations.