esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Analysis  
The bit error rate probability \(P_{b}(\zeta)\) for IEEE 802.15.4 can be calculated [1] as given in the following equation.  
\[P_{b}(\zeta)=\frac{8}{15}\frac{1}{16}\sum_{k=2}^{16}(-1)^{k}\binom{16}{k}e^{2 0\zeta(\frac{1}{k}-1)} \tag{19}\]where \(\zeta\) is the SINR. Let, \(p_{1}\) be the probability for a device having a packet for the transmission and \(\alpha\) be the probability that the channel is found busy during CCA due to data transmission by other ZigBee devices or WiFi devices available in a smart home. Let, \(\lambda_{w}\) be the Poisson packet arrival rates for WiFi devices. Therefore, the channel found busy during CCA duration (\(T_{cca}\)) by the ZigBee device due to WiFi transmission is \(1-e^{-T_{cca}\lambda_{w}}\). Similarly, a ZigBee device found busy due to transmission of any other ZigBee device in the network is \(L_{D}(1-(1-\phi)^{N-1})(1-\alpha)e^{-T_{cca}\lambda_{w}}\), where \(L_{D}\) is the packet length and \(N\) is the number of contending ZigBee devices. Therefore, the probability of finding the channel busy during CCA is given as follows.  
\[\alpha=1-(1-L_{D}(1-(1-\phi)^{N-1})(1-\alpha))e^{-T_{cca}\lambda_{w}} \tag{20}\]  
When the channel access is failed or the current remaining duration in the contention period is insufficient for the transmission, the ZigBee device decides to go for the next backoff. Hence, the probability of going for the next backoff is:  
\[P_{backoff}=p_{2}+(1-p_{2})\alpha. \tag{21}\]
## 7 Conclusions  
This paper applies the concept of cross-layer design considering the random fluctuations of the channel in a coal mine face scenario, and the information intensively embodying network channel variance characteristics was explored from the physical layer and MAC layer to design an anti-fading clustering protocol using management frames and control frames. Moreover, the signal fade margin was also adopted to optimize power. The simulation results indicated that PEAFC-CL possesses a stable network topology and is applicable despite the complexity and variation of the underground environment, effectively prolonging the network lifetime and increasing the data transmission rate. During research with the CMFM-WSN system, network performance is influenced by each layer and a cross-layer technique is widely applied; however, the constraint of all layers cannot be ignored.  
A coal mine face is the most dangerous and important location for mine production. The CMFM-WSN system provides industrial production with technical support and safety supervision assurances. In the future, efforts will be made to study the networking technology under the environment of channel fading and to further probe node deployment, heterogeneous network  
Figure 13: (**a**) Round when the first node dies _vs_. number of nodes; (**b**) Packet loss ratio _vs_. number of nodes.  
integration and QoS in wireless mobile multimedia networks for a coal mine face as a unique application scene. Priority will also be given to studying the transmission reliability of the CMFM-WSN system.  
This work was supported by National Natural Science Foundation of China (Grant No. 51204176), Innovation Fund Project on the Integration of Industry, Education and Research of Jiangsu Province (Grant No. BY2012081), Natural Science Foundation of Jiangsu Province (No. BK20140202), and China 863 Project (No. 2012 AA062103). We would also like to thank the editor and the referees for their valuable comments.  
All authors significantly contributed to the manuscript. [PERSON] is responsible for the theoretical analysis of the routing algorithm. [PERSON] designs the simulation procedure and analyzes the simulation results.  
The authors declare no conflict of interest.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 2 Related Works
### Privacy Protection in Sporadic LBSs  
The privacy challenges of sporadic LBSs directly inform our approach, particularly in balancing privacy protection with system efficiency. The seminal work by [PERSON] et al. [6] established the use of cell access points as data repositories, storing both direct and candidate responses. Although this approach demonstrated the value of distributed storage, its reliance on fixed access points creates potential privacy vulnerabilities that our fully distributed solution addresses. We extend their concept of geographical data distribution while eliminating the dependency on fixed infrastructure.  
Recent approaches have explored various privacy protection mechanisms through three main directions: cryptographic techniques, mobile storage solutions, and hardware-based protection. A groundbreaking contribution by [PERSON] et al. [15] demonstrated that trusted third-party anonymizers could be eliminated using private information retrieval (PIR) techniques. Their approach achieved strong privacy guarantees and protection against correlation attacks through cryptographic methods although at the cost of increased computational overhead. Although our work takes a different approach to eliminating trusted intermediaries through distribution rather than cryptography; both solutions demonstrate the feasibility of privacy preservation without centralized trusted parties.  
On the mobile storage front, [PERSON] et al. [16] proposed using drones as external repositories for k-anonymity, demonstrating the potential of mobile caching points. Our work extends this concept by treating all network participants as potential mobile caches, creating a more robust and scalable system. [PERSON] et al. [17] developed a three-tier cache system combining user caches with cell access points, while [PERSON] et al. [18] introduced a leader-based collaborative scheme.  
In the hardware security domain, recent systems like SecuDB [19] employ Trusted Execution Environments (TEEs) and secure enclaves to protect data during server-side processing. Although TEEs represent a significant advance in securing centralized systems, hardware vulnerabilities such as Meltdown [20] and Specter [21] have demonstrated inherent risks in hardware-based protection. Our MANET-based approach provides an orthogonal solution that could complement TEE-protected servers in hybrid deployments, combining the benefits of decentralized storage with hardware-protected processing when centralized computation is necessary.  
[PERSON] et al. [22] made significant advances by combining caching with k-anonymity while considering temporal query patterns. We build on their temporal awareness in our cache management strategy while extending it to handle the dynamic nature of MANET environments. Their work demonstrated the importance of considering time-based patterns in privacy preservation, which we incorporate into our cache obsolescence handling.
## 3 Data and Methods  
### GNSS, InSAR, and Leveling Data  
High-precision deformation fields are essential for accurately determining fault slip distribution and quantifying the slip rate. In this study, high-quality InSAR, GNSS, and leveling data were used to obtain a high-precision three-dimensional deformation field encompassing the XSHFZ. The InSAR velocity maps in Figure 3 were compiled by processing Sentinel-1 data from ascending and descending tracks spanning the period from 2014 to 2021 [22]. The GNSS velocity field results, relative to the Eurasian framework, are sourced from [PERSON] and [PERSON] [21], spanning from 1991 to 2016. We also employ regional precise leveling data to validate the vertical velocity resulting from the fusion of InSAR and GNSS velocities. The leveling data's observational period spans from 1970 to 2017 [57]. For the detailed processing of the three types of data, refer to the following references [21; 22; 57].
## 3 Results and Discussion
### Morphological Properties
This indicates an augmentation of the peri phase, which was most likely brought about by the first addition of the recycling agent. Since the peri, para, and sal phases are more related to the maltenic fraction of bitumen [24; 25; 26; 27; 28], it is very likely that R2 functions to augment the maltenic fraction of aged bitumen. The second addition of the R2 recycling agent shows a further dilution of the bee structures in the smooth matrix, as they are more separated but less abundant in the aged bitumen's matrix. The third aging cycle shows a few bee structures dispersed in the smooth sal phase, indicating a mere fluxing and softening of the bitumen rather than real rejuvenation, as observed in the case of the R1 recycling agent. The addition of the R2 recycling agent significantly augments the maltenic fraction (para phase). The asphaltenes maintain the size they gained after aging, and are dispersed in an abundant maltenic matrix. In the virgin bitumen sample, the bee structures (catana phase) surrounded by the peri phase are distinctly positioned, showing that they are dispersed in the para phase. Oxidative aging disrupts this colloidal system and causes an increase in size coupled with a clumping together of the bee structures [29]. Technically, a rejuvenator should work by restoring the morphological structure of the aged bitumen to that described for virgin bitumen. Instead, a fluxing/softening agent functions as an oil, and thus interacts with the peri, para, and sal phases, leaving the bee structure morphology unchanged in the  
Figure 4: AFM images of virgin, aged, and R2-modified samples. Figure 3: AFM images of virgin, aged, and R1-modified samples. colloidal matrix. Based on the information provided by AFM analysis, the R1 recycling agent could interact with bitumen's components on a chemical level, reversing the effects of oxidative aging, thus rejuvenating the aged bitumen. The R2 recycling agent, on the other hand, could only augment the maltenic fraction, and did not significantly interact with the bee structures or their morphology. This could mean that it merely fluxes the bitumen by reducing its viscosity.
## 4 Conclusions  
In this paper, the random forest algorithm is used to establish a data-driven ROP prediction model of horizontal wells. The MSE of the training set is 5%, and the test set is 9%, which can accurately predict the ROP of horizontal wells.  
The random forest ROP model and the \"hard-string\" drag calculation model are used as the optimization function, and the maximum ROP and minimum drag are used as the optimization objectives. In the drilling process, the NSGA-II multiobjective optimization model is used to optimize the drilling parameters of the horizontal well. The oil field experiment results show that the multiobjective optimization model for drilling parameters is reliable. Compared with the neighboring well, the ROP of the test well is increased by 10.3%, and the drag is reduced by 4.5% on average. The drilling time is effectively shortened.  
However, the training of the ROP prediction models is purely data driven. Similar to other machine learning models, the interpretability of the model is low, and the reasons why the optimized parameters can improve ROP and reduce drill string drag could not be well explained. Improving the interpretability of data-driven models is an important direction for future research.  
Conceptualization, C.Z.; methodology, C.Z. and Z.L.; investigation, C.X.; data curation, S.Y. and X.X.; writing--original draft preparation, T.P.; writing--review and editing, Y.G.; supervision, X.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are not publicly available because they involve information on Chinese oil fields and need to be kept confidential.  
The author would like to thank the academic salon of the High-Pressure Water Jet Drilling and Completion Laboratory of China University of Petroleum (Beijing) and Zhaopeng Zhu.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Headspace Volatile Analysis Using SPME-GC-MS of Mallab Samples (Roasted/Unroasted)
#### 3.4.3 Acids/Esters  
The relative amounts of both organic and fatty acids detected in the aroma of white mahlab seeds obtained from Greece and Egypt and red Egyptian mahlab unroasted seeds were ca. 22.47, 19.12 and 17.43%, respectively. Greek mahlab seeds, \"WG\", were the most abundant in acids, with valeric acid and its isomer (peaks 5 and 6) accounting for 9.2% among other acids. Valeric acid is a low-molecular-weight carboxylic acid with a very unpleasant odor, which commonly occurs in the valerian flowering plant [41]. It was previously reported in another _Prunus_ species, namely Japanese apricot (_P. mume_) [42]; however, this is the first report of its detection in _P. mahaleb_. This acid is primarily used in the synthesis of esters to generate pleasant odors (fruity flavors) used in perfumes, cosmetics and food additives such as ethyl valerate and pentyl valerate [43]. The latter ester and its respective isomer (peaks 76 and 80) were detected at comparable levels as the most abundant esters at 1.6-1.9% in WG samples, respectively, and 1.2-1.6% in red Egyptian mahlab samples \"RE\", respectively, and are likely derived from valeric acid, also present in seeds. Other esters present at lower levels included pentyl caproate and n-butyl acetate (peaks 82 and 71) at 0.95 and 0.87%, respectively, in WG samples. An increase in the pentyl caproate level in RE was observed upon roasting from 0.41 to 2.25%, which would likely intensify its flavor.  
Several short-chain fatty acids were identified, including butanoic acid, for the first time in _P. mahaleb_, reported before in apricot (_P. armeniaca_), in addition to caprylic and caproic acid, which were previously only reported in _P. mume_[42].
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 10 Open Challenges with Wi-Fi 6
### Wi-Fi 7
#### 10.2.3 Multi-Link (Band) Data Transmission  
A station can transmit or receive one data flow over multiple radio interfaces that can operate on different frequency bands. This is suitable for extremely high data rates and low-latency use cases. However, switching quickly from one band to another, handling multi-link transmissions, and multiplexing packets across different bands are still open challenges.
## 6 Discussion
### Discussion of the Applicability Readiness of the Presented Methodology  
Numerous studies have shown a significant influence of local weld geometry on the fatigue strength or life of welded joints [10; 11; 36; 38; 40]. In welded structures, the weld transition acts as a local stress raiser that can cause fatigue cracks, which often initiate at locations with high stress concentrations [14; 16; 18]. Hence, a representative quantity for the influence of the notch effect at the seam transition is the SCF [19].  
The weld toe radius, the weld angle [44; 45; 46], and the depth of undercuts [19] are primarily decisive for the SCFs at weld transitions. In the past, SCFs were mainly determined by parametric estimation functions that were computed with FE simulations; however, those simulations were limited due to the required computational methods of the time. Nowadays, it is much easier to perform a few thousand FE simulations and to train a neural network on the data, which also provides a higher accuracy. In the current study, a high accuracy of the neural network was ensured by performing a sensitivity analysis and a mesh refinement five times smaller than typical recommendations for notch stress approaches, which yields a deviation from the converged stresses of less than 1%.  
Data-driven methods are increasingly used to predict fatigue of components and parts [28] but also to determine SCFs. In terms of computational efficiency, ANN, once trained, are clearly superior to other computer-based methods such as FE simulations [29]; however, ANN have very limited ability to make predictions outside the training data space [33; 34; 35]. This also applies to DNNs as a subtype of ANNs. Thus, a wide range of meaningful parameter combinations was selected to train the DNN of this study.  
ANN and DNN are now an established method to determine SCFs and might be suitable for automatic inline quality inspections and to assess the relation between fatigue strength and geometrical features. To achieve this goal, a number of steps have to be taken. The research of the last years has primarily focused on two of the aforementioned points: (i) the development of tools to determine weld geometries reliably and fast digitalization of weld surfaces and (ii) methods to transfer weld geometries into indicators that describe the notch effect and fatigue performance of welded joints. Thus, two major points remain to be solved to use the presented methodology in design: (iii) the relation between these quantities and actual fatigue strength has to be established and (iv) the developed tools have to be tested and verified [2].
# Demonstration of Wastewater Recycling in a Slaughterhouse
## 3 Results and Discussion
### Evaluation of the Combined System  
After examining the individual process steps, the overall system is evaluated. Figure 13 shows a Sankey diagram of the entire process. The COD, TN, TS, and electrical conductivity were selected as representative key parameters for the evaluation. Most of the organic impurities are removed in the first two process steps. The inorganic impurities, on the other hand, are separated by reverse osmosis. It is noticeable that the amount of sludge to be disposed of is low. This is due to the fact that the maximum sludge concentration in the aeration tanks had not yet been reached at the end of the monitoring phase, and therefore, no excess sludge had to be removed. Theoretically, the excess sludge production should be 32 kgTS\(\cdot\)d\({}^{-1}\). How the biological system behaves over a longer period of time therefore remains the subject of observation. With the installed system, around 35% of the wastewater produced daily can be treated to drinking-water quality. A third can be used for irrigation, and the rest is still discharged indirectly. This means that fresh water consumption at the site can be reduced by around 35% and wastewater discharge reduced by around 70%. As a result, the original targets for the plant have been achieved.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Unit** & **UF—P permeate** & **RO—P permeate** & **RO—Concentrate** & **Removal [\%]** & **Reuse Limit [26]** \\ \hline K\({}^{+}\) & mg\(\cdot\)L\({}^{-1}\) & 27 & 2 & 50 & 93 & - \\ Ca\({}^{2+}\) & mg\(\cdot\)L\({}^{-1}\) & 105 & \textless{}0.01 & 193 & \textgreater{}99 & - \\ Mg\({}^{2+}\) & mg\(\cdot\)L\({}^{-1}\) & 15 & \textless{}0.01 & 36 & \textgreater{}99 & - \\ Cl\({}^{-}\) & mg\(\cdot\)L\({}^{-1}\) & 97 & 2 & 203 & 98 & 250 \\ NH\({}_{4}\)-N & mg\(\cdot\)L\({}^{-1}\) & 2 & \textless{}0.01 & 4 & \textgreater{}98 & 0.5 \\ NO\({}_{3}\)\({}^{-}\) & mg\(\cdot\)L\({}^{-1}\) & 5 & \textless{}0.01 & 10 & \textgreater{}99 & 50 \\ SO\({}_{4}\)\({}^{2-}\) & mg\(\cdot\)L\({}^{-1}\) & 217 & \textless{}0.01 & 430 & \textgreater{}99 & 250 \\ PO\({}_{4}\)\({}^{3-}\) & mg\(\cdot\)L\({}^{-1}\) & 20 & \textless{}0.01 & 30 & \textgreater{}99 & 3 \\ \hline \hline \end{tabular}  
* no abnormal changes.  
\end{table}
Table 7: _Cont._  
Figure 13: Balance of the full-scale wastewater recycling system based on the results.
# A Kinematic Model of the Shoulder Complex Obtained from a Wearable Detection System
## 4 Discussion  
### Comparison of Collected Data with Result of Klopcar Kinematic Model  
#### 4.1.1 Klopcar Kinematic Model  
[PERSON] used the method of markers (M1-M11) pasted onto the skin of the right shoulder complex, as shown in Figure 10. The angular displacement data corresponding to the joints of the shoulder complex when the upper arm is elevated in the \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\) planes were obtained. The measurements were combined to cover the entire reachable domain of the humerus in the above-defined planes [8]. Through the analysis and integration of the data measured in the experiment, the coupling relationship between the movements of the shoulder girdle and humeral elevation angle was obtained. Therefore, the angle \(\phi_{ed}\) of elevation/depression and the \(\phi_{pr}\) of protraction/retraction were calculated according to Equations (4) and (5) during humeral elevation, respectively.  
\[\phi_{ed}=\left\{\begin{array}{ll}-0.3\phi&\phi<0\\ 0&0\leq\phi\leq 30\\ 0.36\phi-10.8&\phi>30\end{array}\right. \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **SSE** & **R-Square** & **Adjusted** & **RMSE** \\  & & & **R-Square** & \\ \hline \(X^{fl}\) & 0.001677 & 0.9967 & 0.9966 & 0.003786 \\ \(Y^{fl}\) & 0.000360 & 0.9976 & 0.9976 & 0.001763 \\ \(Z^{fl}\) & 0.000199 & 0.9998 & 0.9998 & 0.001309 \\ \(X^{hd}\) & 0.014550 & 0.9855 & 0.9851 & 0.011150\[\phi_{pr}=\left\{\begin{array}{ll}0.35\phi&\phi<0\\ 0&0\leq\phi\leq 70\\ -0.22\phi+15.4&\phi>70\end{array}\right. \tag{5}\]  
The length of the shoulder girdle \(d_{SG}\) can be expressed as in Equation (6):  
\[d_{SG}=(-1.6\times 10^{-5}\phi+3\times 10^{-4}\phi+1)\cdot d_{0} \tag{6}\]  
where \(\phi\) is the elevation angle and \(d_{0}\) is the shoulder girdle from S to G when the arm of the subject is hanging naturally.  
For the Klopcar kinematic model, the origin of the global coordinate system is the crossing of an axis (medial and lateral) through the GH joint center and an axis (anterior and posterior) through the thorax. The calculated reference point is the center of the M3 and M5 markers, which is not clear, leading to difficult estimates. However, the distance from the center of the sternum to point G is three-quarters of the initial distance from that, as presented in Reference [21]. Considering that the coordinate frames are different, we unify the starting point at the GH joint initial coordinate frame. Thus, the Klopcar kinematic model is transformed into Equation (7) based on formulas from Equations (4) to (6).  
\[\left\{\begin{array}{ll}x(\phi)=0.75*(d_{SG}\cdot\cos\phi_{ed}\cdot\cos\phi_ {pr}-1)\\ y(\phi)=0.75*d_{SG}\cdot\cos\phi_{ed}\cdot\sin\phi_{pr}\\ z(\phi)=0.75*d_{SG}\cdot\sin\phi_{ed}\\ d(\phi)=\sqrt{x(\phi)^{2}+y(\phi)^{2}+z(\phi)^{2}}\end{array}\right. \tag{7}\]