esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Objective Optimization with Equivalent Static Loads for Parallel Mechanism
### Application in the Lander
The desirability approach was used to combine \(m\) and \(\omega\) into a single desirability function \(D\). The overall objective function \(f_{obj}\) is the negative of \(D\). The parameter values in the desirability approach are listed in Table 2.  
\[f_{obj}=-D \tag{35}\]  
The maximum Von Mises stress \([\sigma_{\mathrm{s}}]\) and the radius of the tubes are used as the constraint conditions. The boundaries of the design parameter are \(\mathbf{b}_{min},\ \mathbf{b}_{max}\). \[ \begin{split}\sigma_{\mathrm{s}}\left(\mathbf{b},\mathbf{d}^{i},\mathbf{z} \right)-[\sigma_{\mathrm{s}}]\leq 0\\ r_{ml}+0.005-r_{mO}\leq 0\\ r_{sl}+0.005-r_{sO}\leq 0\end{split}  \tag{36}\]  
\[ \begin{split}\mathbf{b}_{min}=0.8\times\mathbf{b}_{k}\\ \mathbf{b}_{max}=1.2\times\mathbf{b}_{k}\end{split}  \tag{37}\]  
The optimization process and results are shown in Table 3. The optimization process went through 11 iterations and finally converged. The total optimization time was 5 h (performed on Intel Xeon Dual CPU 2.10 GHz, 64.0 GB RAM). After optimization, the mass of the lander was reduced by 43.7%, and the average fundamental frequency was increased by 22.6%.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Opportunities for Future Research
#### 9.2.1 Advancing Deep Reinforcement Learning for Underwater SLAM  
Deep reinforcement learning has shown great promise in enhancing active localization and visual navigation in robotics. However, its application in underwater SLAM remains challenging due to the unique conditions of underwater environments, such as light absorption, scattering, and fluid dynamics [172]. Future research should focus on the following aspects:  
* **Realistic Simulation Environments:** developing high-fidelity underwater simulation environments that accurately model underwater physics, including light absorption, scattering, and fluid dynamics, to train DRL models effectively;
* **Sample Efficiency:** improving sample efficiency through transfer learning, meta-learning, and sim-to-real approaches to reduce the reliance on large, costly datasets;
* **Hybrid DRL Frameworks:** integrating model-based and model-free reinforcement learning techniques to enhance data efficiency and adaptability;
* **Hierarchical DRL:** leveraging hierarchical DRL approaches to decompose complex tasks into smaller, manageable sub-tasks, improving scalability and robustness;
* **Adaptability to Dynamic Conditions:** ensuring DRL models can adapt to varying underwater conditions, such as changes in turbidity, dynamic obstacles, and environmental disturbances, through robust policy learning and domain adaptation methods.
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 3 PEB Computation and Channel Estimate  
In this section, we elaborate on computation of PEB using CRLB. The CRLB provides a lower bound on the variance of any unbiased estimator of the unknown parameters [27]. In other words, when the CRLB is attained, variance of an estimator should be at least as high as the inverse of Fisher information. For the calculation of PEB, \(Q_{k}\lor k\), we implement a two step approach: First, we obtain FIM for the channel parameters by aggregating the information obtained through scanning all analog receive beams \(\mathbf{w}_{b}\in\mathcal{W}_{b}\) of each coordinating BS \(b\in\mathcal{B}\), during the training phase \(T_{t}\). Then, we perform bijective transformation to compute these bounds in the position domain and obtain PEB. Finally, we provide channel estimate using the PEB.
## 3 Stability Analysis
### Numerical Simulation Verification  
To verify the time-delay stability analysis method with three degrees of freedom derived in this paper, eight points are randomly selected in Figure 4 for verification, where, \({\rm P_{1}}({\rm g}^{*}=-1.4,{\rm\tau}^{*}=0.74)\), \({\rm P_{2}}({\rm g}^{*}=-0.85,{\rm\tau}^{*}=1.988)\), \({\rm P_{3}}({\rm g}^{*}=-0.86,{\rm\tau}^{*}=4.181)\), \({\rm P_{4}}({\rm g}^{*}=-0.9,{\rm\tau}^{*}=5.903)\), \({\rm P_{5}}({\rm g}^{*}=0.91,{\rm\tau}^{*}=0.71)\), \({\rm P_{6}}({\rm g}^{*}=1,{\rm\tau}^{*}=1.25)\), \({\rm P_{7}}({\rm g}^{*}=0.85,{\rm\tau}^{*}=4.62)\), and \({\rm P_{8}}({\rm g}^{*}=0.85,{\rm\tau}^{*}=6.73)\). Applying a small external displacement excitation \(\mathbf{X}=[0,0,0.01,0,0,0]^{T}\) to the system, the body displacement response is shown in Figure 5. From the verification results, we can obtain that the boundary of the critical time delay is accurate.  
Figure 4: Time-delay dependent stability: Stable region; Unstable region.  
Figure 5: System stability verification: (a) \(\mathrm{P}_{1}(\mathfrak{g}^{*}=-1.4,\tau^{*}=0.74)\); (b) \(\mathrm{P}_{2}(\mathfrak{g}^{*}=-0.85,\tau^{*}=1.988)\); (c) \(\mathrm{P}_{3}(\mathfrak{g}^{*}=-0.86,\tau^{*}=4.181)\); (d) \(\mathrm{P}_{4}(\mathfrak{g}^{*}=-0.9,\tau^{*}=5.903)\); (e) \(\mathrm{P}_{5}(\mathfrak{g}^{*}=-0.91,\tau^{*}=0.71)\); (f) \(\mathrm{P}_{6}(\mathfrak{g}^{*}=1,\tau^{*}=1.25)\); (g) \(\mathrm{P}_{7}(\mathfrak{g}^{*}=0.85,\tau^{*}=4.62)\); (h) \(\mathrm{P}_{8}(\mathfrak{g}^{*}=0.85,\tau^{*}=6.73)\).
## 3 Quantitative Analyses of Disruptive Effects of Large Frequency Errors
### Effects on the C/N\({}_{0}\) Estimation
Again, \(N\) was set to 50, that is, the power ratio was averaged over a time interval of 1 s. The estimated \(C\)/\(N_{0}\) and the true \(C\)/\(N_{0}\) were substituted into Equation (21) to obtain the \(C\)/\(N_{0}\) attenuation. Figure 2 shows the theoretical \(C\)/\(N_{0}\) attenuation and the 1000 Monte Carlo simulation results. The two sets of results are relatively consistent. For the case of \(N_{2}=1\), the attenuation of the estimated \(C\)/\(N_{0}\) caused by large frequency errors is less than 1 dB, and the effect of frequency errors is negligible. However, when \(N_{2}\) is greater than 5 and frequency errors are greater than 20 Hz, the effect of frequency errors on the estimated \(C\)/\(N_{0}\) is very significant. When \(N_{2}=50\) and \(f_{e}>30\) Hz, the \(\mathrm{sinc}^{2}(\cdot)\) term in the numerator of Equation (16) quickly decays to near zero. The estimated \(C\)/\(N_{0}\) and \(C\)/\(N_{0}\) attenuation also drop sharply. Therefore, there is a jump in the \(C\)/\(N_{0}\) attenuation curve of \(N_{2}=50\) at \(f_{e}=40\) Hz. In addition, we limited the minimum value of \(C\)/\(N_{0}\) to 0 dB-Hz, that is, the maximum attenuation of \(C\)/\(N_{0}\) is -40 dB, as seen in Figure 2.
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
## 3 MRC Detection  
When the DFT is applied to the vector \(\mathbf{y}^{k}\) in (10) at the receiver, we obtain  
\[\mathbf{u}^{k}=\mathbf{G}^{k}\beta^{k}+\mathbf{z}^{k}, \tag{12}\]  
where \(\beta^{k}=\mathbf{F}\beta\), due to the inclusion of VC, the vector \(\beta^{k}\) will have \(\
u\) replicas of the \(N_{D}\) data transmitted on the active carriers. Taking advantage of the matrix structure of (12), it is possible to obtain a more convenient equivalent form in terms of matrix operators as  
Figure 1: OFDM transmitter with VC allocation.  
Figure 2: Example of VC assignment in the 802.11 p frame.  
\[\mathbf{u}=\boldsymbol{\Phi}\boldsymbol{\delta}+\mathbf{w} \tag{13}\]  
where:  
\[\mathbf{u} = \big{[}u[0]\quad u[1]\quad\cdots\quad u[N-1]\big{]}^{T}, \tag{14}\] \[\mathbf{w} = \big{[}w[0]\quad w[1]\quad\cdots\quad w[N-1]\big{]}^{T}, \tag{15}\]  
the matrix \(\boldsymbol{\Phi}\) of size \(N\times N\) is defined as  
\[\boldsymbol{\Phi}=\begin{bmatrix}\boldsymbol{\phi}_{(0,0)}&\boldsymbol{\phi}_ {(0,1)}&\cdots&\boldsymbol{\phi}_{(0,v-1)}\\ \boldsymbol{\phi}_{(1,0)}&\ddots&&\boldsymbol{\phi}_{(1,v-1)}\\ \vdots&&\ddots&\vdots\\ \boldsymbol{\phi}_{(v-1,0)}&\boldsymbol{\phi}_{(v-1,1)}&\cdots&\boldsymbol{ \phi}_{(v-1,v-1)}\end{bmatrix}, \tag{16}\]  
where each of the sub-matrices \(\boldsymbol{\phi}_{i,j}\) contains the coefficients of the channel matrix \(\mathbf{G}\) according to the following assignment:  
\[\boldsymbol{\phi}_{i,j}=\begin{bmatrix}g_{[0+i*N_{D},0+j*N_{D}]}&\cdots&g_{[0 +i*N_{D},N_{D}+j*N_{D}]}\\ g_{[1+i*N_{D},0+j*N_{D}]}&\cdots&g_{[1+i*N_{D},N_{D}+j*N_{D}]}\\ \vdots&\ddots&\vdots\\ g_{[N_{D}+i*N_{D},0+j*N_{D}]}&\cdots&g_{[N_{D}+i*N_{D},N_{D}+j*N_{D}]}\end{bmatrix}, \tag{17}\]  
where \(i,j=\{0,1,\cdots,\
u-1\}\), \(\boldsymbol{\delta}\) is a vector of data in the FD with \(\
u\) replicas ordered sequentially as  
\[\boldsymbol{\delta}=\Big{[}\boldsymbol{\delta}_{0}^{T}\quad\boldsymbol{\delta }_{1}^{T}\quad\cdots\quad\boldsymbol{\delta}_{\
u-1}^{T}\Big{]}^{T}, \tag{18}\]  
and \(\boldsymbol{\delta}_{q}\) is a vector with structure  
\[\boldsymbol{\delta}_{q}=\Big{[}\boldsymbol{\delta}_{q}^{0}\quad\boldsymbol{ \delta}_{q}^{1}\quad\cdots\quad\boldsymbol{\delta}_{q}^{N_{D}-1}\Big{]}^{T}. \tag{19}\]  
After processing the matrix \(\boldsymbol{\Phi}\) in (13), the replicas of the transmitted data produced by the VC are used for the data detector through the MRC scheme [27] to obtain the estimated symbol vector \(\boldsymbol{\hat{s}}\). The suboptimal detection process can be defined as  
\[\boldsymbol{\hat{s}}=\frac{\sum\limits_{i=0}^{v-1}\sum\limits_{j=0}^{v-1} \boldsymbol{\phi}_{i,j}^{*}\boldsymbol{\delta}_{j}}{\sum\limits_{i=0}^{v-1} \sum\limits_{j=0}^{v-1}|\boldsymbol{\phi}_{i,j}|^{2}+\sigma_{\oplus}^{2}}. \tag{20}\]  
As will be seen later, the computational complexity savings of this estimation compared to any state-of-the-art solutions are highly significant. This is due to the fact that symbol-wise MRC equalization has lower complexity in terms of the operations required in linear/nonlinear block equalizers.
# Effects of Epigallocatechin-3-O-Gallate on Bone Health
## 2 Functional Effects of EGCG
Additionally, EGCG inhibits osteoclastogenesis through multiple pathways, notably by downregulating key osteoclastogenic transcription factors, such as c-Fos and the nuclear factor of activated T cells 1 (NFATc1), and by suppressing RANKL-induced JNK and NF-\(\kappa\)B signalling, thereby mitigating bone resorption. Moreover, EGCG treatment decreases osteoclast formation by suppressing key osteoclastogenic factors, downregulating c-Fos and NFATc1 expression, and inhibiting JNK and NF-\(\kappa\)B signalling in preosteoclasts, thus directly reducing osteoclastogenesis [32; 33; 34; 38]. These processes that impact bone homeostasis are also dependent on dosage, particularly during the early stages of osteoclastogenesis [35; 38]. Osteoblast migration plays a key role both in the physiological bone metabolism and in pathological bone processes. Research performed on cloned osteoblast-like MC3T3-E1 cells derived from a newborn mouse by [PERSON] et al. [39] showed that the compound inhibited insulin-like growth factor 1 (IGF-I)-induced osteoblast migration by attenuating IGF-I-induced p44/p42 MAP kinase phosphorylation. The effects described above were researched using various cell lines, whose characteristics and relevance to physiological processes are outlined below. Human mesenchymal stem cells (hBM-MSCs) are the most relevant model in the context of human physiology, as they are directly derived from humans and reflect the properties of human stem cells [26; 31; 32; 33; 39; 40; 41]. The MC3T3-E1 line, although widely used in the in vitro studies of osteoblasts, originates from mice and does not fully replicate the processes occurring in human bone tissue [27; 32; 34; 42; 43]. Similarly, the RAW264.7 line, a murine model of macrophages and osteoclasts, is valuable for studying bone remodelling; however, inter-species differences limit the direct translation of findings to human physiology [23; 43; 44; 45; 46; 47]. In summary, hBM-MSCs appear to provide the most physiologically relevant model for human studies. The MC3T3-E1 and RAW264.7 cell lines remain useful for fundamental and mechanistic research, but their applicability to directly model human physiological processes is limited.
## 7 Speaker Tilt Angle Variation  
In the previous section all experiments were done with the phone's speaker facing upwards. This orientation should be optimal for maximizing signal reception if microphones were placed just below the ceiling in each corner of the room. Because ultrasound is very directional, it is important to minimize the angle between the direction the speaker is facing and the line-of-sight to all four microphones. With the speaker pointing upwards this angle will never exceed 90 degrees, which is important for this implementation as reception at four microphones is the minimum required.  
Unfortunately the speaker on the front of the phone (used as the earpiece) is not very powerful. The multimedia speaker used for ringtones and other sounds that are supposed to be heard at distance is  
Figure 3: The position of 10 test locations or check points (centerpoint of 1 m diameter circles) and error vector direction and distance from true position to MPV for Experiments 1, 2, and 3.  
normally placed on the back of the phone. However, when the user interacts with the touchscreen, the speaker will normally be facing downwards. Therefore the aim of the next experiment is to test how well the positioning system works with the phone speaker inclined at various angles other than pointing straight up.  
Unless the speaker is pointing directly upwards or downwards, it becomes important what yaw orientation the mobile phone has in addition to its tilt (pitch). Another factor is the introduction of the user holding the phone. He may or may not be blocking the direct line-of-sight to one of the four microphones. Because this test setup introduces so many new variables, some of them had to be taken out of the equation. Yaw orientation for each location was chosen in such a way that the speaker is facing the farthest microphone. In addition, the user tries not to block direct line-of-sight to each of the microphones if possible. The experiment is designed to measure what effects phone tilt (pitch) variation alone can have on positioning accuracy and failure rate. The variables omitted in this experiment are addressed in the next section.
# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 3 Background on Deep Learning Network Architecture  
Deep learning's superiority in performance against conventional machine learning was first demonstrated by [PERSON] and [PERSON] [30]. Deep learning can be described as a model that represents non-linear processing consisting of multiple layers of artificial neural networks (ANN). The method of learning used can be supervised or unsupervised with feature representation in the form of high-level abstraction [31]. [PERSON] [32] described a Deep Neural Network (DNN) as having more than one hidden layer with thousands of units of neurons making small and simple calculations. The increased structure complexity means DL can process large scale and complex data with more learning capacity to characterize input and targeting data. In modern agriculture where the use of a wireless sensor network (WSN) [33] is prevalent, the huge amount of data produced will make the application of DL well suited, and usually leads to better performance.  
The conventional machine learning method often requires specifically defined preprocessing to obtain features as input variables. In plant science, this involves extracting the plant features manually such as colour (e.g., RGB colour ratio) [34], texture (e.g., Grey-level concurrence matrix (GLCM)) [35], spectral reflectance (e.g., vegetation indices (VI)) [36] and thermal radiation (e.g., crop water stress index (CWSI)) [37] for analytical processing. Processing the information is a labour-intensive and time-consuming endeavour which depends highly on expert knowledge. Feature selection is also required to eliminate low-quality variables and reduce the dimension of the input. [PERSON] and [PERSON] [35] developed several algorithms dedicated to the feature selection process for water stress detection in Sunagoke Moss. Deep learning can reduce the need for hand-engineered feature extraction and selection, which allows for automatic transformation for a faster analysis, providing end-to-end data processing.  
DL is now easily usable by many communities, including in plant science. There are many supportive materials available on deep learning applications. The availability of friendly DL libraries together with easy to learn tutorials ensuring convenience of use [38] with popular programming languages [39] have propelled the wide adoption of the method. Platforms such as Google Colab with GPUs notebook have made online software development and teaching much simpler [40, 41]. There are also many tools provided by the DL community that could be useful in the study of plant stress phenotyping [42]. In addition, much research has been conducted to understand more about the DL model and why its performance is exceptional [43]. The problem of limited data sources has also slowly been resolved as many research groups have shared their datasets with others [44, 45].
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 1 Introduction
The experimental results showed that the system integrates the response coordination relationship between various components within the overall structure in the finite element model and the real change law of the structure in the monitoring data, and is capable of carrying out the health status assessments of bridge structures throughout their entire life cycle. The study carried out in [11] proposed a probabilistic fatigue assessment method based on the simulation of random traffic flow and on-site measured strain influence lines, and verified the effectiveness of the proposed method through a case study of a large-span suspension bridge. The study carried out in [12] proposed a truck queue load model and a mixed traffic flow simulation method to study the load effect level of different truck queues and the impact of mixed traffic flow formed with ordinary vehicles on bridge structures, and evaluated the applicability of current regulations. The study carried out in [13] proposed a method for evaluating the dynamic influence factor (IM) from the dynamic response of bridges considering local surface damage in traffic flow. The method was validated by applying it to two existing bridges with different road surface damage and traffic conditions. In summary, the above-mentioned research is still focused on the evaluation of bridges themselves and traffic operation status, or the impact of traffic flow on bridge structures, lacking an assessment of the impact of changes in bridge status on traffic operation status. However, a timely and accurate understanding of bridge information, effective evaluation of the impact of changes in bridge status on traffic conditions, and the timely adoption of effective measures play an important role in ensuring the safe and reliable operation of traffic and improving the operational efficiency of the highway network. Therefore, this article starts with the impact of bridges on traffic operation, and uses an algorithm model that combines Analytic Hierarchy Process (AHP), Delphi method, and Singular Value Decomposition (SVD) to establish a model for evaluating the impact of bridge traffic operation status, which is applied in practical engineering projects. This model can timely and accurately understand bridge information, effectively evaluate the impact of changes in bridge status on traffic conditions, and play an important role in ensuringthe safe and reliable operation of traffic and improving the operational efficiency of the highway network.
# Study of the Critical Speed of Ultra-High-Speed Diamond Drilling Technology in Marble
## 4 Experimental Verification  
To verify the test effect of the ultra-high-speed diamond drilling technology, a drilling experimental device was used, as shown in Figure 20. The rock used in the tests was marble. The drilling parameters were designed as follows: the range of the rotational speed was approximately 3 m/s-10 m/s and the WOB was 12N. We conducted drilling experiments with impregnated diamond bits at various speeds. The results indicate that, as the speed increases, a critical speed becomes apparent, and there is also an upper speed limit, as shown in Figure 21. During the experiment, the test machine adjusts rotational speed and drilling pressure, with an external oscilloscope recording real-time torque changes and drilling time. After the experiment, we measured groove dimensions on the rock's surface, vital for calculating ROP and MSE, examining the appearance of rock cuttings through the use of the EM-30 Plus scanning electron microscope.  
Figure 19: Rock-breaking energy and friction energy distribution.  
Figure 18: Ratio of rock-crushing energy and friction energy consumption.  
Figure 21 presents the data obtained at a drilling pressure of 12N. Compared with the simulation results, MSE and TOB exhibit a generally consistent variation with speed, and a critical speed range is evident. This further confirms the accuracy of the simulation. There is a speed limit in the ultra-high-speed diamond drilling condition.  
SEM images capturing the morphology of cuttings were obtained at drilling pressures of 12N and rotational speeds of 4 m/s and 8 m/s, separately. Figure 22 illustrates the particle size distribution of cuttings at these two rotational speeds. Across all speeds, cuttings of varying sizes are generated, with smaller-sized cuttings predominantly taking the form of powder. However, at the higher rotational speed of 8 m/s, the size of the cuttings notably increases, and there is a higher proportion of larger-sized cuttings. In the images, cuttings exhibit diverse morphologies beyond the powdery form, including flake-like or chunk-like shapes. This suggests that an increase in rotational speed at the same drilling pressure can significantly modify the rock fragmentation mechanism, leading to a greater degree of brittle fracture.  
Figure 21: The curve of MSE and TOB with speed variation under a WOB of 12N.  
Figure 20: Experimental devices. (**a**) ultra-high-speed diamond drilling test bench; (**b**) structure diagram; (**c**) oscilloscope.
# A Design for SDN-Based Identifier-Locator Separation Architecture on IoT Networks
## 3 Proposed SDN-Based Identifier-Locator Separation Architecture for IoT Networks
### Main Procedure
#### 3.4.1 Registration and Packet Forwarding Process
The OFS of the CN's AR already has the flow table entry for the destination EID via the registration process, the OFS of the CN's AR performs the action in the flow table that replaces the \(EID_{CN}\) in the destination address field to \(LOC_{CN}\). After the field replacement, the OFS sends the packet to the output port of the IP domain for normal IP routing. The packet arrives at the OFS of the CN's s_AR via the IP domain, and the field replacement occurs again from \(LOC_{CN}\) to \(EID_{CN}\) at the OFS of the CN's h_AR. After that, the OFS of the CN's AR forwards the packet to its destination. The routes for the packet from the MN to the CN are established after the first packet arrives, and subsequent packets follow the configured routes. Figure 2 illustrates the registration and packet forwarding process.