esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Theories
### Equations of Motion  
To calculate the dynamic response of floating platform in the time domain, the platform can be considered as a rigid body. The equations of motion [44] is expressed as follows:  
\[(M+A)\ddot{X}(t)+C\dot{X}(t)+KX(t)=\overset{\rightarrow}{F}_{WAP}+\overset{ \rightarrow}{F}_{Wind}+\overset{\rightarrow}{F}_{Line}, \tag{15}\]  
where \(M\) is the mass matrix; \(A\) is the added mass matrix; \(C\) is the damping coefficient matrix; \(K\) is the hydrostatic restoring force matrix; \(\overset{\rightarrow}{F}_{Wind}\) is the wind loads applied to the blades and tower; and \(\overset{\rightarrow}{F}_{Line}\) is the mooring tension. In the in-house code, the mooring tension \(\overset{\rightarrow}{F}_{Line}\), the wind loads \(\overset{\rightarrow}{F}_{Wind}\) and the total wave loads can be calculated using the given parameters as well as the initial motions of the platform. By substituting the force at this moment into the equation of motion, the motion state of the platform can be obtained. At the same time, the force at the next moment can be calculated by the obtained motions of the platform. Similar to the above, the motions of the platform can be obtained by continuously iterating like this. In addition, the rotor dynamics include the aerodynamic loads acting on the blades, the gravity loads, the inertial loads, and the counter-dynamic forces from the shaft. The nacelle dynamics have the forces from the impeller, the cladding load, the aerodynamic loads and the gravity loads. The tower dynamics includes aerodynamic loads, the forces from tower and nacelle, as well as the gravity loads. Simple schematics of each module in the in-house codes are shown in Figure 1.
# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
## 2 Materials and Methods
### Experimental Methodology  
Corn kernel samples were scanned using CT for each sample at incubation times of 0 d, 5 d, 10 d, and 15 d. The sliced images were processed and a 3D model was reconstructed to develop a corn kernel mold detection model based on a deep learning algorithm, which also classified the degree of corn mold.
# Chemical Images on Fingerprints Revealed with Mass Spectrometry  
[PERSON]  
1 Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, [PERSON] 2, 20156 Milan, Italy; [EMAIL_ADDRESS]  
[PERSON]  
2 Dipartimento di Chimica, Universita degli Studi di Milano, Via Golgi 19, 20133 Milan, Italy; [EMAIL_ADDRESS]  
[PERSON]  
2 Dipartimento di Chimica, Universita degli Studi di Milano, Via Golgi 19, 20133 Milan, Italy; [EMAIL_ADDRESS]  
[PERSON]  
4S  
Despite its high sensitivity and wide dynamic range, MALDI-MSI is not completely suitable for small molecule detection (\(m/z<\) 1000 Da), because of the strong background noise generated by the ion source decay of the matrix [10]. Consequently, other ionization techniques [11; 12] have been used to assist MSI-applications; however, the lateral resolutions they achieve are not comparable to the superior optical resolution observed with laser ion sources.  
An alternative approach to the MSI of small molecules is to use UV-absorbent inorganic nanomaterials instead of the conventional MALDI matrices [13; 14; 15; 16; 17; 18; 19]. The limiting aspects of this approach are the ad hoc synthesis required to produce nanomaterials, and their unknown shelf life and stability. As reported in the literature [20; 21], metallic nanoparticles can undergo clusterization under laser irradiation. These side processes result in several interfering signals in the MS spectra. Commercially available oxide-based nanomaterials, with well-established physico-chemical properties and shelf lives, will overcome these issues [22].  
Aside from biological applications of MALDI-MSI [23; 24], this technique is innovative in many other research contexts. In the forensic field, for example, it has been used to detect latent fingermarks (LFMs) [25; 26; 27]. However, the analytical separation of overlapping LFMs and the detection of trace compounds embedded in human skin are still areas of debate [20; 28].  
We developed a new method based on MALDI-MSI, starting with commercially available TiO\({}_{2}\) nanoparticles (Evonik P25). The technique was used to identify the chemical distribution of small molecules embedded in LFMs. The detection of endogenous compounds (e.g., lipids) was used to distinguish overlapping fingerprints from different donors. Exogenous compounds were also detected, proving direct and secondary interactions between skin and potentially illicit substances.
## 6 Test Results
### Mass Market Receiver  
The performance of the MM RUT is summarized in Table 4. Whenever an interfering signal is present, as its power increases, the receiver performance degrades until, at some point, the position fix is lost. The receiver is then capable to re-compute a position solution only when the interference power decreases. An example of such interference impact on the mass-market RUT performance is given in Figures 4 and 5, which show, respectively, the average C/N\({}_{0}\) of tracked satellites and the East-North-Up deviations of the position solution for the test case MM02-STATIC-SENSITIVITY (threat signature 02: multiple narrowband interfering signals). In both figures, the corresponding two-peak ramp interference power profile could also be seen (in the right-hand Y-axis) for the sensitivity test methodology, as described in Section 3.3.  
The inaccurate position solution, especially in the vertical component computed by the RUT at the beginning of the test, is due to the cold start and the resulting unavailability of ionospheric parameters and to the convergence of the navigation filter.  
As it can be seen from Table 4, the Jamming-to-Signal ratio at which the position fix is lost for at least 5 s (J/S\({}_{\text{PVT\_lost}}\)) and the Jamming-to-Signal ratio at which the position fix is reobtained for at least 5 s (J/S\({}_{\text{PVT\_reobtained}}\)) are in the range 45-50 dB and 30-45 dB, respectively. This translates into a position fix availability in the presence of the five interference threats between \(\sim\)60% and \(\sim\)70%. The triangular swept frequency interference signature (i.e., test case MM03) seems to be the most impactful, both in terms of availability and maximum position error.  
The TTRP tests showed that the mass-market RUT is capable of recovering from a strong interference event almost immediately. The TTRP value is, in fact, 1 s for four of the tested interference signatures, and 4 s in the case of a wide swept frequency signal with a fast repeat rate (i.e., test case MM01).
## 3 System Model
### Task Model  
For any MD, the tasks generated in different time slots are identified by \(\mathcal{T}=\{1,2,\ldots,T\}\). The arrival time and data size of the tasks are used from real-world collected IoT data. For each arriving task, it is first stored in the corresponding MD task cache queue, and then the decision model gives where the task will be offloaded to be executed. For \(t\in\mathcal{T}\) time slot, the new task generated by the terminal device \(m\in\mathcal{M}\) is denoted as \(\lambda_{m}(t)=\left(D_{m}^{t},\rho_{m}^{t},\tau_{m,\max}^{t}\right)\). \(D_{m}^{t}\) denotes the size of the task data size, \(\rho_{m}^{t}\) denotes the computational resources required per bit for this type of task, and \(\tau_{m,\max}^{t}\) denotes the maximum tolerated delay of the task, i.e., the new task generated in time slot \(t\) will be thrown when the task has not been completed in time slot \(t+\tau_{m,\max}^{t}-1\).
# Data-Driven Redundant Transform Based on Parseval Frames
## 6 Conclusions  
In this paper, we propose a Parseval frame-based data-driven overcomplete transform (DRTPF) to capture features of images. We also propose the corresponding formulations, as well as algorithms for calculating the sparse coefficients and DRTPF model learning. We have proposed a general frame learning method without imposing any structure on the frame. By applying frames to redundant transforms, we combine the ideas of analysis and synthesis sparse models and let them share almost identical sparse coefficients. We conducted robustness analysis, sparsification of nature image and image denoising experiments, which demonstrated that DRTPF can outperform state-of-the-art models, as it exploits the underlying sparsity of natural signals by the integration of frames and sparse models.  
In future work, we shall consider more efficient optimization algorithms for DRTPF, which facilitate the representation ability and application of the proposed method.  
[PERSON] derived the theory, analyzed the data, performed the performance experiments, and wrote the original draft; [PERSON] and [PERSON] researched the relevant theory, participated in discussions of the work, and revised the manuscript; [PERSON] supervised the project. All authors have read and agreed to the published version of the manuscript.  
This work was supported by NSFC (No.61672066,61976011,U1811463, U19B2039, 61632006, 61906008, 61906009), Beijing municipal science and technology commission (No.Z171100004417023) and Common Program of Beijing Municipal Commission of Education (KM202010005018).  
This work was supported by Beijing Advanced Innovation Center for Future Internet Technology, Beijing Key Laboratory of Multimedia, and Intelligent Software Technology. We deeply appreciate the organizations mentioned above.  
The authors declare no conflict of interest.  
Figure 7: Visual comparison of reconstruction results by different methods on ’Man’ and ’Boat’. From left to right: original, T.KSVD [3], K-SVD [14], DFT [33], and DRTPF.
## 3 Problem Description and Modeling
### Allocation Risk Model  
The allocation risk of hazardous materials can usually be measured in combination with the probability and consequence of hazardous accidents [26], which is given as follows:  
\[f_{tij}=P_{ij}\times C_{tij},\ t=1,2,\ldots,T,\ i=1,2,\ldots,n,\ j=1,2,\ldots,m \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Parameter Significance** \\ \hline \(a_{i}\) & The \(i\)th reserve point \\ \(d_{j}\) & The \(j\)th consumption point \\ \(y_{j}^{k}\) & Initial reserve quantity of hazardous materials of type \(k\) at \(a_{i}\) \\ \(e_{t_{i}}^{k}\) & Remaining reserve quantity of hazardous materials of type \(k\) at \(a_{i}\) after period \(t\) \\ \(c_{tj}{}^{k}\) & Demand for hazardous materials of type \(k\) at \(d_{j}\) in period \(t\) \\ \(l_{ij}\) & Generalized distance between \(a_{i}\) and \(d_{j}\) \\ \(x_{tij}^{k}\) & Decision variables, quantity of hazardous materials of type \(k\) allocated from \(a_{i}\) to \(d_{j}\) in period \(t\) \\ \(r_{ij}\) & Population density on the section from \(a_{i}\) to \(d_{j}\) \\ \(p_{ij}\) & Vehicle traffic accidents on the section from \(a_{i}\) to \(d_{j}\) \\ \(s_{k}\) & Accident impact range of hazardous materials of type \(k\) \\ \(\theta_{R}\) & Risk threshold \\ \(f\) & Cumulative allocation risk \\ \(g\) & Cumulative quantity of reserve points performing the allocation task \\ \(\alpha^{u},\alpha^{l}\) & degree of confidence \\ \hline \end{tabular}
\end{table}
Table 1: Model parameter setting.  
where, \(f_{tij}\) is the allocation risk from \(a_{i}\) to \(d_{j}\) in period \(t\), considering the transportation risk. \(P_{ij}\) is the transportation accident probability on the road section from \(a_{i}\) to \(d_{j}\), and \(C_{tij}\) is the accident consequence on the road section from \(a_{i}\) to \(d_{j}\) in period \(t\).  
Referring to the risk modeling approach proposed by [PERSON] [27] for transporting hazardous materials, the allocation risk described above can be defined as:  
\[f_{tij}=pj_{ij}\times l_{ij}\times pd_{ij}\times\sum\limits_{k=1}^{K}{(s_{k} \times{x_{tij}}^{k})} \tag{2}\]  
where \(P_{ij}=pj_{ij}\times l_{ij}\) is used to measure the transportation accident probability on the road section from \(a_{i}\) to \(d_{j}\), which is measured by the probability of traffic accidents \(pj_{ij}\) and the length \(l_{ij}\) on this road section, and \(C_{tij}=pd_{ij}\times\sum\limits_{k=1}^{K}{(s_{k}\times{x_{tij}}^{k})}\) is used to measure the accident consequences of this allocation. The hazard range and surrounding population density are used to measure the severity of accidents when hazardous accidents occur. However, the traffic accident probability, population density, and accident impact range that determine the allocation risk are difficult to accurately measure. How to quantitatively describe these uncertain factors determines the allocation risk value at each period.
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 3 Methodology
### Particle Swarm Optimization (PSO)  
PSO was presented in 1995 by [PERSON] et al., belongs to the global optimization technique derived from the population [55], and has been applied in many fields, such as the evaluation of TBM tunneling rate [56], the evaluation and prediction of slope stability [57], and the prediction of ground vibration caused by blasting [58]. Its inspiration comes from the information-sharing behavior of social animals, such as birds. The PSO algorithm finds the best solution to the problem by sharing the information of the individuals in the swarm [54; 59].  
Suppose there are \(n\) particles in the space, the dimension of which is \(D\), and they form a swarm. Each particle has two properties, position and speed, and the former stands for a possible solution to the problem. In the \(t\)th iteration, the position and the velocity of the \(i\)th particle are represented by \(\mathbf{x}_{i}(t)=[x_{i1},x_{i2},\ldots,x_{iD}]\) and \(\mathbf{v}_{i}=[v_{i1},v_{i2},\ldots,v_{iD}]\), respectively. Whether a particle has reached a good position can be judged by calculating the fitness value of its current position. At every iteration of PSO, the particle will adjust its speed and position based on these formulas:  
\[v_{ij}\big{(}t+1\big{)}= wv_{ij}\big{(}t\big{)}+c_{1}r_{j1}[p_{ij}(t)-x_{ij}(t)]+c_{2}r_{ 2}[g_{ij}(t)-x_{ij}(t)] \tag{7}\] \[\mathbf{x}_{i}(t+1)= \mathbf{x}_{i}(t)+\mathbf{v}_{i}(t+1) \tag{8}\]  
where \(j\in[1,2,\ldots,D]\), \(w\) denotes the inertia weight, \(p_{ij}(t)\) denotes the personal best position of the \(i\)th particle at iteration \(t\), and \(g_{ij}(t)\) denotes the global best position of the swarm. \(c_{1}\) and \(c_{2}\) are acceleration constants. \(r_{j1}\) and \(r_{j2}\) are random numbers between [0, 1]. The position and velocity of particles are limited to [X\({}_{min}\), X\({}_{max}\)] and [V\({}_{min}\), V\({}_{max}\)] to prevent the blind search for particles.  
In the research of [PERSON] and [PERSON] [54; 60], they found that the goal of balancing global search and local search is achieved by the inertia weight \(w\). If the value of \(w\) is larger, it can help particles to avoid falling into the local minima. If the value of \(w\) is smaller, it can promote the algorithm to converge. So, an adaptive inertia weight is introduced which means that \(w\) will decrease linearly based on the following formula as the iteration goes on.  
\[w(t)=w_{max}-t\times\frac{(w_{max}-w_{min})}{t_{max}} \tag{9}\]  
where \(w_{max}\) and \(w_{min}\) are the initial inertia weight and the final inertia weight, respectively, \(t\) is the current iteration number, and \(t_{max}\) is the maximum of iterations.
## 5 Results
### Round-Trip Time (RTT)
#### 5.1.2 Residential Area  
In this environment, interfering signals are also present in the 2.4 GHz band, but to a lesser extent than in the laboratory environment. As can be seen from results in Figure 6, WiFi6\(\_\)2.420 technology demonstrates higher robustness than WiFi4\(\_\)2.420 technology, as it shows less variability in RTT results obtained. While WiFi6\(\_\)2.420 technology presents an \(RTT_{max}<25\) ms and \(mdev\approx 1\) ms, WiFi4\(\_\)2.420 technology offers an \(RTT_{max}<30\) ms and \(mdev\approx 1.5\) ms, demonstrating that WiFi6\(\_\)2.420 technology is more stable in a partially occupied spectrum. Although Wi-Fi 6 exhibits a slight performance improvement in the 2.4 GHz band, the results demonstrate that the performance of Wi-Fi 7 closely aligns with that of Wi-Fi 4. This observation suggests that Wi-Fi 7 is functioning in a legacy compatibility mode rather than utilising its inherent technological advancements, thereby constraining its anticipated performance enhancements.  
It is important to highlight that the remaining evaluated technologies, operating in the 5 GHz band, exhibit significantly lower latency, with \(RTT_{max}<5\) ms and \(mdev<0.5\) ms. This improved performance is attributed to the lower spectrum congestion in the residential environment. Additionally, it is clearly observed that the advancements in Wi-Fi 6 enhance stability even at higher bandwidths. However, this is only achievable in an environment with reduced interference, as increasing the channel bandwidth inherently raises the probability of inter-symbol interference and signal degradation due to multipath effects and spectral overlap. Furthermore, the results once again demonstrate that Wi-Fi 7 lags,  
Figure 5: Round-trip time (max, min, and mean in red) in laboratory environment for each of the Wi-Fi technologies.  
delivering performance comparable to that of Wi-Fi 5, confirming that it is operating in compatibility mode due to the lack of maturity in its drivers.
## 3 Results and Discussion
### Oil Absorption  
We investigated the oil absorption capacity of porous membranes with different diameters for different kinds of oil. These fiber membranes were soaked in vacuum pump oil, peanut oil, and diesel oil, respectively, and compared with the changes in the weight of fiber membrane before and after the absorption. Then, the largest amount of absorption was calculated. As shown in Figure 8, the capacities of absorbed vacuum pump oil for the samples of F1 to F5 were 23.44, 25.44, 32.67, 36.82, and 42.38 g/g, and those of peanut oil are 21.43, 21.47, 21.63, 25.14, and 28.17 g/g, respectively. Finally, we also put the same samples in diesel oil for absorption. As a result, these samples presented low absorption capacities, i.e., 5.30, 5.73, 5.68, 6.28, and 6.74 g/g, respectively. This is because the diameters of the fibers decreased with the decreasing needle size, leading to increased evaporation the of solvent, leaving an increasing specific surface area and also an increasing pore volume; thus, the oil absorption capability on the surface of the fibers increased.  
Figure 8: Maximum oil absorption capacity of porous membranes with different diameters for different kinds of oil.  
Figure 7: The ATR-FTIR spectra of samples prepared by 14 G, 16 G, 18 G, 19 G, 21 G needles and the raw PLA.  
In Table 1, it is also shown that there were different viscosities for different kinds of oil. Based on the results shown in Figure 8, it seems that the absorption capability is inversely proportional to the viscosity. This is probably because the oil is more likely to adhere to the surface and voids of the fibers to inhibit the oil entering into the interior of the fibers; thus, the oil with lower viscosity has a higher absorption capability [25; 26; 27; 28].  
As one can see, the oil absorption capacity of the fiber membrane increased with the declining diameter of the needle. Therefore, we finally explored whether the fiber membrane could effectively achieve oil absorption from the oil-water suspension. As shown in Figure 9a, in order to absorb the oil, the oil-soluble dye was used to make the oil have a green color. Then, 0.5 g of the fiber membranes obtained from a 21 G needle were placed in the oil-water suspension, and the absorption capacity was tested and calculated using Equation (1). Figure 9b shows an image of the fiber membrane after absorption for 30 s. In addition, the fiber membrane was also able to absorb the vacuum pump oil. As shown in Figure 9c, after 1 min, it was found that there was no significant amount of residual oil [29]. The absorption experiment was further pursued in the pure oil system, showing that the fiber membrane absorbed 10 g of oil and 0.92 g of water, respectively, which means that the oil was successfully absorbed from the oil-water suspension.  
Finally, the approach illustrated above can be scaled up in terms of using rows of syringes to continuously produce large quantities of porous fibers to be applied to clean oil polluted ocean.
## 3 Results and Discussion  
### Coagulation Behaviors under Different Rapid Mixing Speed  
#### 3.1.1 Floc Size Distribution in Three Processes  
Continuous experiments were performed under 250 mg/L magnesium ion with 30 L/h to investigate the effects of rapid mixing conditions on coagulation performance and floc size distribution. According to FSD, average floc size decreased when rapid mixing speed increased in the rapid mixer and sedimentation tank. In the flocculation basin, floc size tended to be stable or slightly broken with the increase of rapid mixing speed to 300 rpm. This is consistent with the findings that repulsive forces tend to stabilize the suspension and prevent particle agglomeration [13; 18]. As shown in Table 3, the average floc sizes 8.39, 8.06 and 8.04 \(\upmu\)m were obtained with rapid mixing speeds of 250, 300 and 350 rpm in a rapid mixer, respectively. Small floc had the same trend to aggregate relatively large flocs. Average floc size was 8.06 and 7.89 \(\upmu\)m in the rapid mixer and flocculation basin when the mixing speed was 300 rpm. In general, the flocs will grow in the flocculation basin, but it seems that floc size decreased in the flocculation process. In fact, during the flocculation process, particles larger than 11.25 \(\upmu\)m accounted for 4.54% and 4.9% in the rapid mixer and flocculation basin, respectively. Particles smaller than 1 \(\upmu\)m also decreased in the slow mixing period. As can be seen also in Figure 2 (sedimentation tank), particles smaller than 1 \(\upmu\)m account for 6.2%, 10.0% and 12.6% with mixing speeds of 250, 300 and 350 rpm respectively. It was observed that the percentage of smaller particles increased with the increase of mixing speed. This indicates that the high mixing speed will break the flocs, and only part of the flocs will aggregate again after the flocs are broken. The general shape of the curves was broadly similar in different units. The average floc size decreased to a steady state; there was a dynamic balance between floc growth and breakage. When the stirring speed was increased from 250 rpm to 350 rpm, small average floc size could be observed in these three units. Following the breakage and aggregation period, there was only a partial flocs re-growth, showing that the broken flocs can only re-grow to a very limited extent.  
\begin{table}
\begin{tabular}{c c c c c c} \hline Flux & \multicolumn{2}{c}{**Rapid Mixer**} & \multicolumn{2}{c}{**Floculation Basin**} & \multicolumn{2}{c}{**Sedimentation Tank**} \\ \hline  & speed & time & speed & time & time \\
30 L/h & 250 rpm & & & & \\
300 rpm & 2 min & 80 rpm & 24 min & 60 min \\  & 350 rpm & & & & \\
60 L/h & 300 rpm & 1 min & 80 rpm & 12 min & 30 min \\ \hline \end{tabular}
\end{table}
Table 2: Operational conditions of coagulation process.