esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
## 3 Results
### Comparison with Other Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Model**} & \multicolumn{2}{c}{**Prediction Day = 1**} & \multicolumn{2}{c}{**Prediction Day = 3**} & \multicolumn{2}{c}{**Prediction Day = 7**} \\ \cline{2-7}
**Branches** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline SVR & 26.22 & 20.29 & 35.81 & 25.44 & 44.16 & 29.32 \\ LSTM & 25.63 & 19.89 & 37.01 & 23.38 & 46.74 & 31.21 \\ LSTME & 19.15 & 14.23 & 20.31 & 16.72 & 24.66 & 17.76 \\ ConvLSTM & 16.78 & 13.87 & 18.74 & 14.03 & 19.87 & 15.24 \\ Method of this paper & 10.79 & 9.23 & 12.67 & 10.86 & 13.86 & 12.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction results of different models. Figure 5: Comparison of Model MAE Errors. ConvLSTM begin to diverge from the real data, evident in the predicted values from 100 to 200 h in Figure 8. The projected outcomes and upcoming patterns of the model discussed in this article may align more closely with the real data. Hence, the model presented in this study is capable of more accurately predicting PM2.5 levels over an extended period of time. Figure 8: Forecast outcomes from the three techniques over a 7-day prediction interval with MAE Errors. Comparison of prediction results of different models within 7-day prediction interval. Figure 6: Forecast outcomes from the three techniques over a 1-day prediction interval with MAE Errors. Comparison of prediction results of different models within 1-day prediction interval. Figure 7: Forecast outcomes from the three techniques over a 3-day prediction interval with MAE errors. Comparison of prediction results of different models within 3-day prediction interval.
## 5 Summary and Outlook
### Concluding Discussion  
This investigation underscores the critical role that MNS devices play in advancing vibration isolation technologies. Through facilitating a QZS mechanism, these devices provide a novel solution to the persistent challenge of isolating low-frequency disturbances. The comprehensive analysis presented here, spanning from theoretical models to practical applications, highlights the adaptability and efficiency of MNS devices across various engineering domains. Reflecting on the breadth of research conducted, it is evident that while significant progress has been made, the exploration of MNS devices remains incomplete. The interplay among material science, computational modeling, and design optimization emerges as a pivotal area for future research, promising to further enhance the capabilities of MNS-based vibration isolation systems.
## 4 Results and Discussion
### Driving Stability Analysis on Multi-Factor
#### 4.2.3 Evaluation of Lateral Stability Based on GRG  
The grey relational grade (GRG) of each factor was obtained in Table 5 with Equations (9) and (10). In terms of LA, higher GRG values for vehicle speed, WHR, circle curve radius and friction coefficient are obtained, and all of which exceed 0.6, indicating that the vehicle speed had the most significant effect on LA, followed by the radius of circular curve, friction coefficient, WHR, superelevation, crosswind angle, and crosswind speed, respectively. For SA, the sequence of relational grades, ranked in descending order, is vehicle speed, radius of circular curve, superelevation, WHR, friction coefficient, crosswind speed, and crosswind angle, respectively. Regarding LTR, the degree of influence of the factors, in descending order, are crosswind angle, vehicle speed, radius of circular curve, WHR, wind speed, superelevation, and friction coefficient, respectively. For RA, WHR has the most obvious effect on RA, followed by vehicle speed, friction coefficient, crosswind angle, radius of circular curve, superelevation, and crosswind speed, respectively. It is notable that the CRG of WHR for RA is over 0.8, demonstrating the highest relation grade among the various factors. It implies that the pavement rutting is inclined to lead to roll-over instability.
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 2 System Model
### 5G Baseband Signal Model
The simplified generation method of 5G signal baseband is as follows:  
\[s(t)=\sum_{m=0}^{N_{SC}-1}r(m)e^{j2\pi(m+m_{0})\Delta f(t-T_{CP})}, \tag{4}\]  
where \(s(t)\) is continuous analog baseband signal in time domain, \(N_{SC}\) is the number of subcarriers, \(m_{0}\) is the initial frequency offset, \(\Delta f\) is subcarrier space, and \(T_{CP}\) is the time of cyclic prefix. In practical application, 5G baseband signal is discrete digital signal generated by 5G baseband chip. In order to conform to the actual situation, 5G baseband signal is discretized:  
\[s_{D}(n)=\sum_{k=0}^{N_{SC}-1}r(k)e^{j2\pi(k+k_{0})\Delta f(nT_{C}-T_{CP})}, \tag{5}\]  
where \(s_{D}(n)\) is discrete digital signal in time domain and \(T_{C}\) is sample interval of baseband signal.
## 3 Results and Discussion
### Patterns of Variation in Ultimate Bearing Capacity  
The ultimate bearing capacity and displacement of the two piles at \(C_{ref}/s_{u}\) = 80 were used as the references, and all data were normalized to analyze the variation pattern between the load-displacement curves and failure modes of the two sets of tests. The standardized curves are shown in Figure 9. The curve of HSCM-1-1 showed that \(P_{1-1}=1.0\) and displacement \(S_{1-1}>1.0\) when \(C_{ref}/s_{u}\in\ [40,80]\). \(S_{1-1}\) increased with the reduction in \(C_{ref}/s_{u}\), and the slope remained constant. The above results indicated that the reduction in the cement strength in this interval would not alter the pile failure mode but reduced the modulus of elasticity of the pile foundation, thus decreasing the stiffness. When \(C_{ref}/s_{u}\in\ [20,40)\), \(P_{1-1}\) remained constant, but the slope of \(S_{1-1}\) increased, indicating that the pile and soil interface failure mode was approaching the limit and the pile foundation failure mode was about to be transformed. When \(C_{ref}/s_{u}\in\ [5,20)\), \(P_{1-1}<1.0\) and decreased with the soil-cement strength, and \(S_{1-1}\) also showed a decreased trend, indicating that the HSCM pile failure mode had gradually changed from pile and soil interface failure to steel pipe and soil-cement interface failure.  
From the curves of SDCM-1-2, \(P_{1-2}=1.0\) and displacement \(S_{1-2}>1.0\) when \(C_{ref}/s_{u}\in\ [40,80]\). \(S_{1-2}\) gradually increased with the reduction in \(C_{ref}/s_{u}\), the SDCM pile exhibited pile and soil interface failure in this interval, but the slope of \(S_{1-2}\) was larger compared to \(S_{1-1}\), indicating that the SDCM-1-2 stiffness decreased more rapidly. When \(C_{ref}/s_{u}\in\ [20,40)\), \(P_{1-2}\) began to decrease and \(S_{1-2}\) remained essentially constant. The above results indicated that \(C_{ref}/s_{u}\) caused a gradual shift from the pile and soil interface failure to steel pipe and soil-cement interface failure in this range. 1-1-SDCM showed a higher soil-cement strength when the failure mode shift occurred compared to HSCM-1-1 and was more susceptible to steel pipe and soil-cement interface failure. When \(C_{ref}/s_{u}\in\ [5,20)\), the curve slopes of \(P_{1-2}\) and \(S_{1-2}\) were further increased, and steel pipe and soil-cement interface failure occurred in the 1-1-SDCM.  
The linear fitting function of the normalized curve was established based on the simulation results to express the ultimate bearing capacity variation law of the HSCM-1-1 more intuitively. When \(C_{ref}/s_{u}\in\ [20,80]\), the fit function was \(y=1\). When \(C_{ref}/s_{u}\in[5,20)\), the fit function was \(y=0.036x+0.03\).
### Case Study No. 2: CODESYS Linked with Node-RED and Microsoft Azure
#### 6.2.4 Connection between CODESYS and Local Node-RED  
After creating and running OPC UA server using CODESYS, we want to obtain the data using OPC UA to Node-RED, which runs on localhost (like OPC UA server). This will be provided by OPC UA Client node, where we set the same address as in the previous text (opc.tcp://localhost:4840). This way Node-RED acts as the OPC UA client. We accept all the variables we want to work with (Figure 21). On the left in Figure 21, we see nodes of type _Inject_ querying each variable every second, defining the variable name in the _msg.topic_ of the given message. The variable name is quite complex in the CODESYS OPC UA address space. We can see the names, and also the data, clearly in any OPC UA client, for example. There we can read that the ID of one of the variables is _ns=4S= \(\mid\) var \(\mid\)CODESYS_  
Figure 18: Sequence diagram for second case study.  
Control Win V3 x64.Application.PLC_PRG.FO_I_RetroreflectiveSensor1_ and based on this ID, we can query the variable in Node-RED.  
Figure 19: Symbol configuration.  
Figure 20: OPC UA Client Factory I/O.  
It should be clarified the reason for running Node-RED on localhost. Similar to the previous case study, we want to use the cloud for discrete-event system monitoring and emergency intervention. Since our OPC UA server is running locally and we do not have a public (static) IP address, we will use localhost Node-RED to send data to the cloud and receive data from the cloud.  
We will send our variables to the cloud using MQTT protocol. We will use _MQTT-in_ node (acting as a subscriber) and _MQTT-out_ node (acting as a publisher). These nodes will connect to the broker (server) that is implemented in the cloud. Specifically, this is the Aedes MQTT broker [36]. We will read our variables in the local Node-RED using OPC UA client, from which we extract individual data (variables) using our own _Filter_ functions and send them to the cloud using MQTT. When sending data via MQTT, we need to set the corresponding _MQTT topic_ for it. For example, for us it looks like _inputs/FIO_I_RetroreflectiveSensor1_.
## 3 Results and Discussion
### Antioxidant Capacities of Porcine Organ Hydrolyzates
#### 3.3.4 Reducing Power  
The reducing power of porcine organ hydrolyzates is shown in Figure 6; a high absorbance level indicates excellent reducing power. Unlike the radical scavenging results, the hydrolyzates in each tissue showed little or no significant difference in reducing power compared to the non-hydrolyzed control. Similarly, [PERSON] et al. [8] reported the decreased ferric-reducing ability of porcine liver after autolysis and explained that electron-transferring compounds such as vitamins and endogenous enzymes could be related to the higher reducing power of the control porcine liver. In addition, the reducing power of protein hydrolyzates is strongly associated with molecular weight distribution, in which the redox reaction occurs with multifaceted molecular weight compounds [29]. Previously, [PERSON] et al. [7] found the highest ferric-reducing power at a higher-molecular-weight fraction (30 kDa) than 5 and 10 kDa fractions. Thus, the decreased reducing power in the porcine organ hydrolyzates might be attributable to an excessive level of hydrolysis. Thus, our results imply that extensive and untargeted enzymatic hydrolysis can weaken the reducing power of porcine organ hydrolyzates.  
Figure 4: ABTS radical scavenging activity (**a–e**) and ABTS radical IC\({}_{50}\) value (**f**) of Alcalase- and trypsin-mediated protein hydrolyzates from porcine organs. H, heart; K, kidney; S, spleen; V, liver; L, lung. Each error bar indicates the standard deviation (\(n=3\)). a–g means with the same letter within the result for IC\({}_{50}\) value are not significantly different (\(p\geq 0.05\)).
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 4 Quality Evaluations of Inverse Halftone Images
### Objective Evaluation
To evaluate the performance of restoring image details of different inverse methods, the index of image clarity is employed to address this problem. The clarity is the internal evaluation method of the image, independent of the original image. The higher the clarity, the clearer the details in the image will be. The clarity is defined as follows. \[C=\frac{1}{mm}\sum_{x=1}^{m}\sum_{y=1}^{n}\sqrt{\frac{\left(I(x,y)-I(x+1,y)^{2 }+\left(I(x,y)-I(x,y+1)^{2}\right)^{2}}{2}} \tag{9}\]  
Finally, we evaluated the image quality on the basis of the above indexes in order to compare the different inverse halftoning methods. The experimental images include the butterfly image, the Lena image, and the peppers image, and the halftone images were generated by the error diffusion method. The inverse halftoning method consists of the PRL method [20], the IH-UNet method [17], and the ESRGAN method [25]. Table 1 gives the experimental results. As shown in Table 1, all objective evaluation indexes can show the image quality in corresponding aspects, which are consistent with the visual performance effect, and all have reference significance. However, none of them could individually evaluate image quality comprehensively. From Table 1, we can also see that the performance of the method based on multiple sub-networks (PRL method) is better than the method based on a single network (IH-UNet method), and the performance of the method based on a general purpose DCNN (ESRGAN method) is the worst.
# Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network +
## 4 System Modelling
### Basic System Settings  
Apart from the set of all miners in the D2D network, there is a computational resource user set \(U\) consisting of \(u\) users in our system, where in each task period \(p\), \(u\) users are requesters and the rest \(h=u-p\) are helpers. We divide the user set \(U\) into two member sets, namely requester set \(P\) and helper set \(H\). There are \(p\) requesters in \(P\) and \(h\) helpers in \(H\), where \(p\geq 1\) and \(h\geq 1\). Each requester is denoted as \(p_{i}\in P\), and each helper is denoted as \(h_{j}\in H\), where \(i\in\{1,2,...,p\},j\in\{1,2,...,h\}\). The smart device of a requester \(p_{i}\) or helper \(h_{j}\) is subject to a D2D communication range \(r_{i}^{p}\) or \(r_{j}^{h}\), respectively, above which D2D direct link connection is not possible. \(c_{i}^{p}\) or \(c_{j}^{h}\) is used to denote the available computational power of a requester or helper, indicating how fast or how much computation the smart device is able to handle per second for our cooperative scheme. Typically, this type of computational power is represented by how many clock cycles the device can run per second, 2.6 GHz for example. At any task period \(\Psi\geq 0\), each requester \(p_{i}\) initializes a task of complexity \(T_{i}\Psi\) in clock cycles (indicating how many clock cycles need to be run to get the result of the task) with its maximum wait time \(t_{i,\Psi}\) in seconds (indicating the maximum time \(p_{i}\) will wait for the result until he/she has to do the assigned uncompleted task slices by himself/herself).
# Cellulose Nanofibers and Other Biopolymers for Biomedical Applications. A Review
## 3 Alternative Biopolymers
### Proteins
#### 3.2.2 Collagen and Gelatin  
Collagen is found in the connective tissues of animals and possesses huge potential in tissue engineering application and drug delivery, due to its high biocompatibility and mechanical properties. Collagen molecules form a triple helix structure, held together by inter and intra chain hydrogen bonding (Figure 7).  
There are 20 genetically distinct members of the collagen family. The major ones are type I (skin, tendon and bone), type II (cartilage), and type III (skin and vasculature) [77]. Cross-linked collagen-based materials can be used to produce tissue scaffolds or matrices for wound healing. Properties of collagen-based materials are dependent on the source, age of animal and type of collagen used.  
Gelatin is a soluble protein obtained from the partial hydrolysis of collagen, and its properties are wholly dependent on the collagen source used [60]. Traditionally gelatin has been used in the pharmaceutical industry in capsule dosage forms, however, in more recent times cross-linking has led to developments in tissue scaffolding and transdermal drug delivery by way of microneedles [78].  
Figure 6: Crystallographic structure of HSA (PDB: IN5U), presenting the three structurally similar domains: I (Brown), II (green), and III (grey). Reproduced with permission from [PERSON] et al. [76].
## 2 Modeling of Tethered UAV Dynamics
### Modeling of Tethered Cables
#### 2.2.3 Tension Formula  
Assuming that the tethered cable extends 0 in the \(Y\)-axis direction, the cable satisfies the following geometric relation:  
\[\tan\alpha_{1}=\frac{dz}{dx} \tag{13}\]  
\[dL_{s}=\sqrt{\left(dx\right)^{2}+\left(dz\right)^{2}} \tag{14}\]  
Dividing Equation (12) up and down yields the following:  
\[T_{0}\cos\alpha_{0}\frac{d^{2}z}{dx^{2}}=\varepsilon\frac{dL_{s}}{dx}= \varepsilon\frac{1}{\cos\alpha_{0}} \tag{15}\]  
From Equations (15) and (12), the following is derived:  
\[T_{0}=\frac{T_{0}\cos\alpha_{0}}{\cos\alpha_{0}}=T_{0}\cos\alpha_{0}\frac{ds} {dx}=T_{0}\cos\alpha_{0}\sqrt{1+\left(\dot{z}\right)^{2}} \tag{16}\]  
Let T\({}_{0}cos\alpha_{0}\) = H, then Equation (16) can be transformed into:  
\[T_{0}=H\sqrt{1+\left(\dot{z}\right)^{2}} \tag{17}\]  
Taking the angle between the boundary condition tether cable and the \(X\)-axis to be 0, it can be obtained:  
\[z=\frac{H}{\varepsilon}ch\frac{x\varepsilon}{H}-\frac{H}{\varepsilon} \tag{18}\]  
The tension equation for the tethered cable is then obtained as follows:  
\[T_{s}=T_{0}=H\sqrt{1+\left(\dot{z}\right)^{2}}=H+\varepsilon z \tag{19}\]  
\(H\) and the maximum cable tension \(T\) can be found in (18) and (19).  
When the tethered UAV performs various movements in the air, its force state will constantly change. As the UAV continues to rise, the gravity of the cable itself will increase accordingly. During horizontal movement, the expansion and contraction of the cable will also be affected, further changing the force situation of the UAV. To cope with these complex force variations, we assume that there is a maximum tensile force \(T\), which represents the limit value of the cable when subjected to tensile forces. The tether cable's tension \(T\) on the UAV platform is decomposed in ground coordinates to obtain the components \(T_{x}\), \(T_{y}\), and \(T_{z}\) of \(T\) in the X, Y, and Z axes, respectively, viz:  
\[T=\left[T_{x}\quad T_{y}\quad T_{z}\right]=\left[T\cos\beta\sin\beta_{1}\quad- T\cos\beta\cos\beta_{1}\quad-T\sin\beta\right] \tag{20}\]Further, generalizing the tether cable tension into a column vector of \(R^{6\times 1}\), there is:  
\[T=\big{[}T\cos\beta\sin\beta_{1}\quad-T\cos\beta\cos\beta_{1}\quad-T\sin\beta \quad 0\quad 0\big{]} \tag{21}\]
## 1 Introduction  
Rare metals are essential to modern industry, especially high-tech industries such as micro-electronics, specialty alloys and high-strength low-alloys etc., [1, 2, 3, 4], which are regarded as strategic and critical metals [3, 4, 5, 6]; however, their production has long been limited in a few countries, and thus their resources, types of deposit, origin, genesis, and tectonic settings attract attention worldwide [7, 8, 9, 10]. Globally the Ta resources mainly occur in pegmatites and rare element-enriched peraluminous granites [3, 4, 6]; consequently, pegmatites had long been the foci of metallogenic research.  
The inherent coarse-grained and in most cases zoning features make the pegmatites distinct from common rocks with similar composition, resulting in the precise determination of the bulk composition of the entire pegmatite almost impossible [11]. Even a simple classification schedule is difficult for pegmatites, as summarized by some scholars [11, 12]. Currently, the five-class depth-zone classification [13] and its revision scheme [14], combined with the family classification [8], are the most widely used for garantie pegmatites.  
The prominence of the scheme is to introduce the geochemical subdivision of the rare element pegmatite class into three families: the niobium- yttrium-fluorine (NYF) family, the lithium-cesium-tantalum (LCT) family, and the mixed NYF-LCT family. Furthermore the NYF-LCT subdivision has become independent from the depth zone classification and been used to distinguish pegmatites of the other four classes as well [12].  
In China, pegmatite deposits are one of the most important rare metal metallogenetic types [15; 16; 17], e.g., the famous Koktokay pegmatite in Altai [18; 19] and the recently discovered Renli high-grade super-large Nb-Ta deposit in Hunan Province [20], indicating a conspicuous mineral resource potential. However, the granitic pegmatoid Nb-Ta deposits discovered so far in China are mainly concentrated in West, Central and South China. The Lijiapuzi Nb-Ta deposit, exploited in the 1970s as a muscovite deposit and confirmed to be a Nb-Ta pegmatite deposit in 2018, is the only pegmatite-type rare metal deposit proven by systematic exploration in Northeast China. Considering that both the Zhaojinggou grainitic pegmatite Nb-Ta deposit in Inner Mongolia [21; 22] and the Lijiapuzi pegmatite Nb-Ta deposit locate at the northern margin of North China Craton, this probably indicates that there is a large-scale rare element mineralization in this area. From this point of view, the detailed study on the metallogeny of the Lijiapuzi deposit is of great importance to evaluation of the resource potential of rare metals in Northeast China.
## 3 Results and Discussion
### Instrumental Colour Measurement  
Changes in colour attributes during simulated retail display are shown in Table 10. As seen, the addition of hemp flour did not significantly affect (\(p>0.05\)) the lightness (L*) and yellowish (b*) of the minced meat products on the same storage day. [PERSON] et al. [18] and [PERSON] et al. [19] reported that the addition of hemp-based ingredients resulted in a decrease in lightness, which refers to the brightness or darkness of a product. These same researchers [17; 18] also found that adding hemp products to meatballs and burger patties had a limited impact on yellowness, which can be primarily related to the colour of hemp flour [20]. Regarding redness (a*), highly significant differences were observed on storage days 2 (\(p<0.01\)) and 3 (\(p<0.001\)). The same pattern was observed in colour saturation (Chroma) values, as expected, whereas hue angle values were less affected. The colour differences are related to the greenish-brown colour of hemp flour. As demonstrated by the colour deterioration during simulated retail display, primarily assessed in terms of redness (a*) and colour saturation (Chroma), samples from treatment HF6 exhibited greater deterioration. Lower redness (a*) values were observed for beef burgers containing levels of Moringa (_Moringa oleifera_) seed flour similar to those used in the present study, and the authors attributed this to the dilution of myoglobin [69]. Moringa seed flour and hemp flour have a similar greenish colour. Finally, with regard to the significant changes (\(p<0.05\)) in hue values on both storage days 2 and 3, it was observed that the samples from the HF6 treatment and HF0 treatment had different colour descriptions [70]. Photographs of the minced meat products on display day 1 are shown in Figure 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Treatment**} & \multirow{2}{*}{**Significance**} \\ \cline{2-2} \cline{4-4}  & **HF0** & & **HF4** & **HF6** \\ \hline Active acidity (pH) & 6.05 \({}^{\mathrm{c}}\) \(\pm\) 0.04 & 5.95 \({}^{\mathrm{b}}\)\(\pm\) 0.03 & 5.60 \({}^{\mathrm{a}}\)\(\pm\) 0.03 & *** \\ Water activity (a\({}_{\mathrm{w}}\)) & 0.917 \(\pm\) 0.010 & 0.922 \(\pm\) 0.003 & 0.923 \(\pm\) 0.003 & NS \\ \hline NS = Non-significant; *** = \(p<0.001\); Superscripts a, b, and c differ at \(p<0.05\). & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Active acidity (pH) and water activity (a\({}_{\mathrm{w}}\)) of the minced meat products on retail display day 2 (mean values \(\pm\) SD). Figure 1: Minced meat products on display day 1.