esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 IoMT Healthcare Services and Applications
### Hand Hygiene Monitoring  
This involves tracking the frequency and effectiveness of handwashing with soap and water by healthcare workers and patients. It plays a crucial role in reducing the spread of infection and understanding the efficacy of hand hygiene practices. It is important because inadequate handwashing by healthcare workers or patients can facilitate the transmission of germs and bacteria, leading to the spread of illnesses and infections. Therefore, monitoring and tracking the hand hygiene practices of healthcare workers and patients is crucial. It is essential to provide education and training on proper handwashing techniques and ensure consistent and correct adherence to hand hygiene practices [101]. Many hospitals today use IoT devices to remind patients to sanitize their hands upon entering hospital rooms. These devices also provide recommendations on hand sanitization techniques based on specific risks for individual patients. One limitation of these devices is that they cannot physically wash the hands of the user. Nonetheless, research suggests that these devices significantly reduce hospital infection rates by more than 60 percent.
## 2 Data and Methods
### Study Area and Data  
The Chengbi River Karst Basin, located in Baise City, northwest of Guangxi, which covers an area of 2087 km\({}^{2}\) and contains 1121 km\({}^{2}\) of Karst area [20], was selected for this study. The basin has a subtropical monsoon climate, with a mild climate and abundant precipitation, which accounts for about 87% of the total precipitation during the flood season. The upper part of the basin is a typical karst peak forest area, with many karst features such as volute rivers, water caves, and skylights; the lower part is hilly, with high forest cover and dense vegetation [21]. There is a Chengbi River Reservoir in the basin, which is located 7 km upstream from the outlet of the basin. As the water source of drinking water of Baise city, Chengbi River Reservoir also has many roles such as power generation, flood control, and irrigation of farmland. Therefore, developing a suitable runoff prediction model with high accuracy is essential and significant for water resource allocation and sustainable economic growth in the Chengbi River Karst Basin area. There are 12 telemetric rainfall stations, 2 hydrological stations, and 1 meteorological station in the Chengbi River Karst Basin. Due to historical reasons, only the Pingtang and Bashou stations have collected representative long-duration data. The Pingtang station was selected as a representative station, and the monthly runoff data of the station for a total of 55 years from 1963 to 2017 were used for the study and analysis, which was provided by the water resources management department. Moreover, the runoff depth was obtained by calculating the total runoff volume of the specified section and dividing it by the rainfall catchment area  
Figure 1: Flowchart of the research.  
above the section. The approximate location of the Chengbi River Karst Basin is shown in Figure 2.
## 3 Materials and Methods
### Preliminaries  
**KGs.** A KG is a collection of the triples \(\mathcal{K}=\{(h,r,t)|h,t\in\mathcal{E},r\in\mathcal{R}\}\subseteq\mathcal{E} \times\mathcal{R}\times\mathcal{E}\), where \(\mathcal{E}\) and \(\mathcal{R}\) represent the sets of entities and predicates in the KG, respectively. In a triple \((h,r,t)\), entities \(h\) and \(t\) are referred to as the _head entity_ and _tail entity_, respectively.  
**KG embedding and notations.** Within the context of a KG \(\mathcal{K}=\{(h,r,t)\}\), KG embedding aims to represent each entity \(h,t\in\mathcal{E}\) and relation \(r\in\mathcal{R}\) using continuous vectors, denoted as \(\mathbf{h,t}\), and \(\mathbf{r}\), respectively. These vectors serve as dense representations that capture the meaning and relationships encoded in the KG.  
**KGC tasks.** KGC, or knowledge graph completion, involves inferring missing facts from the known facts within KGs. The objective of a KG reasoning model is to effectively rank positive triplets higher than negative triplets, thereby accurately identifying potential positive triplets that may have been overlooked in the current graph.  
Figure 1: The figure is a visual representation of the predicate decomposition framework. The left (**a**) is an example of the predicate is_CoupleOf. The circles and triangles represent entities, and the vectors between them represent predicates. [PERSON] and [PERSON], [PERSON] and [PERSON], [PERSON] and [PERSON], and [PERSON] and [PERSON] are all couple pairs; however, the relationship between [PERSON] and [PERSON] is distinctly different from those of the other three pairs. This difference is emphasized by the orange lines in the figure. After predicate decomposition, the predicates can be divided into two separate predicates: is_husbandOf and is_wifeOf. This division is clearly demonstrated in the right figure. On the right (**b**) is another example of the predicate Person-Language. Please refer to the introduction for relevant explanations.  
Formally speaking, KGC is often cast as a _tail prediction_ task, where the model aims to prioritize the tail entity \(t\in\mathcal{E}\) of an incomplete triplet \(x=(h,r,?)\) over a set of negative entities. This set of negative entities is defined as \(N_{t}=\{e\in\mathcal{E}:(h,r,e)\
otin\mathcal{K}\}\), where \(\mathcal{K}\) represents the known facts in the KG. The model, denoted as \(F(x)\), computes a score vector \(s\) for each entity \(e\in\{t\}\ \cup N_{t}\). These scores indicate the likelihood of the triplet \((h,r,e)\) being true. Analogously, the _head prediction_ task strives to complete incomplete triplets like \(x=(?,r,t)\).
# Trace Elements and Mineralogy of Upper Permian (Zechstein)
## 6 Results
### Mineral Composition of Potash-Bearing Deposits
Other listed minerals sporadically occurred are as follows: berlinite, bloedite, crondestdite, gypsum, hexahydrite, hexahydroborite, leonite, loewite, nantokite, picromerite, spodumene, starkeyeite, trevorite, boracite, hydrol calcite, goethite, celestite, hematite, carbonates, and clay minerals (Table 4). Only few of the minerals are the primary ones of evaporitic deposits: halite, carnalite, sylvite, and celestine, and others, including anhydrite, boracite, bloedite, hexahydrite, bischofite, kainite, langbeinite, loewite, and manganese could be precipitated from brines as well as transformed from the primary minerals due to diagenetic and metamorphic processes. Typical secondary minerals of the primary evaporitic ones are epsomite, hexahydroborite, kieserite, leonite, picromerite, and starkeyeite [95; 96; 98]. Some detected minerals, such as berlinite, crondestdite, nantokite, trevorite, spodumene, talc, hydrol calcite, pyrite, hematite, and goethite are primary or secondary mineral components of magmatic, metamorphic, and hydrothermal rocks, and they are an accessory in studied evaporites together with clay minerals. Some of most required trace elements are attributed to defined minerals: boron (B) to boracite, hexahydroborite to chlorites and clay minerals, strontium (Sr) to celestine and gypsum or anhydrite (replacing Ca), lithium (Li) to spodumene, clay minerals, and chlorites, nickel (Ni) to trevorite, and iron (Fe) mainly to pyrite, goethite, and hematite (Table 4). Because such minerals are rare or occasional in studied deposits, the content of these elements is very low.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 4 Node Classification
### Estimating Neighbor Counts
#### 4.2.1 Grid Model  
If it is assumed that anchor nodes are uniformly distributed in a grid pattern, then the number of neighbors of a mobile node is estimated as follows. Let \(X_{1},X_{2},\ldots,X_{n}\) be the number of neighbor nodes that a mobile node can have when they are measured with a fixed interval. As a sensor network contains \(N\) anchor nodes, the probability that the radio coverage of a mobile node contains \(k\) anchor nodes can be represented as \(P(X=k)\). Therefore, the probability that a mobile node has \(k\) neighbors is:  
\[P(X=k)=\binom{N}{k}p^{k}(1-p)^{N-k} \tag{1}\]  
where \(N\) is the number of anchor nodes within the monitoring area and \(p\) is the probability that an anchor node falls within the mobile node's radio coverage.  
It is assumed that anchor nodes are uniformly distributed within a rectangular monitoring area. The probability, \(p\), is \(A_{O}/A_{M}\), where \(A_{O}\) represents the overlapping area between the radio coverage of a mobile node and the monitoring area and \(A_{M}\) represents the area of the monitoring region.  
The expected number of neighbor nodes of a mobile node is:  
\[E(X) =\sum_{k=1}^{N}k\times P(X=k) \tag{2}\] \[=Np\times\sum_{k=1}^{N}\left(\frac{(N-1)!}{(N-k)!\times(k-1)!} \right)p^{(k-1)}(1-p)^{(N-k)}\] (3) \[=Np\times 1=Np \tag{4}\]  
If the radio coverage of a mobile node contains the monitoring area, \(E(X)\) is equal to \(N\). If the monitoring area contains the radio coverage or the two areas are overlapping, \(E(X)\) is equal to \(\frac{(N\times\pi\times R^{2})}{A_{M}}\), because \(A_{O}\) is \(\pi\times R^{2}\), where \(R\) indicates the radio range of a mobile node. If the two areas are overlapping, \(E(X)\) is equal to \((N\times A_{O})/A_{M}\).
# Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
## 2 Materials and Methods
### Development of Multiple Regression Models
#### 2.5.4 Verification of Multiple Regression Models  
Several criteria have been used to evaluate the performance of prediction models based on regression analysis [15; 39; 46]. In this study, to evaluate the predictive ability of the developed model, the statistical parameters between the actual axle torque and the axle torque predicted through the model were calculated according to the following Equations (10)-(13). The statistical parameters such as R\({}^{2}\), mean absolute percentage error (MAPE), root mean square error (RMSE), and relative deviation (RD) were used as an evaluative indicator of the development of each model.  
\[R^{2}=\frac{\sum_{i=1}^{i=N}\left(y_{i}-y_{a}\right)-\sum_{i=1}^{i=N}\left(y_ {i}-\widehat{y_{i}}\right)}{\sum_{i=1}^{i=N}(y_{i}-y_{a})}, \tag{10}\]  
\[MAPE=\frac{1}{N}\sum_{i=1}^{i=N}\biggl{[}\frac{1}{y_{i}}(y_{i}-\widehat{y_{i} })\biggr{]}\times 100(\%), \tag{11}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{i=N}(\widehat{y_{i}}-y_{i})^{2}}, \tag{12}\]  
\[RD=\frac{RMSE}{Mean}\times 100, \tag{13}\]  
where \(y_{a}\) is the mean actual axle torque, \(y_{i}\) is the ith actual axle torque, and \(\widehat{y_{i}}\) is the ith predicted axle torque.
## 3 Flow-based optical detection
### ICP-AES, ICP-OES, and ICP-MS
However, the ion-exchange cannot be employed to accumulate chromium in seawater due to the high concentration of seawater matrix. Therefore, the dual columns containing imidodiacetate chelating resin as the on-line preconcentration systems for the speciation of Cr(III) and Cr(IV) in seawater were introduced [71]. Cr(III) was collected on the first column; and the second column was used to collect the chromium in the effluent from the first column after passing through a reduction agent, or not, for the reduction of Cr(VI) to Cr(III). Cr(VI) was calculated as the difference between the concentration of pre-reduced Cr(VI) and Cr(III) in the effluent from the first column. A detection limit of 0.04 and 0.09 \(\upmu\)g L-1 were obtained for Cr(III) and Cr(VI), respectively. Speciation of vanadium was developed using the alkaloid quinine modified resin micro-column separation/preconcentration [72]. The selectivity of vanadium (V) and (VI) depended on the pH value. The adsorbed species was eluted and evaluated by fluorination assisted electrothermal vaporization (FETV)-ICP-OES. PTFE was used as a chemical modifier to eliminate the memory effect and avoid the formation of the refractory carbides for improvement in the analytical performance of the procedure. Isotope dilution coupled with flow injection ICP-MS was developed for the determination of traces of lead [73]. The removal of the matrix by the chelating reaction on the 8-hydroquinoline-5-sulfonic acid (8-HQS) column was employed. This method can be applied for trace lead determinations in the presence of high salt concentrations.
## 4 Experiments Results and Discussion
### Test Model and Sensor Arrangement  
For the comparative experiments, two types of NTC sensors are designed in the present study. One has a radius of 10 mm and a back-filling material of air (NTC-10), and the other miniaturized sensor has a radius of 2.5 mm and a bottom liner filled with epoxy (NTC-2.5). Figure 6 shows that the sensor NTC-10 obtains excellent heat flux results even without corrections. However, the epoxy in the sensor NTC-2.5 leads to heat flux dissipation so that dynamic corrections are necessary. It should be  
Figure 10: Schematic diagram of the experimental facility. (**a**) Detonation-driven shock tunnel; (**b**) Single detonation shock tunnel; (**c**) Detonation-driven shock tube.  
noted that the radius used here represents the dimension of the CVD diamond, and the radius of the entire sensor is 0.2 mm larger, i.e., 10.2 and 2.7 mm for the NTC-10 and NTC-2.5, respectively.  
Two types of typical models were selected in the present study, a flat plate and an R20 sphere. The flat plate was used in the shock tunnel, and the sphere was used in the shock tube. The flat plate, which is a 17\({}^{\circ}\) angle wedge model, is shown in Figure 11. In this design, the flat plate on the wedge model can be replaced. Due to the relatively large size of the NTC-10, two flat plates are designed. In flat plate 1, three NTC-10 sensors (No. C01-C03) are evenly installed at 150, 200, and 250 mm from the apex along the axis. In flat plate 2, three NTC-2.5 sensors (No. C04-C06) are installed at the corresponding positions. On both sides of C05, two NTC-2.5 sensors (No. C07, C08) are installed at a distance of 50 mm. In addition, there are two reasons for the choice of the plate model; one is that the sensor can be installed flush with the model to reduce measurement errors caused by the installation deviation, especially for the larger NTC-10 sensor. The other reason is that the heat flux of the plate model can be obtained theoretically.  
For the R20 mm sphere model, only two NTC-2.5 calorimeters (No. C05-C06) were installed, as shown in Figure 12. The test model was placed 50 mm downstream of the open end of the driven tube to avoid disturbances caused by the interaction between the detached shock wave and the inside wall of the tube to the flow field around the test model.  
Figure 11: Test model and sensor configuration (units in mm). (**a**) Plate 1 with three NTC-10 sensors; (**b**) Plate 2 with five NTC-2.5 sensors; (**c**) the 17\({}^{\circ}\) angle wedge model; (**d**) photo of the NTC-10; (**e**) photo of the NTC-2.5.
# Three-Dimensional Guidance Laws for Spacecraft Propelled by a SWIFT Propulsion System
## 3 Optimal Guidance Laws  
In this section, the optimal control law is analyzed by considering a heliocentric orbit transfer. In particular, the spacecraft trajectory minimizes the total flight time so that the optimal guidance laws give a rapid transfer between two assigned heliocentric orbits. In this context, the spacecraft position along the initial and the final Keplerian orbit is left free in order to obtain a typical orbit-to-orbit (minimum time) interplanetary transfer. The optimization process is solved using an indirect approach [20; 21; 22], in which the optimal control law is obtained with the aid of the classical [PERSON]'s maximum principle [23; 24].  
More precisely, the optimal control law is studied considering a spacecraft dynamics in terms of (1) the Cartesian position and velocity vector components \(\{r_{x},r_{y},r_{z},v_{x},v_{y},v_{z}\}\); (2) the state variables \(\{r,\theta,\gamma,v_{r},v_{\theta},v_{\gamma}\}\) in a heliocentric spherical reference frame; (3) the Walker's modified equinoctial orbital elements \(\{p,f,g,h,k,L\}\)[25]; and (4) the classical elements \(\{a,e,i,\Omega,\omega,\
u\}\)[26; 27]. In the latter two cases, the modified or the classical orbital elements define the characteristics of the spacecraft osculating orbit during the interplanetary transfer between the two Keplerian orbits.
# A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography
## 3 Experiment and Analysis
### Comparison with Radiosonde Data
Figure 9 represents the linear regression results of the two schemes, in which the scatter points of the right graph were closer to the corresponding straight lines. The linear regression coefficient and RMS of each scheme were achieved, and the regression equations were established. Compared with the result of Scheme #1, the starting point of the regression equation was closer to 0, and the slope was improved in Scheme #2. The numerical results show that the RMS and slope were 2.003 g\(\cdot\)m\({}^{-3}\)/0.8823 and 1.462 g\(\cdot\)m\({}^{-3}\)/0.9367 in the two schemes, respectively. The regression analysis further indicates that the tomographic accuracy of Scheme #2 was superior to that of Scheme #1.
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 6 Industry 4.0 Applications
### Motion Planning of Industrial Robots Inside a Digital Twin Created by V-REP Software
#### 6.1.4 Cost Function to Be Optimized  
As the goal of the optimization is to obtain a desired position and orientation while ensuring that no collision happens, total distance between the desired position and the actual position of the robot is as follows:  
\[D_{T}=\sum_{i}{(x_{i}-x_{d})^{2}+(y_{i}-y_{d})^{2}+(z_{i}-z_{d})^{2}} \tag{82}\]  
where \(x_{d}\), \(y_{d}\) and \(z_{d}\) are coordinates of \(P_{D}\) the desired position of the robot. Other than distance to the desired position, total time is used as another term in the cost function.  
\[f\left(x\right)=K_{D}D_{T}+K_{T}T+D_{C} \tag{83}\]  
where \(T\) represents the total time, \(D_{C}\) represents the distance between the actual position of robot and its desired position in every single movement due to collision with obstacles. The parameters \(K_{D}\) and \(K_{T}\) are two gains used for optimization which are selected as equal to 10,000 and 1, respectively.  
For each individual particle, after performing the movements given by the particle, \(P_{D}\) is given as the final position of the robot. Meanwhile if there exist no obstacles between the last position obtained from the proposed optimization algorithm and \(P_{D}\), robot moves easily to the final position. Otherwise, obstacle stops robot from movement. It is further assumed that if the end effector is close to the desired point robot does not need to continue with the movements given by the proposed XOR BGSA and total time is considered to this point. When end effector is within obstacle free range of the desired position, the other movements given by the proposed XOR BGSA are bypassed and the desired position is used as the next step for robot.  
Figure 10: Communication between Matlab and V-REP.
## 4 Experiment and Security Analysis  
We present the experiment results in this section. We implemented our algorithm using C++ and Python programming languages and collected the experiment results in a personal computer equipped with 2.40 GHz CPU and 8 GB memory. Our test datasets include the color version of the tone-mapped color image from the HDR image database  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **Required Secret Keys** & **Extracted Messages** & **Image Restoration** \\ \hline
1 & \(key_{3}\) & \(\mathbf{d_{du}}\) & No Need \\
2 & \(key_{2}\) & \(d_{wnn}\) & \(R_{onect}^{\prime\prime}\) \\
3 & \(key_{1}\) & \(\mathbf{d_{sb}}\) & \(R_{onect}^{\prime\prime}\) \\
4 & \(key_{3}\), \(key_{2}\) & \(\mathbf{d_{du}}\), \(d_{wnn}\) & \(R_{onect}^{\prime\prime}\) \\
5 & \(key_{3}\), \(key_{1}\) & \(\mathbf{d_{du}}\), \(\mathbf{d_{sb}}\) & \(R_{onect}^{\prime\prime}\) \\
6 & \(key_{2}\), \(key_{1}\) & \(\mathbf{d_{wnn}}\), \(\mathbf{d_{sb}}\) & \(R_{onect}^{\prime\prime}\) \\
7 & \(key_{3}\), \(key_{2}\), \(key_{1}\) & \(\mathbf{d_{du}}\), \(d_{wnn}\), \(\mathbf{d_{sb}}\) & \(R_{onect}^{\prime\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The seven levels of message extraction for recipients holding authorized secret keys.  
Figure 5: The visual inspection of the test image “Baboon”.  
Figure 6: The visual inspection of the test image “Lena”.  
Figure 7: The visual inspection of the test image “Pepper”.  
Figures 3 and 4 exhibit two cases of stego images, \"General Sherman\" and \"Clock Building,\" produced by our scheme. The target images are \"Cemetery Tree (1)\" and \"Ahwhanree Great Lounge,\" and \(W^{*}\) = (0.58, 0.60, 0.71) represents the optimal weight vector derived from the Kullback-Leibler divergence (KLD) in the Red, Green, and Blue channels [34]. Each pixel in the stego image, \(I^{\prime}_{onset}\), is produced by an optimal weighted color-transfer and message embedding, which has concealed 3-ary message vector, \(d_{sb}\), and a 7-ary secret message, \(d_{turn}\). Also shown are the dual stego images, \(I^{\prime\prime}_{s1}\) and \(I^{\prime\prime}_{s2}\). Two pixels have concealed a 5-ary secret message vector, \(d_{du}\). The stego images do not show any artifact due to message concealment, even though the embedding rate is as high as 3.682 bits-per-pixel-per-stego image.
## 4 Discussion
### Possible Involvement of MBW Complexes  
Both positive and negative regulating MYBs are implicated in the regulation of the \"late genes\" required for anthocyanin biosynthesis as part of MBW complexes, consisting of bHLH TFs and WD40 proteins [9; 36]. The fact that several MYB, bHLH, and WD40 candidates were identified implies a possible role of MBW complexes in the FPG regulation as well. This is further supported by the identification of corresponding _cis_-elements in the promoters of FPGAs, as well as promoter deletion studies [5; 37]. Three positive regulators of FPGAs MYB12, MYB75, and MYB111 are known co-regulators with the CHS (Figure 2A-D). Especially the anthocyanin positive regulator, MYB75 [19], showed a strong down-regulation in the F and F/U treatments clustering with the CHS (Figure S3A), suggesting the PTI signaling could negatively regulate these TFs to suppress FPG expression.  
The Arabidopsis genome contains 133 MYB TFs [38], 162 bHLH TFs [39], and 108 WD40 repeat containing proteins [40], indicating a great potential for functional redundancy in the composition of MBW complexes. To date, TTG1 is the only WD40 protein implicated in flavonoid biosynthesis [41]. The UV-B response depends on upstream components such as UVR8. UVR8 interaction with COP1 is negatively regulated by RUP1 and RUP2 and the latter three proteins all contain WD40-domains as well [42]. It is noted that COP1 was shown to mediate MYB75 degradation in the dark [43]. We speculate that some of the differentially regulated TF candidates might form previously undescribed MBW complexes and function either positively (consisting e.g., of MYB75-SFCH-AT5G53500) or negatively consisting, e.g., of MYB7/MYB32-bHLH78-AT1G64610. MYB4 and the phylogenetically related MYB7, are negative regulators of flavonoid production [44], while MYB32 is a negative regulator of lignin biosynthetic genes [45]. All were found to be co-regulated with the FRK1 (Figure 2I-L). However, other combinations could be deduced from the CHS-group, for example, an MBW complex containing negative regulators such as MYB29 and the bHLH TF UNE10, or one probably involved in trichome development consisting of MYB23, the bHLH TF MYC1, and the WD40 protein FAS2. Thus, it is reasonable to assume that the \"early\" FPGs are regulated by MBW complexes as suggested by [PERSON] et al. [9] and indicated in Figure 3. Further experiments are needed, e.g., by mutant analysis, to ascertain the role of MBW complexes in crosstalk regulation.