esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Sensing-Oriented Issues and Solutions
### Defect Types and Antenna Topologies
This is also a requirement for easy deployment and less influenced by nearby objects [100]. However, the size reduction causes a poor radiation efficiency(small radiation resistance) and then degrades the communication performance [101]. The sensitivity and communication distance of the system are thus strictly connected to the antenna's parameters and more to the point, to its quality factor or bandwidth [102]. The quality factor of the antenna represents the ratio of the time-averaged stored energy around the antenna to the radiated (and lossy) power [103]. The high-Q antenna offers a better sensitivity for detection, but can be difficult to be installed on the surface of a metallic structure bacause it is sensitive to the air gap [104]. Furthermore, impedance matching and gain enhancement should be of particular concern in the small antenna sensor design [97]. There are several types of antenna that can meet the requirements, among which patch antenna and its variations are good candidates because of their simplicities (can be easily adapted by researchers from other communities) and controllable field distributions [105]. However, the antenna would be customized for the specific defect in order to optimize its sensing and communication performance. The following part will summarize the advancement of antenna sensors designed for the strain, crack, and corrosion monitoring.
# Strans-YOLOX: Fusing Swin Transformer and YOLOX for Automatic Payement Crack Detection
## 3 Methodology
### STrans-YOLOX
#### 3.2.2 Global Attention Guidance Module (GAGM)  
The effective fusion of feature maps in different scales is helpful for improving the detection accuracy of cracks with multi-class and multi-scale features. However, many existing methods do not take into account the semantic and scale inconsistency between feature maps and real cracks, resulting in the insufficient extraction of multi-scale features. In order to solve this issue, and inspired by [29; 30], we propose the GAGM. The corresponding architecture is illustrated in Figure 4. By introducing the GAGM between feature maps of adjacent scales, the advanced semantic information of high-level feature map guides the detailed information of low-level feature maps for effective multi-scale feature fusion. Specifically, the low-level feature map \(f_{l}\) (high-resolution) and high-level feature map \(f_{h}\) (low-resolution) are input into different branches of the GAGM, respectively. First, a \(3\times 3\) convolution is applied to adjust the channel dimension of the \(f_{h}\), and the adjusted feature map \(X_{H}\) can be formulated as:  
\[X_{H}=\sigma(BN(Conv_{3\times 3}(X_{h}))) \tag{5}\]  
Figure 3: The architecture of the Swin Transformer block.  
where, \(\sigma\) refers to the SiLU activation.  
Then, the global context information of the high-level feature map is obtained through the SE attention mechanism, which can be calculated as:  
\[X_{a}=sigmoid(BN(Conv_{1\times 1}(\sigma(BN(Conv^{\prime}_{1\times 1}(G(X_{H}))))))) \tag{6}\]  
where \(G(\cdot)\) refers to the global pooling operation. The kernel size of the \(Conv_{1\times 1}\) is \(C/r\times 1\times 1\) and the kernel size of the \(Conv^{\prime}_{1\times 1}\) is \(C\times 1\times 1\). Moreover, \(r\) denotes the reduction rate required to control the size of the attention mechanism module.  
The obtained attention feature map \(X_{a}\) is multiplied by the low-level feature map \(f_{l}\) for weighting, so the channel attentional feature information is applied to guide the low-level feature map to enhance the class information. Here, \(X_{H}\) is upsampled to the same resolution with the low-level feature map and then added to the weighted \(f_{l}\) so that the class information of the high-level feature map can be fully integrated with the position detailed information of the low-level feature map. The final output \(X_{out}\) is formulated as:  
\[X_{out}=(X_{a}\otimes X_{l})\oplus upsample(X_{H}) \tag{7}\]  
where \(\otimes\) and \(\oplus\) represent the element-wise multiplication and element-wise addition, and \(upsample(\cdot)\) represents 2 times upsampling.  
In a word, instead of directly feeding the FPN with feature maps of different scales for the purpose of feature fusion, the proposed GAGM takes into account the semantic and scale inconsistency of the features. It can effectively guide and propagate information among different levels of feature maps, thereby enhancing the fusion ability of multi-class and multi-scale feature information.
# New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach
## 4 Discussion  
Before discussing the uncertainty and interannual variability issues, we examined the uncertainty related to the selection of the gap-filling method through a comparison with other models. Table 5 presents the results of Experiment 3-6 using SVR and other machine learning techniques, i.e., random forest (RF) and artificial neural network (ANN). The randomForest R package was used to implement the RF [32] ([[https://www.rdocumentation.org/packages/randomForest/versions/4.6-14/topics/randomForest](https://www.rdocumentation.org/packages/randomForest/versions/4.6-14/topics/randomForest)]([https://www.rdocumentation.org/packages/randomForest/versions/4.6-14/topics/randomForest](https://www.rdocumentation.org/packages/randomForest/versions/4.6-14/topics/randomForest))), and the Neural Network Fitting application of MATLAB(r) was used to implement the ANN [33] ([[https://www.mathworks.com/help/deeplearning/gs/fit-data-with-a-neural-network.html](https://www.mathworks.com/help/deeplearning/gs/fit-data-with-a-neural-network.html)]([https://www.mathworks.com/help/deeplearning/gs/fit-data-with-a-neural-network.html](https://www.mathworks.com/help/deeplearning/gs/fit-data-with-a-neural-network.html))). The number of trees and the number of variables tried at each split for the RF were respectively 1000 and 3, while the number of hidden neurons for the ANN was 10. The SVR-based model performed similarly to the RF- and ANN-based models, suggesting that the uncertainty related to the selection of the machine learning technique was relatively small. Thus, we estimated the fluxes using the SVR-based model trained with all available data except for 2007, 2014, and the target year to answer the following two questions: \"How large is the uncertainty related to the long-period flux-gap-filling?\" and \"Can the long-period-gap-filled flux data capture the interannual variability?\"  
Figure 6: Same as Figure 4, but for Experiments 3-1, 3-2, 3-3, 3-4, 3-5, and 3-6. MBE (**a**) and RMSE (**b**) indicate mean bias error and root-mean-square error, respectively. Slope (**c**) and \(r^{2}\) (**d**) obtained from linear regression analysis.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 2 Related Work
### IEEE 802.15.4 Based  
Zigbee, adhering to (IEEE 802.15.4) [7], is efficient in power use and suited for short-range communications, benefiting systems like those in agriculture developed by authors in [8]. Despite its advantages in creating energy-efficient, cost-effective networks, Zigbee's limited range hinders its use in large-scale projects, such as water station automation.  
For industrial applications, Ref. [9] used WirelessHART for temperature control and diagnostics by integrating it with PLCs and SCADA systems, enhancing real-time automation control. Although proven to be suitable for industry, it has the same range limitation as Zigbee and struggles with interoperability due to its exclusive support for HART devices, posing challenges for comprehensive system integration.  
Reference [10] used ISA-100.11a in their design for water level management, through a system comprising sensors, a repeater, a wired actuator, and a controller, all coordinated via ISA-100.11a for operational stability. The system highlights ISA-100.11a's capacity for maintaining network performance under industrial conditions, including real-time link stability adjustments. Despite its strengths, ISA-100.11a shares the range limitations of Zigbee and WirelessHART.  
Addressing the range limitation, ref. [11] presented [PERSON]'s approach, enhancing range and reliability through a redundant wireless network with a mesh topology for broader coverage; the practicality of such an expansion is constrained by the network's hop limit, potentially complicating the network and increasing costs, a significant limitation for the extensive range required by the Sof-Algeen water station.
# Simplified Integrity Checking for an Expressive Class of Denial Constraints
## 5 Denials with Negated Existential Quantifiers
#### Practical Applications  
We now discuss the most complex non-recursive examples that we found in the literature for testing the effectiveness of the proposed simplification procedure. Example 14: This example is taken from [10]. Consider a schema \(S=\langle\text{IDB},\Gamma\rangle\) with three extensional predicates \(a\), \(b\), \(c\), two intensional predicates \(p\), \(q\), a constraint theory \(\Gamma\), and a set of trusted hypotheses \(\Delta\). \[\text{IDB}=\{ \quad p(X,Y)\gets a(X,Z)\wedge b(Z,Y),\] \[q(X,Y)\gets p(X,Z)\wedge c(Z,Y)\quad\}\] \[\Gamma=\{ \quad\gets p(X,X)\wedge\
eg q(1,X)\quad\}\] \[\Delta=\{ \quad\gets a(1,5)\quad\}\]  
This schema \(S\) is not in \(\mathcal{L}_{s}\) and the unfolding of \(S\) is as follows. \[\mathsf{Unfold}_{\mathcal{L}_{n}}(S)=\{\leftarrow a(X,Y)\wedge b(Y,X)\wedge \
eg\exists W,Z(a(1,W)\wedge b(W,Z)\wedge c(Z,X))\}.\]  
We want to verify that the update \(U=\{b(X,Y)\Leftarrow b(X,Y)\wedge X\
eq 5\}\) (the deletion of all \(b\)-tuples in which the first argument is \(5\)) does not affect consistency. \(\mathsf{After}_{\mathcal{L}_{n}}^{U}(S)\) results in the following extended denial:  
\[\leftarrow a(X,Y)\wedge b(Y,X)\wedge Y\
eq 5\wedge\
eg\exists W,Z(a(1,W) \wedge b(W,Z)\wedge W\
eq 5\wedge c(Z,X)).\]  
As previously described, during the optimization process, the last conjunct can be processed as a separate denial \(\phi=\leftarrow a(1,W)\wedge b(W,Z)\wedge W\
eq 5\wedge c(Z,\mathbf{X})\), where \(\mathbf{X}\) is a free variable that can be treated as a parameter (and thus indicated in bold). With a resolution step with \(\Delta\), the literal \(W\
eq 5\) is proved to be redundant and can thus be removed from \(\phi\). The obtained formula is then subsumed by \(\mathsf{Unfold}_{\mathcal{L}_{n}}(S)\) and therefore \(\mathsf{Simp}_{\mathcal{L}_{n}}^{U}(S)=\emptyset\), i.e., the update cannot violate the integrity constraint, which is the same result that was found in [10]. In order to simplify the notation for tuple additions and deletions, we write \(p(\rightarrow\!a)\) as a shorthand for the database update \(p(\overrightarrow{X})\Leftarrow p(\overrightarrow{X})\vee\overrightarrow{X}= \rightarrow\!a\) and \(\
eg p(\rightarrow\!a)\) for \(p(\overrightarrow{X})\Leftarrow p(\overrightarrow{X})\wedge\overrightarrow{X}\
eq \rightarrow\!a\). **Example 15**. : _The following schema \(S\) is the relevant part of an example described in [11] on page 24._  
\[S=\langle\{  married\_to(X,Y) \leftarrow parent(X,Z)\wedge parent(Y,Z)\wedge\] \[man(X)\wedge woman(Y),\] \[ married\_man(X) \leftarrow married\_to(X,Y),\] \[ married\_woman(X) \leftarrow married\_to(Y,X),\] \[q(X,Y)\gets p(X,Z)\wedge c(Z,Y)\quad\}\]0 \[q(X,Y)\gets p(X,Z)\wedge c(Z,Y)\quad\}\]1 \[q(X,Y)\gets p(X,Z)\wedge c(Z,Y)\quad\}\]2 \[q(X,Y)\gets p(X,Z)\wedge c(Z,Y)\quad\}\]3  
_If we reformulate the example using the shorthand notation, the database is updated with \(U=\{\mathit{man}(\mathbf{a})\}\), where \(\mathbf{a}\) is a parameter.
## 3 Proposed System Architecture
### Abnormal and Normal Event Analysis
#### 3.3.1 Moving Average (MA)  
In analyzing the data points statistically, the moving average is first calculated on the series of data. An odd length symmetric moving average (\(MA\)) is computed, which can be utilized at points to smooth time series data in order to estimate the expected trend of abnormal events. We here propose a formula for moving average (\(MA\)) as in Equation (8).  
\[MA(t,F,N)=\frac{1}{2N+1}\sum_{f=t-N}^{f=t+N}F(f),\ \ F(t)=d(t),r(t) \tag{8}\]  
where \(MA(t,F,N)\) represents the average period in \(N\) at time \(t\), \(N\) means the number of time periods, \(F(t)\) represents the computation on two features, namely point distance (\(d\)) and aspect ratio (\(r\)). We here set the optimal value of \(N\) at \(Th\) (\(MA(t,d,Th)\), and \(MA(t,r,Th)\)), to determine the detection rule by analyzing the crossing point. The optimal value of \(Th\) depends on the frame rate, and here we set \(Th=51\). The idea behind making observations from the crossing point of the moving average is demonstrated in Figure 7a,b.  
Figure 6: Concepts for observation of human posture. (**a**) Observation on the features \(x_{VGP}(t)\) and \(y_{VGP}(t)\). (**b**) Observation on the features \(y_{VGP}(t)\) and \(d(t)\). (**c**) Observation on the features \(d(t)\) and \(r(t)\).
## 3 Proposed Method
### Label-Decoding Layer  
The NER task can be viewed as a label prediction task, and these predicted labels have associations with neighboring labels. For instance, when the current predicted label is \"I-Type1\", the preceding label can only be \"I-Type1\" or \"B-Type1\". Within the encoding layer, we solely focus on the contextual information present in the electronic medical record text and do not account for label dependencies. As a result, we introduce a CRF layer after the neural network layer to determine the globally optimal sequence of labels, thus identifying possible entities.  
Assuming that there is an input sequence \(x=\{x_{1},x_{2},\ldots,x_{n}\}\) and the corresponding predicted label sequence is \(y=\{y_{1},y_{2},\ldots,y_{n}\}\), the score of the predicted label sequence y corresponding to the input sequence \(x\) can be calculated by Equation (15):  
\[S(x,y)=\sum_{i=1}^{n}A_{y_{i-1},y_{i}}+\sum_{i=1}^{n}P_{i,y_{i}} \tag{15}\]  
Figure 4: LSTM cell structure.  
where \(A\) is the transfer score matrix; \(A_{y_{i-1},y_{i}}\) is the score of the label \(y_{i-1}\) transfer to the label \(y_{i}\); \(P\) is the character label score matrix obtained from the output of the BiLSTM layer, and \(P_{i,y_{i}}\) is the score of the i-th character predicted as \(y_{i}\) by the BiLSTM layer.  
Then, the probability distribution of the label sequence y is obtained by normalizing \(S(x,y)\) with the softmax function, which can be calculated as Equation (16):  
\[p(y|x)=\frac{e^{s(x,y)}}{\sum_{\widetilde{y}\in Y_{x}}e^{s(x,\widetilde{y})}} \tag{16}\]  
where \(y\) denotes the true label sequence, and \(Y_{x}\) denotes all the predicted label sequences.  
To improve loss calculation, this paper opts for maximum likelihood estimation, aiming to maximize the probability of the actual label sequences, which can be calculated as Equation (17):  
\[log(p(y|x))=S(x,y)-log(\sum_{\widetilde{y}\in Y_{x}}e^{s(x,\widetilde{y})}) \tag{17}\]  
Ultimately, we employ the Viterbi algorithm to identify the label sequence with the highest score, which can be calculated as Equation (18):  
\[y^{\prime}=\underset{\widetilde{y}\in Y_{x}}{argmax}(S\Big{(}x,\widetilde{y} \Big{)}) \tag{18}\]
###### Abstract  
Specific energy prediction is critically important to enhance field performance of agricultural implements. It enables optimal utilization of tractor power, reduced inefficiencies, and identification of comprehensive inputs for designing energy-efficient implements. In this study, A 3-5-1 artificial neural network (ANN) model was developed to estimate specific energy requirement of a vertical axis rotor type intra-row weeding tool. The depth of operation in soil bed, soil cone index, and forward/implement speed ratio (\(u/v\)) were selected as the input variables. Soil bin investigations were conducted using the vertical axis rotor (RVA), interfaced with draft, torque, speed sensors, and data acquisition system to record dynamic forces employed during soil-tool interaction at ranges of different operating parameters. The depth of operation (DO) had the maximum influence on the specific energy requirement of the RVA, followed by the cone index (CI) and the \(u/v\) ratio. The developed ANN model was able to predict the specific energy requirements of RVA at high accuracies as indicated by high R\({}^{2}\) (0.91), low RMSE (0.0197) and low MAE (0.0479). Findings highlight the potential of the ANN as an efficient technique for modeling soil-tool interactions under specific experimental conditions. Such estimations will eventually optimize and enhance the performance efficiency of agricultural implements in the field.  
intra-row weeding; specific energy; artificial neural network; performance efficiency
## 3 Results and Discussion
### Results and Analysis of the Coefficient \(K\) at the Crack Front
According to type I, type II and type III SIFs at the deepest point of the crack, the ratios of \(|K_{\rm I}/K_{\rm II}|\) and \(|K_{\rm I}/K_{\rm III}|\) of different crack sizes can be calculated, and the results are listed in Table 2. For the three types of cracks at the deepest point of the component containing a V-shaped notch, the coefficient \(K\) differs greatly. It is obvious that \(K_{\rm I}\) are much larger than \(K_{\rm II}\) in the same case, and \(K_{\rm III}\) is close to 0. Therefore, type I SIF plays a dominant role, whereas type II and III SIFs can be negligible. It has good agreement with the results of the hole edge cracking of screwed joints studied by [PERSON] et al. [36]. It is known that for the three types of cracks without a V-shaped notch, the difference in the coefficient \(K\) is not large, so \(K_{\rm I}\) can be used as the main criterion for the fracture of components containing a V-shaped notch. Considering the actual engineering significance of the \(K_{\rm I}\), a correction will be made to the crack shape coefficient of \(K_{\rm I}\) in the following discussion. Figure 8: Distribution of type III SIF (\(K_{\rm III}\)) at the front of the semi-elliptical crack on the neck of the ampoule bottle. (**a**) \(a/c=0.2\). (**b**) \(a/c=0.4\). (**c**) \(a/c=0.6\). (**d**) \(a/c=0.8\).
## 6 Continuous Trajectory Refinement
\tag{47}\]  
where \(\delta_{dis}\) is the free distance between MAV's center and the pose of the closest obstacle. Hence, \(J_{obs}(\Gamma)=\Sigma_{i=d}^{n}f_{c}(\mathbf{p}_{i})\) can be estimated based on a given trajectory in the form of control points. Soft constraints are defined by not exceeding both acceleration and velocity within those max limits. \[ \begin{split} J_{\mathbf{v}}(\Gamma)&=\sum_{\mu}\sum _{i=d-1}^{n-d}f_{v}(v_{i,\mu}),\quad J_{a}(\Gamma)=\sum_{\mu}\sum_{i=d-2}^{k_{ 2}-d}f_{a}(\mathbf{a}_{i,\mu})\\ f(\mathbf{v})&=\left\{\begin{matrix}(\mathbf{v}_{\mu}^{2}- \mathbf{v}_{max}^{2})^{2}&\mathbf{v}_{\mu}^{2}>\mathbf{v}_{max}^{2}\\ 0&\mathbf{v}_{\mu}^{2}\leq\mathbf{v}_{max}^{2}\end{matrix},\quad f(\mathbf{ a})=\left\{\begin{matrix}(\mathbf{a}_{\mu}^{2}-\mathbf{a}_{max}^{2})^{2}& \mathbf{a}_{\mu}^{2}>\mathbf{a}_{max}^{2}\\ 0&\mathbf{a}_{\mu}^{2}\leq\mathbf{a}_{max}^{2}.\end{matrix}\right.\end{split}  \tag{48}\]  
To calculate acceleration and velocity at each control point and when both acceleration and velocity exceed their maximum limits, the convex hull property (33) of B-spline is utilized to penalize those control points. Based on the previous method, Ref. [30] proposed an endpoint cost \(J_{endpoint}(\Gamma)\) into the objective function as an additional term. The key intuition behind adding \(J_{endpoint}(\Gamma)\) is to reduce the error between local trajectory and global trajectory since \(J_{endpoint}(\Gamma)\) penalizes error of both velocity and position with respect to the desired global trajectory. \(J_{endpoint}(\Gamma)\) is determined as follows:  
\[J_{endpoint}(\Gamma)=J_{end}(\Gamma)=\xi_{end}^{p}(\mathbf{p}(t_{end})- \mathbf{p}_{end})^{2}+\xi_{end}^{p}(\dot{\mathbf{p}}(t_{end})-\dot{\mathbf{ p}}_{end})^{2}, \tag{49}\]  
where \(\xi_{end}^{p}\) and \(\xi_{end}^{p}\) are regularization parameters, whereas \(\mathbf{p}_{end}\) and \(\dot{\mathbf{p}}_{end}\) are the desired end position and velocity of the trajectory.
# Examination of Intermolecular Forces Influencing Headspace Analysis of Biological Samples
## 2 Materials and Methods
### Methods
#### 2.2.6 Effect of Temperature on Headspace Concentration  
In experiment 4, four volatiles with 8-carbon backbones were chosen to determine the optimal temperature for headspace analysis: 1-octanol, octanal, 2-octanal, and 2,4-octadienal. Test solutions fortified with volatiles (including the internal standard) were prepared in three different media: water, serum, and 1% intralipid. All analyses for extraction optimization were performed in triplicate. The incubation and extraction were carried out for 10 min with a range of temperatures. Each sample was equilibrated at 40, 50, 60, 70, 80, 90, and 100 \({}^{\circ}\)C for 10 min of incubation and 10 min of extraction to establish an appropriate equilibrium temperature in each medium condition. The resulting data, consisting of measured headspace peak responses determined at seven different incubation temperatures graphically appeared to fit a second-order polynomial of the type Y = aX\({}^{2}\) + bX + C. To determine the coefficients (a, b, and C) and estimate critical values (the value of X associated with the highest value of Y), the data were subjected to a quadratic response-surface model (PROC RSREG in SAS). The RSREG procedure uses the method of least squares to fit quadratic functions. For each analyte, the quadratic functions determined for each media type were then compared by analysis of variance (ANOVA) where media type (water, serum, or intralipid solution) was a fixed effect and the parameters of the polynomial function (intercept, coefficient a, and coefficient b) were covariates (PROC GLM). When ANOVA revealed that at least one quadratic function for a volatile differs from the others, linear contrasts were used to investigate which specific media (serum, water, and intralipid) differed from the others.