esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices
## 3 Methods  
### Least Squares Support Vector Regression  
The support vector machines [19; 20] technique was originally designed for classification problems. For dealing with regression problems, support vector regression [21; 22; 23] was proposed and has become a popular alternative for estimating linear or non-linear prediction problems. However, owing to solving quadratic functions in the procedure of support vector regression, the computation burden is quite challenging. Thus, the least squares support vector regression [24] was designed to decrease the computation load by converting a quadratic programming problem into a linear problem. For an input-output dataset \(\{X_{i},Y_{i}\}\), \(i=1\dots N\), the LSSVR model can be illustrated as Equation (1) [24].  
\[\text{Min}:\ f(W,\tau)=\frac{1}{2}W^{T}W+\frac{1}{2}\Omega\sum_{i=1}^{N}\tau_{ i}^{2} \tag{1}\]  
Subject to  
\[y_{i}=W^{T}\cdot\psi(x_{i})+\delta+\tau_{i},i=1,\dots,N\]  
where \(W\) represents the weighted vector, \(\Omega\) denotes the penalty factors controlling the trade-off between the optimization of approximation error and flatness of the approximated function, \(\tau_{i}\) is the \(i\)th error vector, \(\psi(x_{i})\) is the nonlinear mapping function transferring the original input space into a high dimension input space, \(\delta\) indicates the bias parameter. Using the Lagrange function, the Lagrange form of Equation (1) is depicted as Equation (2)  
\[L(W,\delta,\alpha,\ \tau)=f(W,\tau)+\sum_{i=1}^{N}\alpha_{i}\Big{(}y_{i}-W^{T} \cdot\psi(x_{i})-\delta-\tau_{i}\Big{)} \tag{2}\]  
where \(\alpha_{i}\) represent Lagrange multipliers.  
Applying the Karush-Kuhn-Tucker conditions [25; 26; 27] and setting derivatives with respect to four variables, \(W,\delta,\alpha,\ \tau\), and \(\tau\), equal to zero, Equations (3)-(6) can be obtained.  
\[\frac{\partial L(W,\delta,\alpha,\ \tau)}{\partial W}=0,\ W=\sum_{i=1}^{N} \delta_{i}\psi(x_{i}) \tag{3}\]  
\[\frac{\partial L(W,\delta,\alpha,\ \tau)}{\partial\delta}\,\ \sum_{i=1}^{N} \alpha_{i}=0 \tag{4}\]\[\frac{\partial L(W,\delta,\alpha,\ \tau)}{\partial\tau}\,\ \alpha_{i}=\Omega\tau_{i} \tag{5}\]  
\[\frac{\partial L(W,\delta,\alpha,\ \tau)}{\partial\delta}\,\ W^{T}\psi(x_{i})+ \delta+\tau_{i}-y_{i}=0 \tag{6}\]  
Then, by the least squares method, the LSSVR model can be reformed as Equation (7):  
\[y(x)=\sum_{i=1}^{N}\alpha_{i}K(x,x_{i})+\delta \tag{7}\]  
\[K(x,x_{i})\ =\ \psi(x)^{T}\cdot\psi(x_{i})^{T} \tag{8}\]  
where \(K(x,x_{i})\) represents the kernel function satisfying the [PERSON]'s principle [28]. The radial basis function with a variance \(\sigma^{2}\) expressed by Equation (9) is specified here as a kernel function.  
\[K(x,x_{i})=exp(-\frac{\|x-x_{i}\|^{2}}{2\sigma^{2}}) \tag{9}\]
# Thermophysical Property Measurements with the Finite Bar
## 3 Experimental Setup  
The previously described theory can be applied to an experimental setup which consists of a metal bar with length to width ratio (\(l/w\)) larger than ten. This allows the temperature distribution to be almost one-dimensional when the heat source is heating the entire height on one side of the bar. The bar shown in Figure 2 is heated on the left side (\(x=0\)) with a constant power source homogeneously over the entire height. In this setup, an electric resistance heater cartridge is used as a heat source. The geometry of the sample was chosen to satisfy the operating conditions of the heating cartridge. This includes a hollow cylinder to house the heating cartridge. The minimal allowed wall thickness surrounding the heating cartridge defines the height of the bar. The heating cartridge is powered by an external constant power source. Monitoring of current and voltage allows controlling the power in a window with an error lower than 1%.  
The sample used here consists of a bar with a total length \(l\) of 167 mm, a width \(w\) of 40 mm and a thickness of 8 mm. The thickness of 8 mm was chosen to match the diameter of the heating cartridge (\(d=8\) mm). As the heating cartridge requires an absorbing wall thickness of 4 mm, the heated end of the bar was shaped in the form of a cylinder of 16 mm in diameter.  
The bar was instrumented with PT100 (Class A) temperature sensors with 2.3 mm in height in 2 mm in width. The temperature sensors have a response time of 0.2 s. Seven temperature sensors were placed on the bar at a distance of 25 mm. The data acquisition system was a self developed PCB using MAX31865 RTD-to-Digital converters.  
The heating cartridge power was current controlled using an Aim-TTi CPX400 laboratory power supply.  
Temperature recording with a frequency of 1 Hz is typically sufficient to capture all transient effects. The electric heating is triggered a short time after the recording is started. Since the temperature should be uniform at the beginning of the measurement, this allows to verify the calibration of the temperature sensors. This results in no offset error and only temperature dependent. One sample (AL7075) is measured to show the proof of concept.
## 4 Experiments  
### Experimental Setup  
#### 4.1.1 Datasets  
Our experiments are conducted on the JDDC dataset, which is obtained from JD Dialogue Challenge Competition ([[https://github.com/EndlessLethe/jddc2019-3](https://github.com/EndlessLethe/jddc2019-3) rd-retrieve-model]([https://github.com/EndlessLethe/jddc2019-3](https://github.com/EndlessLethe/jddc2019-3) rd-retrieve-model), (accessed on 25 December 2022)). To the best of my knowledge, the JDDC dataset is a large-scale customer service conversation dataset in the field of e-commerce, which is suitable for customer sentiment analysis in customer service conversation scenarios.  
We first process the dataset by cleaning, deleting, and removing noise and irrelevant content. Each utterance is then annotated with one of five sentiment labels: very negative, negative, neutral, positive, and very positive. The specifications in the annotation process are as follows.  
If there are two or more negative emotional messages, the emotional tendency of the utterance is marked as very negative, otherwise, the emotional tendency is marked as negative, as follows:  
Customer: It's been several times, you are too perfunctory. (very negative)  
Customer: Why has not my order shipped yet? (negative)  
If the utterance is of a simple consultation and does not have any emotional color, it is marked as neutral, as follows:  
Customer: Hello, are you there? (neutral)  
If there are two or more positive emotional messages, the emotional tendency of the utterance is marked as very positive, otherwise the emotional tendency is marked as positive, as follows:  
Customer: The quality of the goods is good, I will give you good reviews. (very positive)  
Agent: Hello, thank you for coming, how can I help you? (positive)  
We arranged for three annotators to label each utterance sentiment. If two of them do not agree on their sentiment labeling, an expert makes the final decision. The data annotation results show that the consistency of the two is above 89%, and the consistency of the three is above 83%. The calculation of the Kappa coefficient between the two labeled persons shows that the mean value of the Kappa coefficient is greater than 0.75. Based on the results of the abovementioned indicators, the data annotation of this topic has a good consistency, and the quality of the data annotation meets the experimental standards.  
After labeling the conversation sentiment of the dataset, the dataset is divided into the training set, verification set, and test set according to the ratio of 7:1:2, and the dataset is statistically analyzed. Table 2 shows the distribution of five types of sentiment labels on the dataset. Neutral occurs most frequently of all sentiments, which is in conformity to the characteristics of conversation in the field of e-commerce. In addition, the dataset contains 6305 conversations where the average turns of a conversation are 11.2.
## 2 Background of Mobile _Ad Hoc_ Network Routing
### AODV Routing Protocol  
MANETs employing AODV routing protocol do not store routes to all other nodes. They maintain only routing information that is strictly necessary. Therefore, a participating node that wants to send a message to another node needs to find the route to the destination in the first place. When a node that uses this protocol wants to send a message to a destination node, and it does not know the route to the destination, it must perform the route finding procedure to identify the route to the destination [7]. The source node broadcasts the _route request packet_ (_RREQ_) to its neighbor nodes. On receiving the message, the neighbor nodes further broadcast the message to its neighbors. These chains of broadcasting are continued until _RREQ_ reaches the destination node, or _RREQ_ reaches a node that has enough information to provide the route to the destination node. The left hand side of Figure 1 shows how _RREQ_s move in the network. Unlike MANETs based on DSR use the source routing in which each data packet carries the complete path to the destination, MANETs based on AODV use on-demand routing in which each data packet has only next-hop information. Like DSDV protocol, AODV protocol avoids forming loops by using sequence numbers. Each node has its own sequence number as well as the broadcast identifier. The sequence number is incremented when the neighbor node is updated, and the broadcast identifier is incremented when the node performs the route finding procedure. In addition to these identifiers, the source node includes the sequence number to the destination node in _RREQ_. An intermediate node may have a larger sequence number than the one in _RREQ_. In such case, the intermediate node must know about the destination better than the source node, and therefore the intermediate node halts the _RREQ_ and responds to the source node instead of re-broadcasting _RREQ_. Thus, in AODV, the data packet may have multiple paths to the destination, and can use the latest route to the destination.  
If _RREQ_ reaches the destination node or an intermediate node that knows the route to the destination node, that node then responds to the source node by sending a route reply packet (_RREP_).  
Figure 1: _RREQ_ and _RREP_.  
The right hand side of Figure 1 shows how _RREP_ moves back in the network. In AODV, each mobile node periodically sends a _hello message_ to other nodes to verify their existence. If a node finds that an intermediate node is lost and or that some routes are no longer passable, it broadcasts a _route error message_ (_RERR_) to propagate the failure information.
## 4 Verification of Spatial Geometric Characteristic
### Track and Rotor Non-Coplanar (Skew-Mounted or Deflection Installation)
#### 4.2.1 Balance Positions Based on Spatial Geometry  
First, draw perpendiculars from each point in the track circumference to the spin axis of the system. It can be presented by the drawing to confirm the balance positions of ABB balls and know whether to satisfy the spatial geometry characteristic. When the system is in dynamic balance, Equation (14) shows that the orbit center is located on the \(xy\) plane. In the inference of spatial geometry, the \(x_{i}y_{i}\) plane is used to describe the orbital plane. For the sake of discussion, the \(y_{i}\) axis is chosen as the intersection line between the orbital plane and the \(xy\) plane when the system is dynamically balanced. Let \(\phi_{1}=0\) further simplify Equation (32) as follows:  
\[R_{i}\Big{(}\sin\alpha_{i}\!\cos\alpha_{i}\!\sin^{2}\phi_{2}\Big{)}-x_{c_{i}} \!\sin\alpha_{i}\!\cos\phi_{2}+y_{c_{i}}\!\cos\alpha_{i}=0,i=1\sim n. \tag{37}\]  
1. When the spin axis passes through the track center, due to the ABB installation deflection, the perpendiculars of each point on the circumference to the rotary axis intersect with the track center axis at only four positions \(\alpha_{i}=0\), \(\pi\) and \(\pm\pi/2\), as shown in Figure 7a. This phenomenon aligns with the findings of the examination principle discussed in [9].
2. If the spin axis does not pass through the track center, as long as the position of the track center \((x_{c_{i}},y_{c_{i}})\) and its equilibrium angle \(\phi_{2}\) can be determined, the intersections of the curved surface obtained by drawing perpendiculars to the spin axis at each point on the circumference of the track and the track center axis can be found. For two examples, (1) \(R_{i}=1\), \(x_{c_{i}}=-0.1\), \(y_{c_{i}}=0.06\) and \(\phi_{2}=25^{\circ}\) and (2) \(R_{i}=1\), \(x_{c_{i}}=-0.05\), \(y_{c_{i}}=0.05\) and \(\phi_{2}=30^{\circ}\) are shown in Figures 7b and 7c, respectively, there may be two (\(\alpha_{1}=-12.77^{\circ}\) and \(\alpha_{2}=111.87^{\circ}\)) or four (\(\alpha_{1}=-165.91^{\circ}\), \(\alpha_{2}=102.58^{\circ}\), \(\alpha_{3}=-9.79^{\circ}\) and \(\alpha_{4}=98.28^{\circ}\)) equilibrium positions of the balls.
## 3 Results and Discussions  
### Thermal Strength Restrained Specim Test  
The results were presented in six groups, as shown in Table 9 and Figure 4. The groups of 1, 2 and 3 were designated to the three gradations of SMA-13, SMA-10 and SMA-5 with a fixed asphalt binder modified with 5% SBS + 10% CRM, respectively. The groups of 4, 5 and 6 were designated to SMA-13, SMA-10 and SMA-5 with a fixed asphalt binder modified with 5% SBS + 20% CRM, respectively. Three parallel trials were conducted for each group.  
Generally, the freezing fracture temperature ranged from \(-17\)\({}^{\circ}\)C to \(-33\)\({}^{\circ}\)C, and the freezing fracture strength ranged from 1.5 to 6.5 KN for the mixtures discussed. The effect of NMS and asphalt binders on the results are discussed in the following section.  
(1) The freezing fracture temperatures of groups 1, 2 and 3 decreased from \(-20.4\)\({}^{\circ}\)C to \(-23.8\)\({}^{\circ}\)C and \(-29.2\)\({}^{\circ}\)C with the decreased NMS from 13, 10 to 5 mm, respectively. The freezing fracture temperatures of SMA-10 and SMA-5 decreased by 16.7% and 43.1% as compared with that of SMA-13, respectively. Similarly, the freezing fracture temperatures of groups 4, 5 and 6 became lower with the decreased NMS. Therefore, when the amount of CRM in the mixed modified binder was constant, the decrease in NMS can reduce the freeze-crack temperature of SMA. This may be caused by a higher OAC for a smaller NMS, as shown in Table 8. In addition, with the same gradation, increasing the content of CRM can slightly reduce the freezing fracture temperatures of SMA, because the higher content of CRM increased the viscosity of the mixed modified asphalt binder.  
(2) The freezing fracture strength of groups 1, 2 and 3 increased from 1.6 MPa of SMA-13, 2.47 MPa of SMA-10 to 5.0 MPa of SMA-5. An increasing rate of 54.4% and 212.5% was obtained for SMA-13 and SMA-10, as compared with SMA-5, respectively. This trend was true for groups 4, 5 and 6, which achieved a higher increase rate in the strength for the same decrease in the NMS. In simple terms, the freezing fracture strength was increased by the deceased NMS when the amount of CRM in the mixed modified binder was constant. This may be again caused by a higher OAC for a smaller NMS, as shown in Table 8. Furthermore, addition of a higher CRM content, resulting in a viscous asphalt binder, will result in the higher freezing fracture strength of SMA.  
Figure 4: Freezing fracture temperature and freezing fracture strength of different asphalt mixtures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multirow{2}{*}{**Types of Asphal (5\% SBS)**} & \multicolumn{3}{c}{**Gradation Types**} \\ \cline{3-5}  & & **SMA-5** & **SMA-10** & **SMA-13** \\ \hline Freezing fracture & \(+10\%\)CRM & \(-29.2\) & \(-23.8\) & \(-20.4\) \\ temperature/\({}^{\circ}\)C & \(+20\%\)CRM & \(-31.5\) & \(-25.5\) & \(-21.2\) \\ Freezing fracture & \(+10\%\)CRM & \(5.00\) & \(2.47\) & \(1.60\) \\ strength/MPa & \(+20\%\)CRM & \(6.15\) & \(3.15\) & \(1.79\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Test results of asphalt mixture restraint specimens.
## 4 Technological Challenges and Integration Issues
### Interoperability and Integration Complexity
Reference architectures, such as those being developed under TwinEU, provide blueprints for modular and vendor-neutral integration [3]. From a governance standpoint, regulatory mandates or common requirements, as discussed in Section 3.2, will be necessary to accelerate the adoption of shared standards and to promote convergence across member states [4]. While voluntary alignment is beneficial, it is unlikely to scale quickly enough to support real-time coordination at the pan-European level [24]. In summary, interoperability is not merely a technical detail but a structural precondition for the success of the grid digital twin. Without it, the digital twin risks becoming a patchwork of disjointed sub-models rather than a coherent and actionable representation of the European electricity grid. Achieving seamless integration will require sustained collaboration, investment in modernization, and an iterative process of refining standards, conducting tests, and aligning institutions. It is one of the most demanding yet also one of the most essential dimensions of digital twin development.
## 3 Vehicle Dynamics Modeling and Discretization
### Model Discretization  
The obstacle avoidance objective of the high-speed AGV controller necessitates a long enough prediction horizon to safely anticipate upcoming obstacles. However, if the controller execution time step is used as the prediction time step throughout the full horizon, the total number of time steps required is prohibitively large for real-time implementation. Alternatively, selecting a large time step for the full horizon degrades the controller's performance in predicting near-term vehicle behavior, which is necessary when reacting to unexpected challenges of vehicle stabilization.  
To address this issue, the prediction horizon used in this work is split into two portions at time step \(N_{s}\). The initial portion is comprised of small time steps of size \(t_{s}\), which is equal to the execution time step of the controller, to accurately capture near-term vehicle behavior. The latter portion is comprised of larger time steps of size \(t_{l}\) to extend the horizon to incorporate upcoming obstacles in the long-term. These variable time steps provide a good balance between the prediction accuracy and the computational burden. Besides, \(t_{l}\), \(N_{s}\) and \(N_{p}\) are chosen according to experimental experience.  
Figure 4: Lateral tire force with linear approximation.  
By virtue of matrix exponentials, the vehicle dynamics model (8) is discretized using zero-order hold (ZOH) and first-order hold (FOH) methods on the initial portion and latter portion of the prediction horizon, respectively. The discretized vehicle model can be expressed as:  
\[\xi^{(k+1)} =\mathbf{A}_{s}\xi^{(k)}+\mathbf{B}_{s1}u_{1}^{(k)}+\mathbf{B}_{s2}u_{2}^{(k)}, \forall k=1,\ldots,N_{s} \tag{9}\] \[\xi^{(k+1)} =\mathbf{A}_{l}\xi^{(k)}+\mathbf{B}_{l1}u_{aug}^{(k)}+\mathbf{B}u_{aug}^{(k+1 )}\forall k=N_{s}+1,\ldots,N_{p}-1 \tag{10}\]  
where \(\mathbf{A}_{s}=\mathbf{I}_{6}+\mathbf{A}\times t_{s}\), \(\mathbf{B}_{s1}=\mathbf{B}_{1}\times t_{s}\), \(\mathbf{B}_{s2}=\mathbf{B}_{2}\times t_{s}\) and \(\mathbf{A}_{l}=\mathbf{I}_{6}+\mathbf{A}\times t_{l}+(\mathbf{A}\times t_{l})^{2}/2\), \(\mathbf{B}_{l1}=\Gamma_{1}-\Gamma_{2}\), \(\mathbf{B}_{l2}=\Gamma_{2}\), \(\Gamma_{1}=[B_{1},B_{2}]\times t_{l}+\mathbf{A}\times[B_{1},B_{2}]\times t_{l}^{2 }/2\), \(\Gamma_{2}=[B_{1},B_{2}]\times t_{l}/2\), \(u_{aug}=[u_{1},u_{2}]^{T}\).
## 2 Data and Methods
### Coupled PMLv2 GPP and ET Products  
The PMLv2 GPP and ET products with an eight-day temporal resolution and a 500 m spatial resolution were downloaded from the Google Earth Engine ([[https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PMLV2_v017](https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PMLV2_v017)]([https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PMLV2_v017](https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PMLV2_v017)), accessed on 7 October 2023). The PMLv2 product adopts a coupled diagnostic biophysical model to simultaneously estimate GPP and ET (namely, the PMLv2 model). More specifically, a rectangular hyperbola function (for GPP estimation) and the Penman-Monteith equation (for plant transpiration estimation) are connected by a biophysical canopy conductance model [13]. Meanwhile, the Priestley-Taylor equation incorporating a soil evaporation coefficient controlled by precipitation and equilibrium evaporation is employed for estimating soil evaporation, and a revised Gash model is employed for estimating canopy interception. The leaf area index, albedo, and emissivity from the MODIS products and the daily maximum, minimum, and mean air temperature; atmosphere pressure; wind speed; specific humidity; precipitation; and incoming shortwave and longwave solar radiation from the Global Land Data Assimilation System (GLDAS) meteorological forcing dataset is used to drive the PMLv2 model. Readers can refer to the original algorithm articles for the description of the PMLv2 model [25; 26].
## 4 Case Study
### Capture of Point Cloud Data
Then, center posts were set onto the anchor plate, and service bolts were tightened to connect posts with anchor plates. The ground seal was integrated into the log for each field. Then, logs were inserted with an integrated ground seal between posts. Figure 14: End post welded to the reinforcement. Figure 13: Anchor plate in the job site and point cloud. (**a**) Anchor plate in the job site and (**b**) anchor plate point cloud. When the correct position of the anchor plate has been checked and documented, concrete work is performed. After the concrete is cured, scans are conducted to capture the anchor plate's as-built position, as shown in Figure 15a,b. The data will be compared to the point cloud before pouring concrete to check if the position is changed due to the concrete work.