esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Energy Consumption Modeling for HVAC Systems in Electric Vehicles
## 2 Materials and Methods  
In order to investigate the dynamic behavior of the thermal conditions inside the vehicle cabin, a numerical model of the HVAC system in combination with the vehicle cabin was created within the Simulink environment. In particular, the developed model calculates the temperature inside the vehicle cabin (\(T_{cab}\)) by the energy balance of external loads (solar radiation, ventilation) and metabolic load [14] with the HVAC system, considering the thermal capacity of the cabin (as shown in Figure 1):  
\[(m_{a}c_{p,a}\,\xi)\frac{dT_{cab}}{dt}=\dot{Q}_{rad}(t)+\dot{Q}_{in}(t)+\dot{ Q}_{tra}(t)+\dot{Q}_{met}(t)-\dot{Q}_{tvac}(t) \tag{1}\]  
where \(\dot{m}_{a}\) is the mass flow rate of the air blown out from the evaporator, \(c_{p,a}\) is the specific heat of air, \(\xi\) is a dimensionless parameter that takes into account the thermal inertia of the vehicle, \(\dot{Q}_{rad}(t)\) is the thermal load due to the solar radiation, \(\dot{Q}_{in}(t)\) is the thermal load due to air infiltration in the cabin, \(\dot{Q}_{tra}(t)\) is the transmission load due to the temperature difference between the vehicle and the outer environment, \(\dot{Q}_{met}(t)\) is the thermal load due to the presence of people inside the vehicle and \(\dot{Q}_{tvac}(t)\) is the cooling power given by the HVAC system. These contributions are variable with time and depend on the boundary conditions, which vary with numerous factors, such as the external weather conditions, the velocity of the vehicle, the number of occupants, etc.  
By integrating Equation (1) over a time interval \(\Delta t\), it is possible to calculate the evolution in time of the vehicle cabin temperature as follows:  
\[T_{cab}(t)=\frac{1}{m_{a}c_{p,a}\,\xi}\int_{0}^{\Delta t}(\dot{Q}_{rad}(t)+ \dot{Q}_{in}(t)+\dot{Q}_{tra}(t)+\dot{Q}_{met}(t)-\dot{Q}_{tvac}(t))dt \tag{2}\]  
To calculate the temperature evolution of the vehicle cabin, two interconnected Simulink block models were developed, the HVAC model and the vehicle cabin model,  
Figure 1: Thermal loads acting on the vehicle cabin.  
which interact with each other. The main blocks of the HVAC system and the mathematical models chosen to simulate the single devices are detailed in the following sections.
# Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO
## 1 Introduction
The communication modes of V2X are distinguished as infrastructure-to-vehicle (I2V), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-grid (V2G), and grid-to-vehicle (G2V) [9]. To effectively address the issue of electric charge management at charging stations, communication between vehicles is crucial. In our research, we integrated ITS-G5 technology to collect real-time data to monitor the movement of electric vehicles. We proposed an allocation technique to assign each EV to the ideal station based on the traffic circumstances collected by the SUMO traffic simulator. This provided flexibility in managing the charging of the system by reducing the energy consumption and charging time required. These are the important contributions of the study:  
* First, we describe the architecture of an enhanced smart grid system integrating ITS-G5 technology, which provides useful real-time information and efficient communication between roadside units (RSUs) and EVs for the purpose of distributing EV charging management decisions. * Second, an optimized approach allowed for optimal allocation of EVs by taking into account the characteristics of the EVs, charging stations, and road traffic disseminated by the RSUs and collected in our study by the SUMO simulator. * Third, a linear mathematical program incorporated all of the constraints. This approach assigned each electric vehicle to an appropriate charging station and consumed a minimum of electric energy when it reached the designated charging station, thus maximizing the final state of charge and reducing the waiting time of EVs at the charging station. * Finally, we performed extensive simulations to verify the effectiveness of this approach. Our evaluation was performed using the realistic traffic simulator. The proposed strategy was found to be more effective than the shorter path strategy in managing the charging of electric vehicles. The structure of this paper is as follows: In Section 2, we provide an overview of the relevant related literature and propose a description of the system and linear mathematical model of the global context of the system and the constraints. In Section 3, we propose an assignment algorithm. Then, in Section 4, we detail the data collection procedure with SUMO and analyze the obtained results. The Section 6 includes a conclusion and recommendations for further research.
## 3 Results and Discussion
### Microhardness Measurements  
Microhardness measurements were performed on both natural (as sintered) faces of the samples and the resulting face after cutting with a diamond wire. The Vickers microhardnesses increase with the increase of the Ce concentration in the samples. On other hand, the micro-hardness of the sample corresponding to the natural face is smaller than those obtained from the cutting face, except those corresponding to the sample with \(x=0.17\) (see Table 6). The results are in agreement with the literature data.  
\begin{table}
\begin{tabular}{c|c|c|c|c|c|c} \hline \hline
**Ce concentration** & **Molecular** & **Crystallographic** & **Relative Density** & **Elasticity Modulus (GPa)** & **Shear Modulus (GPa)** & **Poisson Ratio** \\ \hline
0.09 & 127.62 & Monoclinic + tetragonal & 88.9 & 145.85 & 56.49 & 0.291 \\ \hline
0.13 & 129.58 & Monoclinic + tetragonal & 94.7 & 168.53 & 64.47 & 0.307 \\ \hline
0.17 & 131.53 & tetragonal & 99.9 & 193.43 & 73.16 & 0.322 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Some crystallographic and mechanical characteristics of Zr1-xCe\({}_{x}\)O\({}_{2}\) samples.  
For the samples with \(x>0.09\) we observe a larger microhardness on the face after cutting as comparing with those corresponding to the natural face. It is observable that the results related to the hardness measurements from the sample having a concentration \(x=0.17\) are slightly different compared to the other two concentrations, before and after the mechanical cutting is made. A possible explanation can be that, once the cutting of the natural (original) phase is made, a micro-crack could have appeared on the surface of this sample, the measured hardness being lower on a damaged surface, a result in agreement with literature observations [37].  
We performed the chemical analysis on the surface of the samples and on the surface of fractured samples (see Table 7). The results confirmed that the increase of the Ce concentration will produce an increase of unit cell volumes of monoclinic and the tetragonal phases, respectively (see Tables 1 and 3), in agreement with the literature. On other side, we observed that for the same sample the unit cell volumes will increase from the surface to the inner part (see Tables 1 and 3). It is not clear if we have a continuous decrease of Ce concentration from inner side to the surface of the sample or only the surface layer has another chemical composition as compared with the inner side.
## 4 Results
### Datasets
#### 4.1.2 SECOM Dataset  
The dataset provided by UCI Machine Learning Repository ([[http://archive.ics.uci.edu/mL](http://archive.ics.uci.edu/mL)]([http://archive.ics.uci.edu/mL](http://archive.ics.uci.edu/mL))) is related to a semiconductor manufacturing process. The dataset consists of 1567 observations, and each observation is made up of 591 features representing manufacturing operations of a single semiconductor [56]. The data were collected from the continuous monitoring process using sensors and metrology equipment along the semiconductor manufacturing line. At the end of manufacturing operation, functional testing was performed to ensure that the semiconductor meets the specification for which it is designed. If the result met the expectation, the semiconductor would be classified as the accepted product; otherwise, it would be rejected. There are only 104 rejected cases, whereas there are 1463 accepted cases. Due to its high imbalance ratio, it is difficult to get a high classification performance on the dataset.
# An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching
## 3 The Proposed Method  
To reduce the AI, we designed an improved RIT method by patching image blocks according to the secret segment. The proposed framework of image embedding is shown in Figure 2. We transformed the image in the last (K-T)-classes using traditional RIT firstly. Secondly, the partial AI from the last (K-T)-classes is represented as the secret segment; it will be hidden in the composite image by patching the blocks in the first-T classes. Then, the pixel values of these image blocks will be shifted to generate the transformed image. Thirdly, the remaining AI is processed and embedded by an RDH method. Finally, the process of image embedding is completed, and the stego-image is generated. Note that the secret image extraction is the symmetrical process of image embedding, which is not described in detail here. Since the proposed method mainly improves image block matching, we will focus on this step.  
Before image block matching, we firstly classify the blocks according to their SD values. In fact, [PERSON] et al. [20] counted the distribution of SDs of \(4\times 4\) block for various sizes of 10,000 natural images from a Boss-Bass image database, and found that the SD values of most blocks concentrate on a small range close to zero, and the frequency quickly drops down with the increase of the SD value. Thus, it will be more efficient for the blocking transformation using SD classification.  
In addition, we divided these images into K classes using a K-means clustering method, and calculated the number of each class when \(K=10\), as in Figure 3. Then, we can find that when M is larger, the number of image blocks will decrease and SDs are increased. Therefore, we divide the K classes' blocks into two parts with unequal proportions: the first T-classes for blocks with smaller SDs, and the last (K-T)-classes for blocks with larger SDs. Thus, blocks in the last (K-T)-classes can be transformed by a traditional RIT method to maintain the transformed image quality, and blocks in the first T-classes can be patched according to the secret segment for reducing AI.  
Figure 2: The framework of image embedding of the proposed method.
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 3 Materials and Methods
### Experiment Design  
In this study, we carried out three experiments. All experiments were conducted with different compositions of the input features.  
Experiment 1 evaluated the performance of models trained with different types of features. In this experiment, separate sets of four algorithms were trained with TF-IDF, LIWC, and NRC features, producing 12 models in total. The TF-IDF matrix represents the textual features, whereas the LIWC and NRC matrices represent the psycholinguistic features. The goal of this experiment was to determine which individual type of features had the best predictive capability.  
Experiment 2 evaluated the performance of the classifiers when feature selection was applied. TF-IDF, LIWC, and NRC features selected with Boruta were used to train separate sets of classifiers, resulting in 12 models. The purpose of this experiment was to ascertain whether models trained with reduced features had improved classification performance.  
Experiment 3 evaluated the performance of models trained with different combinations of textual and psycholinguistic features. Given three types of features, we suggest four combinations: TF-IDF+LIWC, TF-IDF+NRC, LIWC+NRC, and TF-IDF+LIWC+NRC. Training four algorithms with four possible combinations of features brings about 16 classifier models. The goal of this experiment was to determine whether combining textual and psycholinguistic features resulted in better performance.
## 3 Methods: Finite Element Modelling
### Tyre-Pavement Contact Model
#### 3.3.2 Contact Analysis  
This study focused on the steady-state analysis of vehicle drive wheel and simplified pavement. Steady-state transport was used in ABAQUS 2021 to simulate the steady-state dynamic interaction between the tyre and rigid pavement surface. For this, the Eulerian description of rigid-body rotation and Lagrangian description of deformation were used to convert the steady-state moving contact problem into a purely spatially dependent simulation that described the rolling of the tyre as a material flow motion through the mesh.  
To conduct a steady-state analysis, the motion state of the tyre can be set to steady-state rolling, braking, or driving. In ABAQUS 2021, the speed at which the tyre travels can be simulated in terms of linear and angular velocities, and the driving state of the tyre can be changed by transforming their combined values [29]; the relationship between the linear and angular velocities is given by  
\[\omega=\frac{v}{r} \tag{4}\]  
where \(\omega\) denotes the angular velocity, \(v\) denotes the linear velocity, and \(r\) denotes the tyre radius of rotation. When the torque of the tyre around the rotation axis is zero, the tyre is in a stable rolling state; when the torque is non-zero, the tyre is accelerating or decelerating.  
In the steady-state rolling analysis model, the tyre pressure was 260 kPa, the load was 2600 N, and the driving speed was 60 km/h, corresponding to a linear speed of 16.67 m/s. The angular velocity was initially calculated to be 57 rad/s based on the radius of the tyre. When simulated in ABAQUS 2021, the angular velocity was appropriately adjusted such that the tyre torque was close to zero. Eventually, the linear velocity was determined to be 16.67 m/s and the angular velocity 56.2 rad/s. Thus, when the linear velocity remained constant, tyres with angular velocities less than 56.2 rad/s were decelerating, whereas tyres with angular velocities greater than 56.2 rad/s were accelerating.  
In the steady-state braking analysis model, the tyre pressure was 260 kPa, the load was 2600 N, the friction coefficient was 0.3, and the driving initial speeds were 30, 60, 90, and 120 km/h. Using the methods described in the previous paragraph, the linear and angular speeds of the tyres were determined for the different speeds during steady-state braking, and simulation analyses were performed using these values.  
Figure 10: Three-dimensional contact model of the tyre and simplified pavement.
# Energy-Saving Routing Protocols for Smart Cities
## 4 Conclusions and Future Works  
In this work, we analyzed routing protocols in two distinct categories, namely, proactive and reactive protocols. The Cupcarbon network simulator was used to evaluate important metrics such as data package delivery rate, average end-to-end delay, average jitter, throughput, and load consumption of battery charge. Thus, the Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Distance Vector Routing (DVR) routing protocols were implemented in the Cupcarbon simulator. In addition, a tool for calculating the range between devices according to the Egli propagation model was developed and integrated into the graphical interface of Cupcarbon.  
The results showed that the DSR protocol was the most suitable option among those implemented for use in conjunction with the \(P_{TX}\) adjustment algorithm proposed in this work, providing energy savings of 11.32% compared to the original DSR. On the other hand, the AODV protocol had better overall performance and had the second-highest power consumption. While the DVR protocol consumed the most energy, it had the best performance in terms of latency; however, it led to high packet loss.  
For this implementation, a mixed network topology was defined using the DSR protocol together with the LoRaWAN protocol. A cloud application was developed to monitor data reception, confirming the correct functioning of the network.
## 4 Results
### Evaluation of Performance of Key Features
#### 4.1.2 Qualitative Evaluation (Ablation Study)  
The aim was to segment and identify the bony features from the ultrasound spine image for automatic scoliosis detection. The LD model successfully segmented the thoracic and lumbar bony features, as shown in Figure 10(c). Figure 10(d) shows the LCS model, which provided better results in the segmentation of the left thoracic bony features (Nos. 5, 6, 7, 8, and 9) compared to Figure 10(c). It implies that the model with a selection gate detects the target bony features better than the LD model. To further improve the LCS model, the conventional shortcut connection is replaced with the inception skip path to form the LDI model. From Figure 10(e), it is clear that the LDI model produces better segmentation of the left thoracic bony features compared to the LD and LDC models. Finally, the proposed LDS U-Net model, as shown in Figure 10(f), generated more accurate boundary segmentation of the left thoracic bony features than the LD, LCS, or LDI models. Figure 12 shows several more qualitative visual comparisons of bony feature segmentation using the LD, LCS, LDI, and proposed LDS U-Net models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Avg. Jaccard Index** & **Avg. Dice Score** & **Avg. F1 Score** & **Avg. Accuracy** \\  & **(Std Dev)** & **(Std Dev)** & **(Std Dev)** & **(Std Dev)** \\ \hline LD & 0.7123 (\(\pm\)0.039) & 0.8204 (\(\pm\)0.036) & 0.8412 (\(\pm\)0.030) & 0.9108 (\(\pm\)0.025) \\ LCS & 0.7280 (\(\pm\)0.037) & 0.8292 (\(\pm\)0.033) & 0.8532 (\(\pm\)0.031) & 0.9203 (\(\pm\)0.021) \\ LDI & 0.7339 (\(\pm\)0.034) & 0.8419 (\(\pm\)0.032) & 0.8704 (\(\pm\)0.029) & 0.9367 (\(\pm\)0.020) \\
**Proposed LDS U-Net** & **0.7415 (\(\pm\)0.03)** & **0.8694 (\(\pm\)0.028)** & **0.8885 (\(\pm\)0.025)** & **0.9592 (\(\pm\)0.020)** \\ \hline \hline \end{tabular} LD: light dense; LCS: light convolution selection; LDI: light dense inception; LDS: light dense selection.  
\end{table}
Table 1: Quantitative evaluation of ablation study.  
Figure 11: Individual segmentation result of the spine ultrasound image: (**a**) Raw image, (**b**) truth mask, (**c**) LD model, (**d**) LCS model, (**e**) LDI model, and (**f**) proposed LDS U-Net model.  
Figure 12: Qualitative comparison of 6 different case models: (**a**) Raw image (**b**) truth mask, (**c**) LD model, (**d**) LCS model, (**e**) LDI model, and (**f**) proposed LDS U-Net model.
## 3 Methods and Materials
### Smart Grid Communication Challenges
#### 3.4.1 Interference  
For the smart grid to work, smart meters must be installed in homes and businesses. In the typical household, more and more technologies are becoming standard. Nowadays, H.A.N.s are almost ubiquitous in homes. Under conditions of dense distribution, Network Area Controllers and smart meters may interact. This might result in inaccurate readings from smart meters, endangering the system's stability. Power line harmonics may cause communication equipment on the smart grid to malfunction.
## 2 Materials and Methods
### Proposed Methodology
#### 2.1.2 Spatial Kriging  
Spatial kriging is an optimal interpolation technique that uses the theory of regression to predict the value at an unobserved location using values of observed neighborhood locations. Here, observed values of neighborhood locations are weighted according to the covariance values. As depicted in Figure 2, consider the location of interest, \(X_{0}\), that is surrounded by the neighboring observed locations, \(X_{i}\).  
The value at location \(X_{0}\), \(Z(X_{0})\), is estimated through the values of \(n\) neighboring sample locations taken as a linear combination of observed location values. Let the weight be associated with the observed value at location \(X_{i}\), i.e., \(Z(X_{i})\) is \(w_{i}\). Then, the spatial kriging estimate of \(Z(X_{0})\) can be computed as follows:  
\[\hat{Z}(X_{0})=k+\sum_{i=1}^{n}w_{i}Z(X_{i})=k+w^{T}\mathbf{Z} \tag{4}\]  
where \(w\equiv(w_{1}\), \(w_{2}\),..., \(w_{n})^{T},\mathbf{Z}\equiv(Z(X_{1})\), \(Z(X_{2})\),..., \(Z(X_{n}))\). The values of \(k\) and \(w\) are calculated by minimizing the mean squared prediction error [18].  
The optimal linear predictor of \(Z(X_{0})\), \(\hat{\mathbf{Z}}(X_{0})\), is given by:  
\[\hat{\mathbf{Z}}(X_{0})=\mu(X_{0})+\boldsymbol{c}^{T}\sum^{-1}(\mathbf{Z}-\mu) \tag{5}\]  
where \(\mu\) is the Lagrange multiplier, \(\boldsymbol{\mu}\equiv(\mu(X_{1}),\mu(X_{2}),\ldots,\mu(X_{n}))^{T}\), \((X_{i})=E(Z(X_{i}))\), \(i=0,1,2,\ldots,n\), \(\boldsymbol{c}\equiv(Cov(X_{0},X_{1}),\ldots,Cov(X_{0},X_{n}))^{T}\), and \(\sum\) is the \(n\times n\) matrix whose \((i,j)^{\text{th}}\) element is \(Cov(X_{i},X_{j})\).  
Then, the minimum value of \(MSE(X_{0},\ w,\ k)\) for a fixed location \(X_{0}\), \(\sigma^{2}(X_{0})\) (spatial kriging variance) is given by [18]:  
\[\sigma^{2}(X_{0})=Cov(X_{0},\ X_{0})-\boldsymbol{c}^{T}\sum^{-1}\boldsymbol{c} \tag{6}\]  
The values of \(\boldsymbol{c}\) and \(\sum\) are calculated through the variogram or a covariance function. The different kriging models (simple, ordinary, or universal) can be obtained by changing the way of modeling \(E(Z(X))\).
## 5 Discussion  
The results of the sensitivity analysis on the number of yard blocks, time windows, and transport companies (forwarders) clearly show the impact of maximizing the benefits of stakeholders.  
When looking at the case of cooperation, the efficiency of the cooperation model was higher as the number of yard blocks and time windows increased. In case 3, the cooperation model showed an efficiency (gap) of 2.46%. This means that the more segmented tasks a truck can reserve, the more cooperation is required. The more detailed the reservation, the easier it is to plan the gate and the yard operations. As the number of transport companies (forwarders) increased, the efficiency of the cooperation model rapidly increased. In case 3, the cooperation model showed an efficiency (gap) of 11.83%. This proves that cooperation is necessary as the competitive relationship intensifies. Transport company (forwarder) can share information with other transport companies (forwarders) beyond communication with the terminal operator, enabling efficient truck allocation.  
Looking at the perspective of the transport company (forwarder), the penalty of the transport company (forwarder) proportionally increased as the number of yard blocks, time windows, and transport companies (forwarders) increased. The increase in the number of tasks a truck can reserve has a minor impact on the transport company (forwarder). However, increasing the number of transport companies (forwarders) means increasing competitors, so the penalty for each transport company (forwarder) increases dramatically.  
From the perspective of the terminal operator, results were quite different from those of the transport company (forwarder). The terminal operator's penalty is proportional to the number of yard blocks and time windows. However, as the number of transport companies (forwarders) increased, the terminal operator's penalty decreased in inverse proportion. The fact that a small number of transport companies (forwarders) participate in the TAS means that the management efficiency of the terminal operator is low. It is because one transport company's allocation schedule is a big part of it. On the other hand, as the number of participating transport companies (forwarders) increases, the terminal operator's management efficiency increases. This is because as the number of transport companies (forwarders) increases, the number of unreserved slots decreases.