esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing  
For each situation of exposure, local SAR in the entire model was estimated, that is the SAR value for each grid cell, calculated as the ratio between the absorbed power in each grid divided by grid mass. Then, we found the maximum local SAR of all the grid cells for each human model.  
A radiofrequency electromagnetic wave exponentially decays from the surface to deeper regions in the human body [6]. According to this, SAR at a certain depth in the tissues can be calculated from the knowledge of the SAR value at the surface of the skin and the so-called penetration depth or skin depth. This quantity is conventionally defined as the depth corresponding to the SAR of \(1/e^{2}\) (or 13.5%) of SAR at the surface [6; 27].  
For the phone call scenario, the SAR penetration depth along the zdirection, from the surface (ear skin) to the deeper regions, has been calculated for all the models.  
The SAR trend along the zaxis has been evaluated from the axial SAR map extracted from FDTD results, while considering the cell with the maximum value of the local SAR.  
Moreover, the SAR at a 1 mm depth (by percentage with respect to its peak value) in the head model was also calculated.  
The most recent ICNIRP guidelines [7] give Basic Restrictions for frequencies >6 GHz to 300 GHz in terms of Absorbed Power Density (Sab) rather than SAR. To obtain a direct understanding of the safety of the chosen exposure scenario, we calculated Sab according to the ICNIRP definition:  
\[Sab=\iint\limits_{A}dxdy\int_{0}^{zmax}\rho(x,y,z)\cdot SAR(x,y,z)dz/A, \tag{1}\]  
where the body surface is provided at \(z=0\), \(zmax\) is the depth of the body at the corresponding region, and \(A=0.02\) m \(\times\) 0.02 m represents the averaging area. The given limit for \(Sab\) is 20 W/m\({}^{2}\).
## 2 Related Work
### Knowledge-Driven Prediction  
Knowledge-driven approaches build mathematical models that incorporate domain-specific prior knowledge, treating pedestrians as physical entities governed by physical laws. The Social Force Model [17] conceptualizes pedestrian movement as a result of social forces in simulating pedestrian dynamics, crowd evacuations, and understanding social behaviors in crowded spaces. These forces are not physical but represent the motivation of individuals to move towards a destination, avoid collisions, and maintain a personal space. Since SFM is built based on macroscopic mechanical principles, it pays more attention to the simulation of group behavior and often ignores the microscopic behaviors of individuals. The model's accuracy depends on the precise tuning of its parameters, which can vary significantly across different scenarios. Cellular Automata [18] uses a floor field model to guide pedestrians toward their target positions, and when conflicts occur, game theory is used to resolve conflicts. Since CA relies on local rule updates and a limited number of neighbor states around the cell, its capabilities to capture the complex global dynamics may be hindered. The Velocity Model [19] uses a series of equations to predict the future path of a pedestrian, assuming uniform linear motion and constant acceleration. However, it focuses on immediate avoidance rather than long-term path planning, and its effectiveness often decreases in real-time applications where the environment changes rapidly. The Gaussian Processes Model [20; 21] predicts the distribution of possible trajectories based on a set of observed trajectories and infers a continuous function that describes the motion patterns to provide a measure of uncertainty in the predictions. GP typically involves operations on large matrices, leading to high computational costs with increasing data size. Crowd Analysis [22; 23] statistically analyzes and models group behavior, structure, and individual behavior. Nevertheless, this approach results in an inadequate generalization capability of the model, and the model's performance can deteriorate in unfamiliar situations or settings. These above methods are hand-crafted by the modeler and can easily produce unrealistic results.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
###### Abstract  
Worldwide, cities with mountainous areas struggle with an increasing landslide risk as a consequence of global warming and population growth, especially in low-income informal settlements. Landslide Early Warning Systems (LEWS) are an effective measure to quickly reduce these risks until long-term risk mitigation measures can be realized. To date however, LEWS have only rarely been implemented in informal settlements due to their high costs and complex operation. Based on modern Internet of Things (IoT) technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication protocol, the Inform@Risk research project is developing a cost-effective geosensor network specifically designed for use in a LEWS for informal settlements. It is currently being implemented in an informal settlement in the outskirts of Medellin, Colombia for the first time. The system, whose hardware and firmware is open source and can be replicated freely, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g., tilt sensor) on board and can be connected to various different sensors including a newly developed low cost subsurface sensor probe for the detection of ground movements and groundwater level measurements. Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed LEWS offers a good benefit-cost ratio and in the future can hopefully find application in other parts of the world.  
early warning system; landslides; geosensors; monitoring; Colombian Andes; low income settlements; informal settlements; IoT 2021  
1 Chair of Engineering Geology, Technical University of Munich, 82024 Munich, Germany; [EMAIL_ADDRESS]  
2 AlpGeorisk, 85716 Unterschleiheim, Germany; [EMAIL_ADDRESS]
## 3 Monopulse Ratio Curve Improvement
### Proposed Monopulse Rate Curve Improvement Method Based on the Presented Tile
In this study, to solve this problem, the received signal was synthesized using a sum and difference pattern to maintain, in the location estimation region, a relatively uniform received signal strength. The synthesized received signal strength is shown in Equation (21). \[PF_{RSSI} = P_{\sum}+0.4195P_{\Delta} \tag{21}\]  
Figure 13 shows the result of converting the synthesized received signal strength into a radiation pattern. In Figure, #1, #2, \(\cdots\), #10 represent tile indices. Table 4 shows the results generated by comparing the estimation performances of the monopulse ratio curves of the proposed and other techniques (eight elements (presented tile), six elements, and four elements). Here, the monopulse rate curve was chosen for comparison with the proposed technique, considering a case in which the MAE mean was approximately 0.05. The \"beamforming number\" mentioned in Table 4 represents the beamforming number at 36\({}^{\circ}\), which is the divide region for a single tile, divided into the linear region of the selected monopulse ratio curve. Figure 14 shows the beamforming results based on the normalized pattern. As a result of the comparative analysis, we can confirm that the performance of the method proposed in this paper was improved for its use in aviation communication networks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{Case} & Presented Tile & \multicolumn{2}{c}{General Method} & Proposed \\  & (8 Elements) & 6 Elements & 4 Elements & Method \\ \hline \(\phi_{c}\) & 3.6\({}^{\circ}\) & 4.5\({}^{\circ}\) & 6.0\({}^{\circ}\) & 18.0\({}^{\circ}\) \\ MAE mean & 0.044 & 0.049 & 0.051 & 0.01 \\ Beamforming num. & 5 & 4 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimation accuracy comparison. Figure 13: Synthesized patterns in (a) single tile and (b) antenna system.
# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 2 Methodologies
### Holt-Winters Forecasting Model  
There are two variations of the Holt-Winters forecasting method: the additive and multiplicative models of the seasonal variations. The multiplicative model is suitable for a time series with an increasing seasonal pattern that is proportional to the data level. In contrast, the additive model is suitable for fitting a time series with a constant seasonal variation. In this work, the multiplicative method is utilized for forecasting the maximum load of Kuwait.  
The Holt-Winters model consists of forecast equation and smoothing equations of the level, trend, and seasonality of the time series. Assuming that \(s_{t}\) represents the seasonality, \(b_{t}\) to represent the trend and that \(l_{t}\) corresponds to the level with smoothing parameters of \(\alpha,\;\beta\), and \(\gamma\). Therefore, the Holt-Winters multiplicative forecast model can be expressed as:  
\[\hat{y}_{t+h\mathrm{lf}}(k)=(l_{t}+h\mathrm{b}_{t})s_{t+h-m(k+1)} \tag{7}\]  
\[l_{t}=\alpha\frac{y_{t}}{s_{t-m}}+(1-\alpha)(l_{t-1}+b_{t-1}) \tag{8}\]  
\[b_{t}=\beta^{*}(l_{t}-l_{t-1})+(1-\beta^{*})b_{t-1} \tag{9}\]  
\[s_{t}=\gamma\frac{y_{t}}{(l_{t-1}+b_{t-1})}+(1-\gamma)s_{t-m} \tag{10}\]  
where \(m\) denotes the seasonality period and the smoothing parameters defined as:  
\[0\leq\alpha\leq 1,\qquad\quad 0\leq\;\beta\leq 1,\qquad\quad\text{and }0\leq \gamma\leq(1-\alpha)\]  
In the Holt-Winters multiplicative method, the seasonality component is expressed relatively, and the time series can be seasonally adjusted by dividing through by the seasonality component. Equation (8) corresponds to the level, and it is expressed as a weighted average between the seasonal term and the non-seasonal term \((l_{t-1}+b_{t-1})\). While Equation (9) corresponds to the trend of the time series, which is expressed as a weighted average of the trend between the level and previous slope \(b_{t-1}\). Equation (10) corresponds to the seasonality and is calculated by the weighted average between the current and previous seasonal components of the time series.
# Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning
## 2 Materials and Methods  
This study introduces an effective yet simple framework for image retrieval. The feature descriptor is extracted from a backbone network and goes further through a projection module to become embedded vectors for retrieval. We study the behavior of the output representation space when training with a contrastive loss, in particular, how augmenting impacts the space properties and the performance of image retrieval on low-resolution inputs. Our framework is illustrated in Figure 2. This section demonstrates our framework with three modules. A feature extractor module extracts a visual representation of a given image, while a projection head trained in a contrastive approach helps to map visual representation to an embedding space so that the similarity of samples can be calculated. Finally, we introduce an auxiliary module with classification loss and triplet loss, which significantly enhances the category retrieval's performance.  
Figure 2: The overall architecture of our proposed framework. The framework is described using five major computing steps. (1) An input image is augmented into two views with different resolutions for contrastive learning. (2) Two augmented images go through a shared encoder module to extract representation vectors. (3) A projection head is added on top of the encoder model to perform non-linear mapping into an embedding space so that (4) contrastive and triplet loss can be established to maximize the similarity of positive pairs and dissimilarity of negative pairs. (5) We add a linear layer with softmax activation followed by batch normalization to calculate all samples’ class probability from two branches.
## 7 Conclusions  
In this paper, a study of 6G and the Tactile Internet was carried out. As the future Tactile Internet has stringent requirements, such as latency, reliability, and data rates, it is necessary to design the 6G networks to support its applications. Hence, the research on Tactile Internet applications was undertaken to derive the characteristics for select applications of virtual reality/augmented reality, remote surgery gaming, smart cities, autonomous vehicles, industrial automation, brain-machine interfaces, and telepresence/holography. To discuss these characteristics further, we considered several parameters and other requirements of 6G along with 5G and support technologies. This approach identifies the design considerations for 6G and allied technologies for realizing these applications. It highlights the key performance parameters, such as end-to-end latency, reliability, data rate, update rate, security, scalability, power efficiency, edge computing capability, AI integration, interactivity, jitter, haptic feedback quality, multi-sensory integration, biocompatibility, localization accuracy, and interoperability that must be met by 6G to support Tactile Internet applications. We also presented classification and explored various sensors crucial for Tactile Internet applications.  
This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Characterization Method  
The diffraction pattern was tested and compared with the joint committee on powder diffraction standard (JCPDS) values using an X-ray diffractometer (XRD) of the TTRAIII model manufactured by Rigaku Company, Tokyo, Japan. Moreover, inspection was performed using the grazing incidence X-ray diffraction (GIXRD) method. The field emission scanning electron microscope (FE-SEM) of the S4700 model, manufactured by Hitachi Company, Tokyo, Japan, was used to examine the thickness of thin films in a cross-sectional imaging system and to determine the grain size in the surface imaging system at different depositing conditions. To investigate the substrate surface roughness, the atomic force microscope (AFM) method was used to determine the surface topography using the NX10 model, manufactured by Park Systems (Suwon, Republic of Korea). UV-vis spectrophotometer model Cary 7000 UMS (Agilent Technologies Company, Santa Clara, CA, USA) was used to analyze the optical properties of ITO film within the measurement range of 200-2200 nm, displaying the percentage transmittance (%T). The point probe model RM3 (Jandel Engineering Limited, Leighton Buzzard, UK) was used to measure electrical properties with the Hall effect model HMS-3000 (Ecopia Corporation, Anyang, Republic of Korea). In addition, solar simulator model XPS-300 (Solar Light Company, Inc., Glenside, PA, USA) was used to test the efficiency of DSSC devices under the short circuit current or current density (J\({}_{\text{sc}}\)), open circuit voltage (V\({}_{\text{oc}}\)), maximum current (I\({}_{\text{max}}\)), maximum power (P\({}_{\text{max}}\)), and fill factor (FF).
## 5 Experimental Results
### Dataset
#### 5.2.1 Pcgan  
In our experiments, we randomly selected 5000 real face images from CelebA dataset and 5000 fake ones of good quality from the fake face image database ([[https://goo.gl/ozy7](https://goo.gl/ozy7) Kq]([https://goo.gl/ozy7](https://goo.gl/ozy7) Kq)) shared by [2]. All collected images were in 256 \(\times\) 256 and stored in PNG format. In our experiments, we resized all images into 64 \(\times\) 64 using bilinear interpolation and compressed them using lossy JPEG compression with a quality factor of 95. Finally, we divided the images into the training set (3750 pairs of true-fake face images), the validation set (1250 pairs). To achieve convincing results, we used four-fold cross-validation and reported the average results in the following experiments.
# Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks
## 4 Discussion  
In order to verify the generalization ability and prediction accuracy of the GA-BP ANN model, the remaining 15 sound samples were used as model verification samples, and the established GA-BP ANN and MLR models were used for the comparative analysis. As shown in Figure 17, the prediction error of the GA-BP ANN model for the SQ in EVs is generally lower than that of the MLR model. Moreover, the average percentage error of the GA-BP ANN model is only 5.81%, which is much lower than the 8.14% of the multiple linear regression model, indicating that the GA-BP ANN model is more accurate in predicting the subjective evaluation score of the SQ.  
The performance index (PI) is more suitable for comparing the performance of various prediction models. The PI of the evaluation model is evaluated by the distance function \(d^{2}\) between the actual value and the model prediction.  
\[d^{2}=\frac{1}{n}\sum\left(lg\mathrm{y_{i}}-lg\mathrm{c_{i}}\right)^{2}, \tag{2}\]  
where \(\mathrm{y_{i}}\) is the actual value, \(\mathrm{c_{i}}\) is the model prediction value, and \(\mathrm{n}\) is the number of test samples. The smaller the function \(d^{2}\), the closer the predicted value to the actual value, which reflects the better  
Figure 16: The relative error of the GA-BP ANN model.  
Figure 17: Comparison of the percentage error between the actual scores and the predicted values of the two models. MLR, multiple linear regression.  
predictive performance of the model. Moreover, according to Equation (2), the \(d^{2}\) value of the MLR model is 0.0339, and the \(d^{2}\) value of the GA-BP ANN model is 0.0245, which is far lower than that of the MLR model, indicating that the GA-BP ANN model has better prediction performance. By analyzing the reasons, the subjective sound evaluation for the auditory perception characteristic is a nonlinear process. The established model using the MLR analysis is a linear approximation of the nonlinear process, while the GA-BP ANN model has a good nonlinear input-output relationship and can better simulate the complex nonlinear function relationship, so its accuracy is higher than that of the MLR model. Moreover, the GA-BP ANN model has strong learning and generalization abilities, which are not available in the MLR model.  
In the subsequent analysis and evaluation of the SQ of EVs, the psychological objective parameters of sound can be calculated, and the GA-BP ANN evaluation model can be used to predict the subjective scoring values of the sound samples, thus eliminating the tedious and time-consuming subjective hearing tests and simplifying the process. The evaluation process of the GA-BP ANN model compared with the jury test is shown in Figure 18.
# Impact of Putting on Traffic Safety: A Synthesis of Research Findings
## 2 Research Methodology
### ML-Based Prediction Models  
The estimation of the accident rate represents a regression problem. Due to the existence of multiple dependent variables, previous studies utilized regression models such as negative binomial regression [9; 18; 19; 20], zero-inflated Poisson regression [20], maximum likelihood estimation of a Poisson regression [15], logistic regressions [21], seemingly unrelated regression equations [22], and random-parameters negative binomial regression [23]. However, the advances in ML algorithms open new horizons that can improve the prediction of the accident rate. Therefore, this study employed multiple ML algorithms to develop prediction models using the extracted dataset.  
Subsequently, the third stage involved developing prediction models for traffic accident rates based on rut depth values under varying conditions. Figure 3 illustrates the different phases in the process of developing ML models and evaluating the criticality of various features used as inputs for prediction. The first phase involved building the database by reviewing and selecting relevant literature, followed by the extraction of data points. Necessary pre-processing was performed, such as selecting a uniform unit for accident rate; in this case, the number of crashes per 100 million vehicle kilometers a year (100 MVkm) was adopted. Ultimately, a dataset representing the relationship between rutting and accident rates was constructed by extracting data points from a subset of studies.  
Figure 2: Literature-retrieval methodology.  
As presented in Figure 3, the second phase focused on preparing the data for the development of the ML models. Initially, the dataset was validated, and the relevant features were selected. Next, the dataset was divided into 90% for training and validation and 10% for testing. K-fold cross-validation was employed to validate the models and minimize the risk of overfitting. The remaining 10% was used for testing. The next phase involved the development and evaluation of the ML models. The developed models included decision trees (DT), random forests (RF), artificial neural networks (ANN), and support vector machines (SVM). Model performance was assessed using root mean square error and absolute root mean square error. Finally, the weights of different attributes were evaluated. The development of the models was carried out using the student version of Altair Al Studio software 2024.0.0 (formerly RapidMiner Studio) [24].
# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 4 Condition-Adaptive MPC Controller  
### Factors Affecting MPC Path Tracking  
In the MPC controller, the prediction horizon \(N_{\text{p}}\) indicates the number of future time steps the system predicts, and the control horizon \(N_{\text{c}}\) represents the number of groups of control increments the system solves in a control cycle. As shown in Figure 3,  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter/Unit** & **Value** \\ \hline Sampling period \(T\) & 0.05 \\ Weighting matrix \(\boldsymbol{Q}\) & Diag [200, 100, 100] \\ Weighting matrix \(\boldsymbol{R}\) & [5 \(\times\) 10\({}^{4}\)] \\ Weight coefficient \(\rho\) & 1000 \\ \hline \end{tabular}
\end{table}
Table 2: The controller parameters.  
when \(N_{\text{P}}\) is large, the controller can predict a longer period, but the weight assigned to tracking errors far from the current position will be greater, leading to an increase in the cumulative error and heightened complexity in controller solution. When \(N_{\text{P}}\) is small, the controller has a lower calculation burden, enabling faster system response and higher real-time performance. However, the predicted distance is too short, which reduces system responsiveness to the future. Under the constraint of the controller on the front wheel angle, the vehicle may fail to turn in time, leading to path tracking failure. When \(N_{\text{c}}\) increases, the sensitivity and accuracy of the controller will be improved, but this will increase the amount of calculation and reduce the real-time performance and stability of the system. When \(N_{\text{c}}\) decreases, the sensitivity and accuracy of the controller decrease, but the stability and real-time performance of the system will be improved.  
Vehicle speed and road curvature are pivotal factors influencing MPC path tracking. During path tracking, the vehicle's anticipated rotation angle varies in tandem with alterations in road curvature [36]. Given that the controller imposes limitations on steering angular velocity, the vehicle necessitates a certain duration to output the desired steering angle. As depicted in Figure 4, when the vehicle is moving at a slower speed, its turning radius is smaller; conversely, at higher speeds, the turning radius increases. In such scenarios, if the vehicle traverses a curve with significant curvature, a substantial tracking deviation may arise.  
Figure 3: Influence of the control time domain on path tracking. (**a**) Large Np; (**b**) Small Np; (**c**) Large Nc; (**d**) Reference yaw angle.  
In summary, the tracking performance of the MPC is highly contingent upon its time domain parameters. Moreover, the optimal time domain parameters of the system are not static; they undergo changes in response to variations in the current road curvature and vehicle speed.