esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The control focuses on different areas of rooms such as workplaces, where the actual conditions for control implementation contain space restrictions for positioning the sources. Instead of distributing several and usually big-volume monopoles, according to the antinode of the modes under control, as is often met in the literature, the designed low-frequency control source is concentrated in a fixed position and can be located anywhere in the modal field. The local noise control is attained by reducing the sound pressure at the microphone position. This work extends our previous studies on low-frequency noise control simulations with quadrupole sources [23; 24], and the development of small low-frequency compound sources with low force factor (low-\(Bl\)) drivers [25; 26], suitable for control application in small common or factory control rooms. In the study of [24], the successful attenuation of all the investigated modal frequencies would not be possible if monopoles or dipoles were solely used. A compound source was simulated in global control by reducing the total acoustic potential energy of harmonic fields in a small enclosure, compared to a method with distributed sources [27]. Furthermore, the low-\(Bl\) drivers have a high efficiency in a limited frequency region [28], so they can be tuned at low-frequency resonances, \(f_{S}\), in designs with small cabinets. Multiple such loudspeakers can be used as narrow-band components to form an ideal subwoofer system for the optimization of low-frequency reproduction [29]. The proposed ANC system is integrated with a developed GA-based software that optimizes the control signals in real time to attenuate a noise field. The challenge is to utilize the low-frequency compound sources, which, with the beneficiary search behavior of genetic algorithms, are combined to offer an alternative solution to a common problem of principal interest. Given a quadrupole in different setups as a control actuator and a limited number of possible positions, the target is to find the optimal control parameters for maximum noise attenuation in predefined areas. The experimental results confirm the efficacy of the presented control design. The main contributions of this paper are as follows. Firstly, the adaptation of the compound source radiation is utilized to couple with the modal field. As a result, there is no need to use multiple sources in a limited space. Secondly, the system adapts to any available position and controls multiple frequency resonances at different single areas without the need to reposition the secondary source. Thirdly, the low-_Bl_ loudspeakers are beneficial for compound sources in ANC. This novel system offers the flexibility to adapt the control regardless of the room space and shape. To the best knowledge of the authors, this technique has not been applied to low-frequency noise control.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 3 Testbed Architecture  
HANSEL incorporates the technologies mentioned above and a set of complementary services in an expandable architecture based on the aggregation of microservices that interact between them and with the external users by means of the RESTful Application Programming Interface (API) protocol. RESTful offers a flexible interface between all building blocks, that encapsulate each of the Hansel services (detailed in Section 4) via a Docker container.  
This distributed architecture allows each service to run independently and in a distributed manner, and albeit in the current implementation they are fully contained in a single computer, the system hasbeen designed so that some (or all) of them can reside in external virtual computers or even distribute several service replicas in different clusters.  
This approach makes it specially suitable to incorporate new technologies in the future or upgrade existing components without affecting the rest of the system (because of e.g., dependency renewal). In addition this architecture is specially convenient in a multi-team development environment: Each developer or team can implement their service in their environment of choice (database, programming language, etc.).  
Being a distributed system, in which each service has been developed independently, with different technologies (database, programming languages, etc.), a critical limitation comes from the fact that interfaces need to be specially taken care of, specially for the services that collect data from other services (e.g., CAC service). A modification in the interface (i.e., RESTful URL or parameters) might break the testbed. In HANSEL this has been addressed by placing some tests that could potentially detect breaking conditions such as the one mentioned.  
The development process is also very tied to the architecture. Developers implement in a Continuous Integration/Continuous Development (CI/CD) environment: They modify code and push their changes into ESA's Gitlab environement, which triggers the execution of tests that, if they are successful, will immediately build the new Docker images for the service that will be also deployed into the _testbed_. In this way, the developer will not have to worry about the deployment process and the new changes will be instantaneously available in production.  
An early implementation of the _testbed_ was pursued through Amazon Web Services (AWSTM) services and preliminary test campaigns were performed through a remote server physically far from the users connected to the different services. During the COVID-19 emergency in March 2020, an outage of the supply infrastructure occurred where the _testbed_ was located (i.e., Universitat Autonoma de Barcelona). The agile Docker-based architecture allowed a quick, temporary remote backup of the central node to an AWSTM service and a quick restoration of the service afterwards.
# Enhancing an Imbalanced Lung Disease X-ray Image Classification with the CNN-LSTM Model
## 2 The Literature Review
[32] compared two deep-learning models, namely, convolutional neural networks (CNN) and a combination of CNN and long short-term memory (LSTM). The dataset consists of 4095 CXR images (1400 of normal conditions, 1350 of COVID-19, and 1345 of pneumonia). Both CNN and CNN-LSTM models are evaluated using a confusion matrix and compared in terms of performance. The experimental results demonstrate that the CNN-LSTM model outperforms the CNN model, achieving an overall accuracy of approximately 98.78%. It also exhibits high precision and recall, reaching 99% and 98%, respectively. These findings suggest that the proposed CNN-LSTM model can contribute to fast and accurate COVID-19 detection. The previous studies primarily focused on the development of deep-learning algorithms and certain preprocessing methods to classify lung diseases. Furthermore, the utilization of LSTM networks has been recognized as an effective approach to achieving higher performance scores [5; 25; 26; 30; 32]. To the best of our knowledge, the problem of imbalanced datasets has been given limited consideration in these studies. Therefore, our research aims to address this gap by focusing on selecting the optimal architecture for the CNN-LSTM model and tackling the challenges associated with imbalanced datasets. We plan to employ various image augmentation techniques to improve the model's performance and enhance its ability to handle imbalanced data.
# Streamlining Haptic Design with Micro-Collision Haptic Map Generated by Stable Diffusion +
## 3 Methods
### MCHa Map
#### 3.2.1 Intensity Layer  
The intensity layer represents the vibration strength experienced when the visual texture is touched. We designed a converter to transform visual texture images into the MCHa intensity layer. To suppress image noise, Gaussian blur is applied as an initial step. This widely used smoothing technique employs a Gaussian function as a convolution kernel, averaging each pixel with its surroundings to achieve a smooth, refined image. \(I(x,y)\): input image; \(G(u,v)\): Gaussian kernel weights; and \(\sigma\): standard deviation of the Gaussian function, controlling the blur strength.  
\[I_{\text{blurred}}(x,y)=\sum_{u=-k}^{k}\sum_{v=-k}^{k}G(u,v)\cdot I(x+u,y+v)\]  
\[G(u,v)=\frac{1}{2\pi\sigma^{2}}\exp\left(-\frac{u^{2}+v^{2}}{2\sigma^{2}}\right)\]  
Then, during micro-collision interactions, noticeable height differences occur when the fingertip comes into contact with or leaves bumps. These transitions are expected to produce more pronounced vibrations, making it necessary to detect the edges of such bumps in the visual texture. To achieve this, we applied the Sobel algorithm for edge detection in images [37; 38]. To extract edge details from the image, the Sobel algorithm [37; 38] is used to compute the gradient by assessing pixel value variations in horizontal and vertical directions. Prominent edges in the visual texture are highlighted with color values mapped proportionally to their gradient significance. The horizontal gradient is  
\[G_{x}=I*K_{x}\]  
The vertical gradient is  
\[G_{y}=I*K_{y}\]  
The Sobel kernels are defined as  
\[K_{x}=\begin{bmatrix}-1&0&+1\\ -2&0&+2\\ -1&0&+1\end{bmatrix},\quad K_{y}=\begin{bmatrix}-1&-2&-1\\ 0&0&0\\ +1&+2&+1\end{bmatrix}\]  
Figure 2: Generating MCHa map. The figure shows how the MCHa map was generated, and the source of intensity layer and frequency layer.  
Using the horizontal gradient \(G_{x}\) and vertical gradient \(G_{y}\), we calculate the gradient magnitude for each pixel, which represents the edge strength.  
\[M(x,y)=\sqrt{G_{x}^{2}+G_{y}^{2}}\]  
For the intensity layer, we mapped the grayscale values of the MCHa maps to the _Intensity_ parameter in Apple's Core Haptics API. In this case, the mapping is direct: higher grayscale values represent stronger vibration intensity. Let the grayscale intensity \(G\in[0,255]\), and the corresponding _Intensity_ parameter \(I\in[0,1]\); we define a linear mapping as  
\[I=\frac{G}{255} \tag{1}\]  
This ensures that a pixel with the maximum grayscale value (\(G=255\)) produces the highest haptic intensity (\(I=1\)), and a pixel with \(G=0\) results in the minimum intensity.  
In our test environment, the _Intensity_ parameter corresponds to a physical vibration strength ranging approximately from 0 g to 1.3 g, where \(g\) denotes the gravitational acceleration. This linear relationship enables a consistent and scalable mapping from the generated MCHa maps to perceptually meaningful vibration feedback on typical LRA-equipped devices.
## 3 Methods
### Category-Adaptive Sampling Method
#### 3.1.4 Dynamically Adjust the Oversampling Factor  
To effectively address category imbalance while mitigating the risk of overfitting, a periodic oversampling strategy is proposed. Rather than maintaining a fixed oversampling rate throughout training, this strategy periodically updates the oversampling factor to reduce computational overhead per epoch and enhance model generalization. Specifically, the oversampling factor \(\gamma_{s}\) is updated every predefined interval (e.g., every 10 epochs), and its magnitude is gradually decreased as training progresses. This dynamic adjustment allows the model to focus more on underrepresented categories in early training stages while reducing the influence of oversampling in later stages. The update rule is defined in Equation (6):  
\[\gamma_{s}=\gamma_{\text{max}}\cdot\left(1-\frac{epoch\mod\ interval}{N_{ \text{epochs}}}\right) \tag{6}\]  
Among them, \(\gamma_{\text{max}}\) is the initial oversampling factor, and \(epoch\mod\ interval\) represents the remainder of the current \(epoch\) and the periodic update interval \(interval\), which determines the adjustment ratio within the current period; \(N_{\text{epochs}}\) is the total number of training rounds, and \(interval\) is the interval of periodic updates (for example, updated once every 10 epochs).  
This gradually decreasing oversampling intensity helps prevent overfitting to minority classes in later stages of training and improves the generalization capability of the model.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 3 IaaS Pricing Historical Data and Analysis
### Dataset Description
The prevalence of the _memory-optimized_ instance family, as shown in Table 2, highlights the significance of delivering cloud services with robust memory capabilities. This instance family satisfies the expanding demand for memory-intensive tasks, such as data processing and analytics, to be performed by computational resources that are particularly designed for such tasks. Likewise, the prevalence of the _storage-optimized_ instance family demonstrates the growing significance of storage capacity and efficiency in cloud services. With the rise of data-intensive applications and the ever-increasing volume of data produced, the demand \begin{table}
\begin{tabular}{l l} \hline \hline
**Instance Family** & **Offers** \\ \hline _Memory-optimized_ & 207,843 \\ _Storage-optimized_ & 128,812 \\ _General purpose_ & 113,364 \\ _Compute-optimized_ & 82,958 \\ _GPU instance_ & 24,350 \\ _FPGA instances_ & 1040 \\ _Machine-learning ASIC instances_ & 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Instance families and their number of offers across the years 2016–2022. for efficient storage solutions has increased significantly [33]. On the other hand, the emergence of the _GPU instance family_ suggests a niche market within the larger landscape of cloud services. GPUs (graphics-processing units) are highly specialized hardware components that excel at graphics-intensive tasks and accelerated computation, such as machine learning and artificial intelligence programs. The relatively smaller number of GPU instances compared to other families suggests that these resources appeal to a subset of users with specialized needs and duties.
## 4 Results
### Monitoring of the RCA and Calibration Bias
#### 4.1.3 Change in RCA due to the Calibration Status  
The RCA of GDK, KSN, PSN, and SSP radars changed slightly after the routine inspection due to the change in calibration status, such as loss in the transmission path, radar constant, and transmitted power. The mean RCA of GDK radar for P1 was \(-0.1\) dB with a STD of 0.17 dB, while the mean RCA of GDK radar for P2 was 0.69 dB with a STD of 0.15 dB. Moreover, the \(\varepsilon_{\rm SC}\) changed from 0.04 dB to \(-1.11\) dB. The \(\varepsilon_{\rm SC}\) was close to 0.0 dB for P1 and \(-1.0\) dB for P2. The difference in mean RCA between P1 and P2 (+0.80 dB) and the change in \(\varepsilon_{\rm SC}\) (\(-1.15\) dB) were similar. This resulted from the change in loss in the transmission path (1.67 to 1.29 dB) and transmitted power (671.82 to 667.38 kW). The RCA of KSN radar for each period was \(-0.02\)\(\pm\) 0.20 dB, \(-1.09\)\(\pm\) 0.18 dB, and \(-1.44\)\(\pm\) 0.20 dB. The mean RCA of PSN radar slightly changed two times (P1-P2: \(-0.17\) to \(-0.31\) dB, P2-P3: \(-0.31\) to \(-0.57\) dB). The mean RCA of the SSP radar slightly changed from 0.03 dB (P1) to \(-0.35\) dB (P2). For the MYN radar, RCA significantly changed at the end time of P1. This change was due to the change in calibration bias (\(\varepsilon_{\rm SC}\) decreased from 3.64 to 0.63 dB). The calibration bias (0.98 dB, red dot in Figure 7) was estimated from \(\Delta\)RCA (2.66 dB, RCA at the end of P1 was 0.63 dB, RCA at the start of P2 was 3.29 dB) despite clear weather. It was noted that GC monitoring is valuable for use in an operational environment, especially during the non-precipitation period. The RCA of the GSN radar significantly changed twice due to the change in calibration bias. The \(\Delta\)RCA was used to estimate the calibration bias in \(Z_{\rm H}\) using the GSN radar (red dots in Figure 7). The change in RCA (P1-P2: 0.12 to 3.97 dB, \(\Delta\)RCA = 3.85 dB, P2-P3: 4.24 to 0.05 dB, \(\Delta\)RCA = 4.19 dB) was similar with the change in \(\varepsilon_{\rm SC}\) (P1-P2: \(-1.45\) to \(-6.14\) dB, P2-P3: \(-6.14\) to \(-1.12\) dB). When the RCA changed, the \(\varepsilon_{\rm GC}\) could be estimated regardless of weather conditions, and it was similar to the \(\varepsilon_{\rm SC}\) estimated on the next precipitation event.  
Figure 9: Example of azimuth angle slip issue. (**a**) Time series of ground clutter (GC) reflectivity statistics for MYN radar from 0000 KST 1 June 2020 to 0000 6 June 2020. The black and blue dots represent the RCA and dMedian. (**b**,**c**) PPIs of \(Z_{\rm H}\) at the elevation angle of \(-0.8^{\circ}\) of MYN radar at 6 June 2020 0845 KST and 1840 KST. The rings are intervals of 30 km.
# Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control
## 2 Literature Review
In order to get optimal solutions, the weight used in the objectives in model predictive control is decided by expert's experience or trial-and-error normally, while in some cases, the weight is adjusted dynamically. This study makes the first attempt to combine model predictive control with a dynamic multi-objective genetic algorithm to find Pareto optimal solutions for freeway congestion control problem. Then TOPSIS method is adopted to select the solution to be implemented. In order to assess the performance of the proposed approach [21], the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. Compared with classical MPC approaches that deal with multiple objectives by the weighted sum approach, the model predictive control method based on dynamic multi-objective genetic algorithm can better fulfill goals of alleviating traffic congestion, reducing emissions and fuel consumption although it may also be computationally expensive. With the rapid development of multi-objective evolutionary algorithms, our study suggests the potential of model predictive control method based on dynamic multi-objective genetic algorithm to deal with a wide range of freeway congestion control problems in the future. In this paper, the METANET model is used to predict the traffic states, the integrated VT-macro model is used to calculated total emissions and fuel consumptions. Therefore, we will discuss the METANET model and VT- macro model first, then the multi-objective control problem solved by MPC_CPDMO-NSGA-II algorithm and related constraints will be described later. Finally, the simulation result will be discussed. The acronym List are given in Appendix A.
## 5 Conclusions  
In this work, a 3D U-net DL model was trained on CBCT an CT scans in order to segment bladder, rectum, and prostate on CBCT scans. The proposed approach significantly outperformed all the DIR-based segmentation methods applied on our dataset in terms of DSC, JI, and SMBD for bladder and rectum. The conclusions were more mitigated concerning prostate, where the DL-based segmentation did not significantly outperform alternative approaches. A Bland-Altman analysis on the manual and predicted organs' volumes revealed a low bias on the predicted volumes for all organs, but higher precision (i.e., a larger spread of the volumes) for prostate than for the other organs. Furthermore, the study showed that the cross-domain data augmentation consisting of adding CT to the CBCT scans in the training set significantly improved the segmentation results. A further step will be to highlight these improvements by showing the better tumor coverage and reduction in the doses delivered to organs at risk that it allows.  
Conceptualization: J.L., E.B., P.D., C.D.V., J.A.L., and B.M.; methodology: J.L., E.B., P.D., C.D.V., J.A.L., and B.M.; software: J.L., E.B., and P.D.; validation: J.L. and E.B.; formal analysis, J.L., E.B., and P.D.; investigation: [PERSON], [PERSON], and [PERSON]; resources: [PERSON] and [PERSON]; data curation: [PERSON], and [PERSON]; writing, original draft preparation: [PERSON], [PERSON], and [PERSON]; writing, review and editing: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; visualization: [PERSON], [PERSON], and [PERSON]; supervision: [PERSON], [PERSON], and [PERSON]; project administration: [PERSON], [PERSON], and [PERSON]; funding acquisition: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
[PERSON] is a Research Fellow of the Belgian national scientific research foundation Fonds de la Recherche Scientifique (FNRS). [PERSON] is supported by the Walloon Region under Grant RW-DGO6-Biowin-Bidmed. [PERSON] is a member of the Prother-wal project funded by the Walloon Region (Belgium). [PERSON] and [PERSON] are Senior Research Associates with the Belgian F.R.S.-FNRS.  
We thank CHU-UCL-Namur ([PERSON]) and CHU-Charleroi ([PERSON]) for providing the data. The authors thank [PERSON] for her technical support in the data acquisition and annotation and to Dr. [PERSON] for the verification of the contours. Finally, we thank [PERSON] for editing the final revision of this paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CBCT & Cone beam computed tomography \\ CT & Computed tomography \\ CTV & Clinical target volume \\ DIR & Deformable image registration \\ DL & Deep learning \\ DSC & Dice similarity coefficient \\ DVF & Deformation vector field \\ EBRT & External beam radiation therapy \\ FCN & Fully convolutional neural network \\ GPU & Graphical processing unit \\ JI & Jaccard index \\ LoA & Limit of agreement \\ OAR & Organ at risk \\ ROI & Region of interest \\ SMBD & Symmetric mean boundary distance \\ \end{tabular}