esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, an application of a three-dimensional Convolutional Neural Network for flood mapping is proposed. The deactivation factor minimizes the overfitting problem to reduce the number of neurons and simplify the connections. The research results are that the 3D-CNN method enables the analysis of multi-temporal images for flood detection and classification instead of using multiple image pairs with multiple classification levels. For three combinations of splitting training/test data, the highest overall accuracy of 0.72 was achieved for a split of 90/10 and 150 epochs in 302 min. Regarding computation time, the best performance is achieved with an 80/20 split and 150 epochs with an accuracy of 0.71 in 283 min. Another test with epochs other than 150 showed that accuracy gradually decreases with a 90/10 split, but with a lower training function, the accuracy improves as the number of epochs increases.  
The research was led by [PERSON] and [PERSON]; [PERSON] provided satellite data and insights on satellite data processing; [PERSON] was responsible for developing methods and analysis. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Doctoral Program Research Indexed Publication Grant of Universitas Indonesia (PUTI Doktor UI) 2020 under Grant NKB-3321/UN2.RST/HKP.05.00/2020.  
Not applicable.  
The authors would like to thank [PERSON] and [PERSON] of the Faculty of Mathematics and Natural Sciences, Universitas Indonesia, for satellite data processing support.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 12: Result map compared to (**a**) 2 January 2020 SAR image, and (**b**) Jakarta Flood Map released by the government report.
###### Abstract
The above features constitute the basis for building supervised seabed classifications. All the pieces of the above dataset needed to be analyzed under a common scale, achieving stationary information in all extents of the area. This common scale was achieved through a segmentation process on the 100 kHz mosaic (being the only full seabed coverage dataset), which provided the means for an Object Based Image Analysis (OBIA). This way, regardless of the different data densities and non-stationarity of some data sources, all their features can be joined into common minute regions covering the full seafloor scene. This work is an effort to exploit the synergistic use of MBES and SSS as complementary sources of information for improved benthic habitat mapping with emphasis on Mediterranean seagrass habitats. By weighing their relative importance for habitat discrimination, we identify optimal mapping strategies, informing future surveys and extending interpretation of the huge amount of existing datasets. At the same time, a high-detail habitat map of a very important Mediterranean MPA, the National Marine Park of Zakynthos (NMPZ), is presented for the first time. The remainder of this paper is organized as follows: Section 2 describes the ecological and geological settings of the study area (NMPZ); Section 3 describes the data acquisition materials and methods and the detailed benthic habitat cartographic work derived by the expert interpretation of the available data. Sections 4-7 present the data processing methods, regarding the feature extraction and supervised seafloor classification approaches. Finally, Section 8 provides an in-depth analysis of the results along with a discussion for general guidelines and future perspectives.
# Real-Time Guitar Amplifier Emulation with Deep Learning +
## 2 WaveNet-Style Model
### Model Training and Selection  
The performance of the neural network depends mostly on the number of channels used in the convolutional layers, the activation function, and the dilation pattern. The choice of these hyperparameters also affects the computational load of the model, as shown in Section 2.3. A hyperparameter search was conducted to find a suitable trade-off between model performance and computational load. Three different dilation patterns were considered:  
\(d_{k}=\{1,2,4,\ldots,512\}\),  
\(d_{k}=\{1,2,4,\ldots,256,1,\ldots,256\}\), and  
\(d_{k}=\{1,2,4,\ldots,128,1,\ldots,128,1,\ldots,128\}\).  
These dilation patterns correspond to models with 10, 18, and 24 convolutional layers, respectively. The number of non-zero coefficients in the filters was set to \(M=3\), which means that, according to Equation (4), the 10, 18, and 24-layer networks have the receptive fields of \(N=2047,2045\), and 1530 samples, respectively. At the 44.1-kHz sample rate, these receptive fields correspond to approximately 46, 46, and 35 ms, respectively.  
For the convolutional layers, the performance of the following activation functions were compared: the hyperbolic tangent:  
\[z=\tanh(H*x), \tag{5}\]  
the rectified linear unit (ReLU):  
\[z=\max(0,H*x), \tag{6}\]  
and the gated activation, which was used in the original WaveNet [24]:  
\[z=\tanh(H_{f}*x)\odot\sigma(H_{g}*x), \tag{7}\]  
where \(\odot\) is the element-wise multiplication operation, \(\sigma(\cdot)\) is the logistic sigmoid function, \(H_{f}\) and \(H_{g}\) are the filter and gate convolution kernels, respectively. Finally, the softsign-gated activation, as used in [33], was evaluated:  
\[z=g(H_{f}*x)\odot g(H_{g}*x), \tag{8}\]where the hyperbolic tangent and the logistic sigmoid of the standard gated activation are both replaced by the softsign function:  
\[g(x)=\frac{x}{1+|x|}. \tag{9}\]  
The softsign nonlinearity can be computationally cheaper than the hyperbolic tangent and the logistic sigmoid functions, as shown in Section 2.3, while having a similar shape.  
With the gated activations, the convolutional layer used in the model can no longer be considered a Wiener model. Instead, it can be described as two parallel Wiener models, whose outputs are multiplied together to produce the layer output.
## 4 Comparative Analysis of the E-Government Acts in Korea and the United States
### ICT Human Resources (CIO-Based System)  
The US Federal Government CIO was officially introduced in August 1996, when the Information Technology Management Reform Act (ITMRA) came into effect. The ITMRA's mandatory enforcement of the CIO system in federal agencies in the United States was intended to establish clear responsibility for federal government information resource management activities, promote information sharing among federal agencies, and promote the visibility of information activities within agencies.  
Therefore, in the United States, the ITMRA in 1996, the Presidential Order 13011 on Federal Information Technology in 1996, and other laws have reduced government documents, efficiently managed information resources, organized information technology and organizational performance,and established the CIO system, which can be evaluated as a continuous effort to build e-government. As a follow-up to this, the E-Government Office was authorized by the E-Government Act to coordinate with the CIO Council as a federal CIO Council secretariat.  
In Korea, the CIO system was introduced in 1998 but currently is of little practical use. On 21 May 1998, the Ministry of Government Administration and Home Affairs informed the President of the necessity of introducing an information accountability system, and the President instructed the government to introduce the CIO system to the public sector. As a result, the Presidential Directive No. 73 was enacted in September 1998 to implement the CIO system. However, the CIO system was not related to the E-Government Act and is currently stipulated in Articles 11 and 12 of the Framework Act (see Appendix A).  
As such, the CIO system is currently malfunctioning in Korea. A major limitation of the CIO system is the lack of fully committed human resources; only 30% of council members work full-time and can fully commit to their duties and responsibilities, while the remaining 70% hold adjunct positions [25]. While the CIO council's mission is to coordinate among ministries and share information to implement ICT projects and serve as a ground for government officials to meet and work on informatization across ministries, it has failed to function properly and cannot coordinate among ministries regarding related businesses. As a result, all former administrations in Korea have established special committees for information technology and several governments have promoted digital government projects through the presidential ICT committee.
## 2 Background
### Ellipack-Itpack Format  
The Ellpack-Itpack [20] (ELL) format is an efficient storage format for vector processor architectures. It is well suited for storing coefficient matrices generated from structured grids, since the matrix rows have the same number of nonzero elements, except for the boundary points. The matrix generated by a \(4\times 4\) structured grid in the case of a 5-point discrete format is given in Figure 3. In the ELL format, two \(n\_row\times nnz\_row\) arrays store the elements and column indices in column order, where \(n\_row\) indicates the matrix row number, and \(nnz\_row\) indicates the maximum number of nonzero elements per row.  
Algorithms 1 and 2 describe the unoptimized SpMV and Gauss-Seidel algorithms using the ELL format to store matrices. The arrays \(K\), \(idx\), \(x\), \(ax\), \(F\), and \(diag\) store the matrix elements, column indices, the vector to be multiplied, the output of the matrix-vector multiplication, the right-hand term, and the matrix diagonal elements, respectively. It can be seen that SpMV has good data parallelism and can be simply developed for multicore vectorization. However, the Gauss-Seidel algorithm has a strong dependence on the update of \(x\). At each iteration through the outer loop, \(x[i+1]\) can only be calculated after \(x[i]\) is finished, and the outer loop of Algorithm 2 (Line 1) can only be computed serially.  
```
1:for\((i=0;i<n\_row;i++)\)do
2:for\((j=0;j<nnz\_row;j++)\)do
3:\(ax[i]+=K[i+j*n\_row]\cdot x[idx[i+j*n\_row]]\)
4:endfor
5:endfor
```  
**Algorithm 1** Unoptimized SpMV algorithm using ELL format to store matrices  
```
1:for\((i=0;i<n\_row;i++)\)do
2:for\((j=0;j<nnz\_row;j++)\)do
3:\(ax[i]+=K[i+j*n\_row]\cdot x[idx[i+j*n\_row]]\)
4:endfor
5:\(x[i]+=(F[i]-ax[i])/diag[i]\)
6:endfor
```  
**Algorithm 2** Unoptimized Gauss-Seidel algorithm using ELL format to store matrices
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### XAI Frameworks
#### 4.1.18 Integrated Gradients  
Integrated Gradients (IGs) is an XAI framework delving into individual predictions completed by complex models, particularly deep learning models. It sheds light on the model's decision-making process for specific instances by attributing the final prediction to each input feature value. This enables understanding how much each feature contributes to the model's output. IGs leverage the concept of gradients, which indicate how the model's output changes as a specific input feature changes. By calculating the gradients along a path from a baseline (no information) to the actual input, IGs estimate the contribution of each feature. This information can be visualized through heatmaps, where regions with higher attributions contribute more significantly to the prediction. Integrated Gradients offer a valuable approach to understanding the individual predictions in complex models. Notably, its versatility, intuitive visualizations, and interpretability contribute to the approach's popularity in the XAI field [50; 51].
# World-Models for Bitrate Streaming
## 4 Design  
It is straightforward to formulate bitrate streaming as a reinforcement learning problem. The state \(s\) is a vector \((x_{t},\tau_{t},n_{t},b_{t},c_{t},l_{t})\), where \(x_{t}\) is the throughput measurement, \(\tau_{t}\) is the download time of the past chunk, \(n_{t}\) is a vector of the \(m\) available sizes for the next chunk, \(b_{t}\) is the size of the buffer, \(c_{t}\) is the number of chunks that remain in the video, and \(l_{t}\) is the selected bitrate of the previous chunk. The action \(a\) is the bitrate for the next chunk, and the reward \(r\) per time step is the contribution to the QoE function for time step \(t\).  
Many reinforcement learning algorithms are evaluated on collections of environments, such as the Arcade Learning Environment [28], RLLab [29], and OpenAI Gym [30]. These collections have a common interface and allow for researchers to easily experiment with novel algorithms. Moreover, they allow researchers to easily reproduce and evaluate the results. Until recently, there was no easy-to-use platform for experimenting and benchmarking problems in the computer systems domain. Many applications of reinforcement learning in computer systems require difficult configurations and the collection of traces in production systems. Park [7] is an attempt to lower the barrier to entry to machine learning research in the systems domain. The Park platform consists of 12 systems-centric environments, including both custom-built simulators and interfaces to real system environments. Park includes a bitrate streaming simulator that is based on the work in the Pensieve project [1]. This project leverages the Park simulators for ease of development and training. Algorithms to learn hallucinogenic world-models must successfully work on these simulated systems environment before they can be applied to larger, more complex systems, such as an entire data center. Park environments use an interface that is similar to OpenAI Gym; agents provide a valid action to the environment and receive a tuple containing the next state, the reward, and a boolean value marking the end of the episode. Multiple instances of the same Park environment can easily run concurrently, generating experience in parallel to speed up training and evaluation. We ported the Park environment to OpenAI Gym for easier installation and modification.  
The default Park formulation uses the linear QoE, a linear combination of the bitrate and stall, for its quality function. The Park environment [7] supports a set of network traces, which contain a 3G/HSDPA mobile data set collected in Norway from commuters in transit [31], such as bus or train passengers. In order to avoid situations where bitrate selection is trivial (consistent high network throughput) or impossible (little to no service), the data set only contains traces where the average throughput is less than six Megabits per second (Mbps), and the minimum throughput is greater than 0.2 Mbps [1]. In addition, we have added a test set of extended traces from the Pensieve project to the Park environment.
## 3 System Model and Proposed Scheme
### The Proposed Interference Mitigation Scheme
#### 3.2.2 Almost Blank Sub-Frame  
The proposed scheme mitigates the inter-sector interference of a target DUE-Rx using the ABS method. In the proposed scheme, a DUE on receiving mode (DUE-Rx), which receives severe interference, is regarded as a victim node. On the other hand, a CUE in a neighboring sector, which causes the inter-sector interference, is treated as an aggressor. To address the inter-sector interference problem in the LTE-A network, 3 GPP Release 10 proposed the enhanced Inter-Cell Interference Coordination (eICIC) technique by the allocation of almost blank sub-frames (ABSs) [18]. ABS is a technique based on adaptive resource partitioning in the time domain as can be seen in Figure 5. In Figure 5, sub-frames 2, 3, 4, 6, 7, and 8 are ABS where the aggressor nodes do not send data signals to avoid interference to victim nodes. During ABS, the aggressors can transmit only control signals such as cell-specific reference signals (CRSs) [19; 20]. In Figure 5, sub-frames 0, 1, 5, and 9 are not blank, which are reserved to transmit primary synchronization signals (PSS), secondary synchronization signals (SSS), SIB-1, and paging.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area** & **Partition** & **Frequency** & **CUE** & **D2D** \\ \hline Cell-Center & \(S_{0}\) & \(F_{c}\) & \(F_{c}\) & \(F_{1}\), \(F_{2}\) and \(F_{3}\) \\ Cell-Edge & \(S_{1}\) & \(F_{1}\) & \(F_{1}\) & \(F_{c}\), \(F_{2}\) and \(F_{3}\) \\ Cell-Edge & \(S_{2}\) & \(F_{2}\) & \(F_{2}\) & \(F_{c}\), \(F_{1}\) and \(F_{3}\) \\ Cell-Edge & \(S_{3}\) & \(F_{3}\) & \(F_{3}\) & \(F_{c}\), \(F_{1}\) and \(F_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resource allocation for cellular user equipment (CUE) and device-to-device (D2D) pairs.  
Figure 4: Hybrid FFR-almost blank sub-frame (ABS) system.  
We propose a hybrid FFR-ABS scheme to mitigate the inter-sector interferences as well as inter-cell interferences. CUEs in cell-edge areas should have much higher transmission power to communicate with eNB located in the cell center. Thus, CUEs also cause interferences to DUE-Rxs in neighboring sectors. The proposed FFR-ABS scheme can prevent this problem and improve overall throughput.
###### Abstract
Also, the RHONNs have the following characteristics [11; 12; 14]:  
* They allow an efficient modeling o complex dynamic systems, even those with time-delays. * They are good candidates for identification, state estimation, and control. * Easy implementation. * A priori information of the system to be identified can be added to the RHONN model. * On-line or off-line training is possible. The goal of the inverse optimal control is to determine a control law which forces the system to satisfy some restrictions and at the same time to minimize a cost functional. The difference with the optimal control methodology is that the inverse optimal control avoids the need of solving the associated Hamilton-Jacobi-Bellman (HJB) equation which is not an easy task and it has not been solved for general nonlinear systems. Furthermore, for the inverse approach, a stabilizing feedback control law, based on a priori knowledge of a Control Lyapunov Function (CLF), is designed first and then it is established that this control law optimizes a cost functional [15]. The control scheme consisting of a neural identifier and an inverse optimal control technique is named neural inverse optimal control (NIOC), this control scheme has shown good results in the literature for trajectory tracking [15; 16; 17]. However, the designer has to tune the appropriate value of some parameters of the controller discuss later in this work, the quality of the controller depends directly on this selection. In this work, the main contribution is the introduction of an optimization process using GCO, in order to find the appropriate values for the controller parameters which minimize the tracking error of the system to be controlled. Performance of the optimization is shown presenting simulation and experimental tests comparing the results of the trajectory tracking using the NIOC with the parameters selected by the designer and the results using the parameters given by the GCO algorithm. This work is organized as follows: In Section 2 the Germinal Center Optimization algorithm is described. In Section 2.1 the vertebrates adaptive immune system is briefly explained, in Section 2.2 we detail the germinal center reaction and in Section 2.3 the computation analogy of the germinal center reaction is presented along with the algorithm description. Section 3 introduces the Neural Inverse Optimal Control (NIOC) scheme for this work, where Section 3.1 presents the RHONN identifier and the extended kalman filter (EKF) training, and in Section 3.1.2 the design of the inverse optimal control law is discussed. Section 4 unveils comparative simulations (Section 4.2) and experimental (Section 4.3) results between the selection of the parameter of the controller using the GCO algorithm and the classic way which is let completely to the designer for an application of the NIOC to an All-Terrain Tracked Robot. Conclusions of this work are included in Section 5.
## 2 Mobile Social Networking  
To begin our discussion of location-based services (LBS) for personal use, we turn to mobile social networking and the impact that contextual awareness might have on it. The phrase _mobile social networking_ (MSN) is a broad category inclusive of all mobilely accessible social networking services; we include both context-aware and traditional social networking services here. In general, _context-aware services_ are those that adapt according to environmental changes, including variations in physical location of use and changes in the end-user's intended application of use [1]. We consider _traditional services_ to be services which do not fit the definition or intent of contextual awareness. Examples of traditional MSN services are Facebook, Myspace, and Twitter [2; 3; 4]. Though these services offer mobile access (e.g. via a WAP gateway and modern cellular phone), and can correlate end-user access with time, such services are incapable of correlating user access with intended use or physical location. System knowledge of detailed context information enables a more robust end-user experience. For example, services can recognize an end-user's repetitive use patterns, using those patterns to improve that user's socialization with other users. Unfortunately, highly available context information also allows for complex and intrusive attacks (such as violating a user's location privacy). We focus on context-aware mobile social networking (CAMSN) services in the following section.  
Although service designers can apply context information to mobile social networking services in many ways, of the CAMSN services we have encountered, the primary goal has been to allow digital interaction between users concerned with the same physical location. For example, users might be able to create or edit content related to their shared physical location (e.g. reviews of the restaurant they are dining in). As another example, attendees at a social event might receive automatic notifications about persons in their vicinity with similar interests (the goal being to foster interaction between strangers).
## 3 Results and Discussion
### Following the A. niger Metabolites Biomarkers Pattern over 7 Days  
By Analysis of _A. niger_ Cultures Obtained from Contaminated Grapes  
Based on the previous model, an application study was carried out to detect _A. niger_ in contaminated grapes. The same growth strategies, analysis, and data treatment were employed, and the data obtained were tested to predict the contamination of the samples with this pathogen. Thus, after 7 days of growth, the spores of _A. niger_ in the grapes were transferred to petri dishes with YGC\({}_{\text{A}}\) medium. For this procedure, two grapes per plate were used and the experiments were performed in five repetitions for each grape variety. Figure 4 presents the complete data set, using the same previous model, including the samples from contaminated grapes.  
According to Figure 4, the set of data obtained with the contaminated grapes is similar to the previous data obtained to construct the model, following the PLS-DA graph. Therefore, the fungus can be detected after only one day of infection. The interest in detecting the presence of microorganisms has been widely studied [59; 60], with applications employed in the hospital area [61; 62], the environment [63], and mainly food [12; 64], including the fungus _A. niger_[65]. Nevertheless, the differential of this study lies in the fact that the detection of contamination by _A. niger_ can be performed in only one day involving all the steps described above, which is quite significant when compared to traditional methods dependent on the isolation and characterization of infectious agents; usually a very long and troublesome path. Although the protocol is not an identification that can completely replace the use of molecular tools, the strategy employed can assist in the early investigation due to detection or not of the microorganisms' presence before using other approaches for accurate identification.