esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Survey of Techniques for Updating
### Traffic Reduction
#### 3.2.5 Power Efficient Adaptive Network Coding (PEANC)  
By adding neighbour discovery to AdapCode [46], higher efficiencies are obtained, as the nodes have a more accurate estimate of the number of neighbours [47]. In the initial deployment, nodes exchange a selected number of beacons in order to evaluate the link quality to each neighbour. Prior to code dissemination, nodes broadcast a beacon so nodes may establish their neighbours. The dissemination is then performed in a manner similar to AdapCode, where, once a node has received enough packets to decode a page using Gaussian elimination, it then uses the current neighbour count to recode the page prior to broadcasting it. NACKs are used to ensure 100% reliability. The reported results indicate an approximate halving of energy used when compared to AdapCode.  
As for AdapCode, apart from the improved traffic reduction, it is an open question whether autonomous updates can benefit from the use of network coding to disseminate the update.
# Mutual Information, the Linear Prediction Model, and CELP Voice Codes
## 3 A Mutual Information Decomposition  
[PERSON] [5] proposed the following decomposition of the several contributions to the synthesized speech by the CELP codec components. In particular, letting \(X\) represent a frame of input speech samples, we define \(X_{R}\), \(X_{N}\), and \(X_{C}\) as the reconstructed speech, the prediction component, and the combined fixed and adaptive codebook components, respectively. Then we can write the mutual information between the input speech and the reconstructed speech as  
\[I(X;X_{R})=I(X;X_{N},X_{C})=I(X;X_{N})+I(X;X_{C}|X_{N}). \tag{2}\]  
This expression states that the mutual information between the original speech \(X\) and the reconstructed speech \(X_{R}\) equals the mutual information between \(X\) and \(X_{N}\), the \(N\)th order linear prediction of \(X\), plus the mutual information between \(X\) and the combined codebook excitations \(X_{C}\) conditioned on \(X_{N}\). Thus, to achieve or maintain a specified mutual information between the original speech and the reconstructed speech, any change in \(X_{N}\) must be offset by an adjustment of \(X_{C}\). This fits what is known experimentally and was alluded to earlier. If we define \(X_{A}\) to represent the adaptive codebook contribution and \(X_{F}\) to represent the fixed codebook contribution, we can further decompose \(I(X;X_{C}|X_{N})\) as  
\[I(X;X_{C}|X_{N})= I(X;X_{A},X_{F}|X_{N}) \tag{3}\] \[= I(X;X_{A}|X_{N})+I(X;X_{F}|X_{N},X_{A}),\]  
where we have used the chain rule for mutual information [16]. The expressions in Equations (2) and (3) correspond to the analysis  
Notice that we are not attempting to model the mutual informations in the CELP encoder and decoder of Figures 1 and 2 directly; we are effectively creating analysis and synthesis Markov chains that use the choices for \(X_{F}\), \(X_{A}\), \(X_{N}\) produced by the CELP encoder in the original analysis-by-synthesis CELP structure prior to the adoption of the adaptive codebook approximation [17].  
While these expressions in Equations (2)-(4) are interesting, the challenge that remains is to characterize each of these mutual informations without actually having to calculate them directly from data, which is a difficult problem in and of itself [18].  
An interesting quantity introduced and analyzed by [PERSON] in a series of papers is the log ratio of entropy powers [5; 8; 9]. Specifically, the log ratio of entropy powers is related to the difference in mutual information, and further, in many cases, the entropy powers can be replaced with the minimum mean squared prediction error (MMSPE) in the ratio. Using the MMSPE, the difference in mutual informations can be easily calculated. The following sections develop these concepts before we apply them to an analysis of the CELP structure.
## 3 System Model and Problem Formulation
### Dynamic Trust Score Modeling  
Each client \(c_{i}\) is assigned a trust score \(\tau_{i}^{t}\in[0,1]\) in round \(t\), representing the overall reliability of its recent behavior. The score is updated via the following exponential decay rule:  
\[\tau_{i}^{t}=\gamma\cdot\tau_{i}^{t-1}+(1-\gamma)\cdot\tau_{i}^{t}, \tag{1}\]  
where \(\gamma\in[0,1)\) is the memory decay coefficient, and \(\tau_{i}^{t}\) is the instantaneous score defined as a weighted sum of behavioral indicators where  
\[\tilde{\tau}_{i}^{t}=\lambda_{1}\phi_{i}^{t}+\lambda_{2}\psi_{i}^{t}+\lambda_{ 3}\omega_{i}^{t},\quad\sum_{j}\lambda_{j}=1. \tag{2}\]  
Specifically, \(\phi_{i}^{t}\) is the participation frequency, or the proportion of active rounds within the past \(T\) rounds; \(\psi_{i}^{t}\) is the gradient consistency, or the cosine similarity between the local update and the global average direction; and \(\omega_{i}^{t}\) is the contribution effectiveness, or the improvement of the validation error brought by the update.  
The trust score serves not only as a behavioral descriptor but also as a control variable in both aggregation and incentive allocation.
# Energy Efficient Data Transmission for Sensors with Wireless Charging
## 2 Motivation and Problem Statement  
### Intermittent Wireless Charging  
Previous works have shown that wireless electromagnetic energy transfer through induction, radio frequency waves, or resonant evanescent coupling results in thermal effects on tissues [8; 11; 12; 13; 14; 15; 16; 17]. The temperature of a wireless power receiver will rise as the energy is being transferred. It is stated that exposure to SAR (Specific Absorption Rate) of 8 W/kg for 15 min has shown to result in severe tissue damage [11]. Organs like lenses are especially sensitive to any temperature increase and prone to thermal damage because of a lack of blood flow to them [12], and even a slight thermal rise would cause damage. Thus, we must consider the thermal issues in human tissues during wireless charging. Four cooling mechanisms of radiation, conduction, convection and perspiration can cool human body, and the blood flow can also take heat from hot areas to cool areas so as to keep temperature balanced [18; 19]. In a word, the body can cool down itself and keep the temperature within certain boundaries automatically after a certain time.  
Therefore, we use a periodical wireless charging mechanism to eliminate the thermal influence. In the beginning of each period, the sensors receive wireless energy, thus the temperature increases as shown in the red curve in Figure 1. When the temperature increases to a certain degree, it should stop wireless charging to wait the temperature to reduce to a safe degree, as shown in the blue curve in Figure 1. Usually, the cooling process is much slower than that of the temperature increasing process; thus, the duration of the blue curve is usually much longer than that of the red curve.
## 3 Double Difference Observation Modelling  
### Double Difference Principle  
Combining carrier phase measurements from two stations or receivers \(A\) and \(B\) and two satellites \(i\) and \(k\), DD can be formed, which eliminates common error sources and largely reduces similar propagation specific effects:  
\[\Phi_{AB}^{ik} =\Phi_{A}^{i}-\Phi_{A}^{k}-\left(\Phi_{B}^{i}-\Phi_{B}^{k}\right) \tag{1}\] \[=\lambda N_{AB}^{ik}+e_{AB}^{ik} \tag{2}\]  
Figure 5: Flight dynamics recorded by the IMU along the whole trajectory. The black dashed line indicates different marked points, which are explained in Table 3. Marked point labels are only inserted in the last subplot but apply to all time series.  
where \(\Phi\) is the carrier phase measurement. In a perfectly synchronised zero-baseline configuration, the only remaining part in \(\Phi_{AB}^{ik}\), is the DD integer ambiguity \(N_{AB}^{ik}\) and a noise term \(\epsilon_{AB}^{ik}\) that contains the carrier phase noise and also remaining multipath effects if, for example, different types of receivers are used in the processing. Hence, DD is an efficient way of analysing the characteristics and the noise of the carrier phase observations.
## 6 Discussion  
The main goal of the work presented in this paper was the control system designed for the 2 DOF manipulator robot using a RHONN module in a hardware-in-loop setup. As such, we have presented a methodology for identification and tracking of the plant (2 DOF) to be  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device Utilization Summary** & **Neural Identifier (RHONN + EKF)** & **Control System (RHONN + EKF + SMC)** \\ \hline  & **Used** & **\%** & **Used** & **\%** \\ \hline Slices & 62,754 & 9 & 262,692 & 43 \\ FFs & 1983 & 13 & 3009 & 20 \\ BRAM & 0 & 0 & 0 & 0 \\ LUTs & 242,464 & 39 & 199,938 & 99 \\ LOB & 76 & 10 & 78 & 11 \\ Mults/DSP48s & 1818 & 64 & 2796 & 99 \\ TBUFs & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware resource utilization of logic circuits used in the hardware-in-loop architecture.  
Figure 8: Applied torque for (**a**) joint 1 and (**b**) joint 2.  
controlled. Once the identification and tracking results are reached, the present proposal can be expanded with a real 2 DOF implementation, where the mathematical model is unknown and only the state variables are available. The results are obtained without the necessity to know the complete mathematical model of the plant, or the parameters. Even when the results are obtained via a hardware-in-loop configuration, the proposed methodology constitutes an advance in a new control system architecture: the RHONN, modified EKF, and SMC implemented on an FPGA. This is due to the strategy proposed in this work, which guarantees there is no requirement for an exact mathematical model to be reproduced by an FPGA. Furthermore, the contribution of this proposal is in the area of programming a RHONN on an FPGA. The FPGA can compute and solve the problem with this architecture: a RHONN in discrete-time; the modified extended KF algorithm where the associated state and measurement noise covariance matrices are composed by the coupled variance between the plant states; and the decentralized discrete-time sliding mode controller combined with a block control algorithm. The results of the control system with the new combination of the mentioned architectures has been discussed in detail and the experiments show very satisfactory results.  
The results of the tracking control and identification process show the high performance of the system, which is a tangible evidence of the new design architecture implemented for discrete nonlinear systems.  
This control system architecture has never been solved with this methodology in this field and has not used the XSG, ignoring its potential in the design of complex systems. Using an FPGA and co-design, in tandem with a hardware-in-loop approach, any control can be implemented in the re-configurable hardware for quick simulation and production.
## 4 Origin-Destination Matrices Estimation
### Implemented Solution
#### 4.1.2 Configuration for the ITS in Montevideo  
The destination estimation algorithm processes sales data grouped in chunks corresponding to 24 h periods. Records are split at the time of the day when the lowest sales activity is observed, as recommended by [PERSON] et al. [15]. In the studied scenario, the lowest amount of sales occurs at 3:00.  
The destination estimation algorithm limits the search of a possible destination bus stop to a configurable radius. The search is sensitive to this parameter: large values may incorrectly identify destinations when other transport modes are used within the chain of bus trips, while a small radius might miss to identify destinations for trips that involve large walks from the bus stop to the destination. In the reviewed works of the related literature, several values were found for this parameter: 800 m [16], 1000 m [13; 14], and 2000 m [12]. In this work the maximum distance to search for a destination bus stop was set to 1000 m, which is the median of the values found in the related literature. Additionally, 1000 m is also the maximum distance used to classify a walk as \"short\" according to the urban mobility survey [23].
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 4 Case Study
### Performance Evaluation  
To test the efficiency of the proposed method, we implement the BuildingTree on the huge city model data supplied by citygml.org. This model contains 484 copies of the Ettenheim dataset with a LOD4 building. The compressed model is 3.8 GB and 44 GB uncompressed. Using our BuildingTree model, this huge city can be easily visualized with the X3 DOM-based web page. According to our implementation, the dataset can only be loaded in LOD1 and the BuildingTree model. Therefore, we perform a more detailed test on a smaller dataset.  
The test is carried out on a local network and the parameters are listed in Table 1. The original model (LOD3) could not be loaded into the smart phone due to the data volume which resulted in a system crash, so it is not listed in the table. The size of BuildingTree only contains the path to the data rather than the data itself of LOD3 ES or LOD3 models  
From Table 1, we can see that the size of the 3D models is reduced quite significantly with CSG representation since BuildingTree only contains the address rather than the data of LOD3 ES or LOD3 models. Also, the loading time is improved. However, the complete CSG representation (LOD2) does not greatly improve the visualization speed (frames per second) because the number of nodes is increased. We also can see that compared with the original models, it increases by less than 5% of  
Figure 15: Progressive visualization of BuildingTree. (**a**) Fly view; (**b**) Street level view.  
extra data by introducing these middle level LODS. For 3D rendering, it is indicated that the CSG representation does not increase much from LOD3 ES (16.7%). One of the reasons for this is the Frame per Second (FPS) is only determined by the number of primitives being drawn, rather than the file size of a model. Therefore, even though the data volume is reduced significantly, the FPS is not improved.  
We also test the proposed NoSQL database in a computer with Win7 x64, 8G RAM, 2.6 GHz CPU and MongoDB v2.2.7. In the test, one million records are generated and inserted into the database and query performance is evaluated. It takes about 53 seconds to insert the one million records. Meanwhile, it takes around 1700 ms to query in the one million records without index and 50 ms with index.
# AutoFL: Towards AutoML in a Federated Learning Context
###### Abstract  
Federated learning (FL) is a decentralized machine learning (ML) technique that learns from distributed data by moving the training process from a centralized server towards many clients rather than centralizing the client data, as is common with classical machine learning. The recent literature on federated learning often focuses on domain-specific use cases (e.g., IoT), investigates various privacy concerns (e.g., membership inference), or analyzes the impact of adversarial attacks (e.g., poisoning) and possible countermeasures. In these works, it is common for the server to have already chosen a specific machine-learning model and predefined hyperparameters prior to initiating the distributed training process. This decision is based on the server's ability to accomplish the task by either reusing well-established neural network architectures suitable for the specific task (e.g., ResNet-50 for image classification) or evaluating the adequacy of a model using the limited data it has access to. Additionally, the server may also assess publicly available datasets, which may or may not accurately represent real-world data distributions. In this paper, we address the challenge where this step--i.e., the ML model selection and hyperparameter optimization--is not possible in a centralized manner. In such a context, the data of a single client may not be sufficient or not representative enough to construct an ML model configuration that is effective for all clients. In real-world deployments, the data on the different clients may be imbalanced and heterogeneously distributed, and the performance impact of countermeasures is often unclear upfront. While various automated machine learning (AutoML) frameworks have been proposed for classical machine learning and deep learning in a centralized setting, we investigated the practical feasibility of AutoML in a federated learning context while taking into account the presence of security and privacy countermeasures. We implemented and validated our proof-of-concept framework, called AutoFL, on top of open-source libraries for machine learning, federated learning, and hyperparameter optimization, and have demonstrated the added value of our framework with public datasets in different scenarios.  
federated learning; AutoML; model selection; hyperparameter tuning; security; privacy +
Footnote †: journal: Applied Sciences
## 3 Datasets and Methodology
### Methodology
#### 3.2.1 TEC Data Rejection  
As we aimed to produce a reliable prediction of earthquake occurrence, we excluded all GPS ionospheric maps during days where there were solar disturbances and strong solar flare activity, due to the influence of extreme UV and X-ray radiation on the ionospheric F2 layer [6]. Moreover, we ensured that no strong CME events took place during the 48 h prior to all earthquake events used in our study (the CMEs that took place were defined as very poor events).  
By doing so, we were left with a model that predicted earthquakes based on ionospheric enhancements originating from non-solar and geomagnetic sources influencing the ionospheric TEC density.
## 1 Introduction
This possible simplification or reduction in the amount of information transmitted is never at the expense of a loss of semantic content;
* An enrichment of information results from a merger of processing information as it passes through the network;* A lower requirement of time and processing at the destination end nodes results because the information reaches its destination with a pre-processing level that is achieved by its transit through the network. Some of the features that a comprehensive network processing solution should include are the pre-processing, the data simplification, and the data enrichment functionalities. The pre-processing functionality is that information is being processed along the way and arrives at its destination with some degree of processing, instead of starting the process after it reaches the destination. Let's consider as an example an image processing application to reduce the size of an image [3]. A very simple method could be used to reduce the size of the image, consisting in dividing it in several pieces and send the pieces individually. The size reduction of the individual pieces may take place in the network. The functionality of simplification of information is that less information is transmitted each time as a result of a simplification process. The key point is not the processing of information to provide a result but instead is involving less information to send packets as they travel over the network. Consider for example the case of an image that is sent to a destination but in a simplified manner. This scenario does not send the entire image but instead sends only a few selected points of the image. Another example could be a character recognition application for the use of a transit department [3]. The idea is to recognize characters of car plates, starting from the image that is get by means of a video surveillance camera. The camera takes real time video and delivers it to the network (either as video or as an already selected image), which performs the whole information processing before delivering the final result to the destination node (in data format) that could search the corresponding proprietary name and address in the data base. Image is being simplified as it travels through the network, and the information that the application needs is extracted in the last phase. The information enrichment functionality consists of applying data fusion techniques for increasingly enrich information in terms of semantic content as it travels over the network. For instance, consider an Ambient Intelligence (AmI) medical application [4] and suppose that a patient wants to contact a doctor. He sends his request to the network, which provides an answer based on the information that was provided by one or more doctor-type nodes that were considered semantically related to that specific requirement.
## 6 Time Dispersion Parameters and Analysis
The RMS delay spread values for all of the frequencies vary between 9.2 and 29.2 ns, with mean values of 19.2, 19.3, and 20.3 ns at 19, 28, and 38 GHz, respectively. The MN-EX/RMS delay spread in this environment was more than 1, and all of the frequencies have the same value of 1.3. Based on these findings and the results in Figure 4a-c, it can be concluded that many of the strong paths arrived after the midpoint of the power delay profile and the early excess at less than 30 ns. Figure 5: _Cont._  
For the RMS delay spread results and spreading factor, it can be concluded that the time dispersion parameters are more symmetrical in the earliest MPCS compared with the latest MPCs. Figure 5(a)-c illustrates the effect of path loss on the RMS delay spread at 19, 28, and 38 GHz. From these figures, it can be observed that the linear relationship between the RMS delay spread and path loss is very low. Figure 5(a) shows that at 19 GHz, the highest path loss is 65.2 dB at an RMS delay spread of 23.2 ns. The lowest RMS delay spread is 12.9 ns at a path loss of 61.2 ns. The lower path loss is 58.7 dB at an RMS delay spread of 14.4 ns. Figure 5(b) shows that at 28 GHz, the lowest RMS delay spread is 9.2 ns at a path loss of 65.5 dB. The heighest path loss is 74.5 dB at an RMS delay spread of 22.4 ns. The maximum RMS delay spread is 29.2 ns at a path loss of 71.2 dB. Figure 5(c) shows that the maximum RMS delay spread is 24 ns at a path loss of 75.8 ns. The maximum path loss is 80.5 dB at an RMS delay spread of 17.9 ns. The minimum RMS delay spread is 8.2 ns at a path loss of 74.4 dB. It is worth noting that in some locations, more power is acquired with a low RMS delay spread, which is very useful for 5G communication systems regarding the high information rate, to avoid intersymbol interference (ISI). Figure 5: RMS delay spread (RMSDS) in the NLOS environment as a function of Tx–Rx separation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **MAX-EX** & **Mean of MN-EX** & **Mean of RMS Delay** & **MNEX/RMS** \\
**[GHz]** & **[ns]** & **[ns] (min, max)** & **Spread [ns] (min, max)** & **MNEX/RMS** \\ \hline
19 & 90 & 26.1 (8.1, 42.7) & 19.2 (13.0, 23.3) & 1.3 \\
28 & 83 & 25.8 (4.2, 57.0) & 19.3 (9.2, 29.2) & 1.3 \\
38 & 64 & 27.4(3.7, 27.4) & 20.3 (8.2, 23.4) & 1.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maximum excess delay; mean, minimum (min), and maximum (max) RMS delay spread; MN-EX delay; and mean/delay spread (MN/RMS) for the measured frequencies. Figure 6: The spread of RMS delay as a function of path loss.