esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Engineering Example
### Data Preprocessing and Model Settings
#### 4.2.3 Time Dimension Downsampling  
Due to the low amplitude of daily fluctuations in the corrosion rate, for the convenience of prediction study, the method of calculating daily averages was used to downsample  
Figure 7: Data collection and transmission process.  
the sample data, changing the sampling frequency from 1 h to 1 day, and constructing a dataset using the average corrosion rate for each day, resulting in a total of 1000 sets of daily corrosion rate data, with the same processing applied to other corrosion indicators.  
After the above preprocessing, the corrosion rate curves of the 6 corrosion monitoring points inside the pipeline in 6 months (to ensure visual clarity and to highlight the spatial differences between the monitoring points) are shown in Figure 8. As can be seen from Figure 8, the corrosion rates of different monitoring points have large differences, which reflects the influence of spatial location on the corrosion behavior. Therefore, consideration of the spatial characteristics of pipeline corrosion can help better mine the corrosion state information.
# Fabrication and Testing of Thermoelectric CMOS-MEMS Microgenerators with CNCs Film
###### Abstract  
Manufacturing and testing of a TMG (thermoelectric microgenerator) with CNCs (carbon nanocapsules) film fabricated utilizing a CMOS (complementary metal oxide semiconductor) technology are investigated. The microgenerator includes a CNCs layer, thermopiles, and thermometers. CNCs, a heat absorbing material, are coated on the microgenerator, so that the TD (temperature difference) of HP (hot part) and CP (cold part) in the thermopiles increases, resulting in an enhancement of the microgenerator OP (output power). Thermometers fabricated in the microgenerator are employed to detect the HP and CP temperature in thermopiles. In order to enhance thermopiles' TD, the HP in thermopiles was manufactured as suspension structures isolating heat dissipation, and the CP in thermopiles was made on a silicon substrate to increase the heat sink. Experiments showed that the microgenerator OV (output voltage) was 3.3 mV and its output power was 125 pW at TD 3 K. Voltage and power factors of TMG were 0.71 mV/K/mm\({}^{2}\) and 9.04 pW/K\({}^{2}\)/mm\({}^{2}\), respectively.  
thermoelectric; microgenerator; CMOS-MEMS; carbon nanocapsules +
Footnote †: journal: Applied Physics Letters
## 3 Circuit Level Perspectives of JCRS RF Hardware
### Phased Arrays and Beam Steering Systems
#### 3.3.2 Beamformer Networks
A fully digital beamformer at 140 GHz has been presented in [80] using 128 element phased array. Since digital beamforming is not always practical due to its high power consumption, analog beamforming can be attractive above 100 GHz due to the reduced circuit size. However, BFs above 100 GHz become challenging due to phase shifters' higher loss and noise figures. BF requires RF blocks with controlled phase variations, which also become difficult due to parasitics at this frequency. One potential solution can be high IF BF using IF frequencies between 10-20 GHz, as reported in [81].
## 2 Architecture of the CAPT Framework  
The overall framework of the desired French phoneme utterance detectors is shown in Figure 1. Users utter the phoneme to learn it and record it as the input of the system. According to Figure 1a, the normalized frequency spectrum of the utterance waveform \(\mathbf{x}\) is assigned to the detector as the training or testing predictor vector. Figure 1b zooms into the architecture of the detector unit, which is implemented as an Intellectual Property (IP) core in this paper. This architecture is a 2-layer network whose output \(y\) can be mathematically described as  
\[\mathbf{y}=\delta^{(2)}(h^{(2)}(\mathbf{\delta}^{(1)}(h^{(1)}(\mathbf{x}^{(1)})))) \tag{1}\]where \(\delta^{(1)}\) and \(\delta^{(2)}\) are two activation function sets. \(h^{(1)}\) and \(h^{(2)}\) are the propagation functions of the first and second layers expressed as  
\[h^{(1)}(\mathbf{x}^{(1)})=\mathbf{x}^{(1)}\times\mathbf{W}^{(1)} \tag{2}\]  
and  
\[h^{(2)}(\mathbf{x}^{(2)})=\mathbf{x}^{(2)}\times\mathbf{W}^{(2)}+b \tag{3}\]  
\(\mathbf{x}^{(1)}=<x_{11},x_{21},\ldots,x_{m1}>\) (\(m\) is the vector size and set as 16,384 in this paper) is the input of the detector to which the predictor vector \(\mathbf{x}\) is assigned directly. \(\mathbf{W}^{(1)}\) and \(\mathbf{W}^{(2)}\) are the coefficient matrices of the two layers, respectively. Their sizes are \(m\)-by-\(n\) and \(n\)-by-1, where \(n=35\) is the phoneme number of the French language. \(b\) is the bias value of the second layer. \(\mathbf{x}^{(2)}\) is the output of the first activation function set \(\mathbf{\delta}^{(1)}\), whose element functions are rectified linear units (ReLU). For the second layer, the sigmoid function is used to its output as the activation function in order to constrain \(y\) into a reasonable range from 0 to 1.  
The decision of the system is made by comparing the output of the detector \(y\), which is the diagnosis score corresponding to the utterance quality, with a threshold \(\eta\) to feedback the diagnosis result. This work trains the detectors through a heterogeneous process presented in [27]. It consists of partial least square (PLS) regression and soft-margin support vector machines.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 5 Experimental Results  
### Impact of Non-IID Data on FedAvg Performance  
In this section, we analyze the impact of non-IID data distribution on the performance of the FedAvg algorithm across three datasets: MNIST, Fashion MNIST, and CIFAR-10. For the experiments, we randomly selected 20 clients for each round of computation. Each client locally updates its model five times per round, with the overall training lasting for 200 rounds. The batch size was set to 10 for the MNIST and Fashion MNIST datasets, and 20 for the CIFAR-10 dataset.  
To evaluate federated optimization, it is crucial to determine how data are distributed among clients. We examine two data distribution methods: IID and non-IID, as described earlier. The results from these two distribution methods are compared, and we focus on the optimization of federated learning with non-IID data, which closely simulates real-world scenarios in federated learning systems. Given that users' device usage varies, the samples on each device are not uniformly distributed, leading to a significant degradation in the performance of federated learning, particularly in terms of model accuracy and the number of communication rounds required for convergence.  
We begin by presenting the results of federated learning with a fixed learning rate for both IID and non-IID data distributions.
## 4 Experiments
### Experimental Setup
#### 4.1.2 Evaluation Metrics  
Following the work of [PERSON] et al. [7], we evaluated our proposed model using several retrieval metrics: (1) Mean rank of the ground truth response (Mean), (2) recall at \(k\) (\(k=\{1,5,10\}\)), which is denoted as R@k and evaluates where the ground truth is positioned in the sorted list, and (3) MRR [30]. NDCG was also introduced as a primary metric in the VisDial v1.0 dataset, and decreases when the model gives a low ranking to candidate answers with high relevance scores. MRR evaluates the precision of the model by ranking where a ground truth answer is positioned, whereas NDCG evaluates relative relevance of the predicted answers. In this work, we compare our model with the baselines mainly based on both NDCG and MRR metrics following the most recent visual dialog challenge (the VisDial challenge (2018\(\sim\)2019) selects the winners based solely on NDCG metric, but the winners are picked based on the average rank of NDCG and MRR from the 2020 challenge). Specifically, we rank all the baselines including ours on each metric individually (i.e., NDCG and MRR), then average ranks across two metrics (denoted as AVG in the following results).  
Figure 3: Performance of Multi-View Attention Network (MVAN) according to the ratio of relevance loss and discriminative loss on the VisDial v1.0 validation set.
## 4 Analysis  
### Existing Superannuation Payment Process  
When an employee is hired by a company, the employee provides details to the employer such as their name, date of birth, contact number, email, tax file number and super fund details. Then, the employer sets the employee up in the payroll software with the provided details. When the employer processes weekly or fortnightly wages and pays salary to the employee with a payment slip, the accounting software updates the ledger for payroll clearing, PAYG withholding, and superannuation payables. Additionally, the employer processes single-touch payroll (STP), which provides information on the wages, PAYG withholding and superannuation to the ATO. After this process, the employer pays superannuation to the employee's super fund monthly or quarterly, prior to the due date. This payment process can be carried out in different ways. Small businesses (Figure 3) use the ATO Small Business Super Clearing House (SBSCH) or pay the employees' super funds directly. When using SBSCH, the employer must provide all employee details to register the employee with SBSCH, which will remain for further payments. Then, the employer has to update their super payment instructions with the payment period and the super amount payable for each employee. The payment instructions can be lodged followed by the payment, using the ATO SBSCH payment instructions. The ATO transfers the money to the employee's super fund. Figure 3, presented below, outlines the superannuation payment process used by small and medium-sized enterprises.  
With large businesses (Figure 4), the process is similar to that of small businesses up until the point of the superannuation payments. At the stage of the super payments, they have the option of paying the employee's fund directly or using an external online superannuation payment service provider that uses a similar process to that of SBSCH. When these payments are carried out, the employer updates the superannuation payable ledger as liability paid.  
Figure 3: Existing superannuation process model of a small business.
## 3 Materials and Methods
### Methodology
vegetation index max (NDVI max and RVI max) and vegetation index min (NDVI min and RVI min) represent the maximum and minimum histogram value of the vegetation image. It is evident, therefore, that the selection of vegetated and non-vegetated regions is of great importance for the correct estimation of the proportion of vegetation cover. For this reason, the crowdsourced OpenStreetMap was used as a training area for the equations mentioned above, once these vector polygons have been confirmed from the Google Earth high-resolution products. Once these sites have been selected, the results from the optical and radar sensors using Equations (3)-(6) were retrieved. A comparison between the optical and the radar sensors separately (namely the results from Equation (3) against the results from Equation (4), and the results from Equation (5) with the results from Equation (6)) was performed.
# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 2 Background
### Related Work
Specifically, a novel representation-based classifier with two discriminative constraint terms and affine non-negative constraints was introduced to reduce interclass correlation and enhance class-specific parameter learning. Episodic training was the option for this study and, as for the experiments, RESISC-45 and RSD46-WHU [53] were the sets of images. The same types of few-shot tasks were considered, as in the previous studies, and no sign of statistical tests appear in the article. Other recent FSL approaches published in the literature for scene classification include an Iterative Distribution Learning Network (IDLN) [34], Manifold Augmentation-based Self-supervised Contrastive Learning under the meta-learning framework [35], a framework based on Metric Learning and Local Descriptors (MLLD) [36], a Multi-Scale Interaction Prototypical Network [37], and a Data Augmentation Technique based on Distortion Magnitude Optimisation [38]. The RS datasets used are some of those previously mentioned in this section, and the same observations which have already been stated hold here. To reiterate the points made in Section 1, despite these interesting solutions, the following differences exist between our study and theirs: (1) these approaches introduce new FSL techniques and compare them to various strategies. However, they do not focus on determining whether inductive or transductive techniques are generally superior; (2) the number of interactions (epochs/episodes) during training is not varied as is proposed in this study, aiming to observe how this relates to the number of unseen classes during the inference phase; (3) in all these articles, the number of unseen classes during inference is fixed at five (\(K=5\)). Few-shot tasks are one of these two types: 5-way 1-shot or 5-way 5-shot; (4) no statistical tests were applied; and (5) none of the studies have explored the impact of similarity between unseen classes during inference and some training set classes on model performance.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 3 System Model  
As shown in Figure 1, we consider an HFL system, including one cloud server, edge servers, and clients. The cloud server is responsible for selecting participating clients for training and managing the global model parameters of the entire FL system. The edge servers receive updates of local model parameters from the clients, instead of data samples, thus reducing communication overhead. The clients receive the global model from the cloud server and update the global model based on their own data samples. Moreover, these clients are mobile and thus are associated with different edge servers at different time intervals. The sets of clients and edge servers are denoted using \(\mathcal{I}=\{i:i=1,2,\ldots,I\}\) and \(\mathcal{J}=\{j:j=1,2,\ldots,J\}\), respectively. The cloud server is denoted using \(C\). Each client \(i\) has an independent local dataset \(D_{i}=\{(x_{m},y_{m})\}_{m=1}^{|D_{i}|}\), where \(x_{m}\) represents the \(m\)th input sample, \(y_{m}\) represents the corresponding label, and \(|D_{i}|\) is the size of the dataset on client \(i\). In the following, we detail the training process and then formulate an optimization problem to improve the training performance.
## 3 Experiments and Results
### Comparison and Compensation Experiments  
The proposed system is compared with commercial instruments for testing. The straightness measurement is compared with the interferometric laser encoder (Renishaw RLE, accuracy of \(\pm 1\) ppm). The pitch and yaw were compared with an industrial theodolite (Leica TM5100A, accuracy of \(\pm 0.5\) arcsec). The roll was compared with a photoelectric autocollimator (Taylor Hobson DA400, accuracy of \(\pm 0.2\) arcsec). The effect of angular drift on the system was analyzed.  
The comparison experiments are shown in Figure 11. When the moving stage is 500 mm-away from the light source, the comparison deviations of pitch and yaw are \(\pm 2.5^{\prime\prime}\) and \(\pm 3.5^{\prime\prime}\) without correction, respectively. After the beam drift correction, the comparison deviations of the pitch and the yaw are \(\pm 1.9^{\prime\prime}\) and \(\pm 2.8^{\prime\prime}\), respectively. The comparison deviations of the horizontal straightness and vertical straightness changed from \(\pm 4.8\) um to \(\pm 2.8\) um and \(\pm 5.9\) um to \(\pm 3.6\) um, respectively. The comparison deviation of the roll is \(\pm 4.3^{\prime\prime}\). In addition, we repeated the experiments with the moving stage at a distance of 1 m from the light source to verify the validity of the experimental results. The comparison deviations of the measurements are \(\pm 2.0^{\prime\prime}\) for pitch, \(\pm 2.3^{\prime\prime}\) for yaw, \(\pm 4.5^{\prime\prime}\) for roll, \(\pm 2.8\) um for horizontal straightness, and \(\pm 3.1\) um for vertical straightness. From the experimental results, it can be seen that the common light path compensation system can effectively compensate for beam drift.  
Figure 10: Results of the stability experiments.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 5 Sillcom\({}_{3R}\): Basic Design with Three Online Rounds  
### Overview of Sillcom\({}_{3R}\)  
We first give a high-level overview of Sillcom\({}_{3R}\). Indoor localization can be decomposed into three submodules: distance computation, top-\(k\) ranking, and top-\(k\) result selection. Research on these submodules also focuses on optimizing communication and computation overhead while ensuring efficient end-to-end performance. For distance computation, we employ RSS to ensure that communication overhead does not increase with the number of APs \(n\), while maintaining lightweight computation. The top-\(k\) ranking and selection stages involve secure comparison and selection operations, which are inherently nonlinear and often constitute a performance bottleneck in secure computation. To address this performance issue, we transition the three computing servers into an asymmetric mode and utilize FSS to securely compute these nonlinear components. This results in our Sillcom, which requires only three rounds of online communication. In the following, we introduce those blocks separately.
## 4 Results and Discussion
### Spatiotemporal Data Correlation  
Since spatiotemporal correlations exist among sensor readings, thus correlation is fundamentally about understanding the direction and strength of the relationship between sensed data variables. Although correlation can be used with hypothesis testing [63], it does a number of other useful roles in this section, as follows:  
* Reliability: A strong correlation between link features on a test means that they are consistent with measuring the same behavioral outcome. If all the link features on the test correlate well, then it is a reliable test.
* Validity: When developing a brand new test of intelligence, one could empirically want to demonstrate that the test correlates strongly with an existing measure of intelligence, measuring the same construct as the first intelligence test. A strong correlation between a new test and \"Gold Standard\" test, such as link quality estimations and their multi-category classification, means they are measuring the same construct and therefore the test is valid.
* Prediction: Experimentally, if link features are strongly correlated such as a drop in packet transmission count and increase in PRR, the next time PRR begins to drop, one can predict that link quality will deteriorate soon. When two data variables are correlated, when one changes, it can be _predicted_ what happens with the other.
* Verification: It is for theory verification or testing a theory, if a link feature is causing changes in another link feature, then they are strongly correlated. If this theory is correct, then an outcome \(x\) should happen. If the link feature turns out to be uncorrelated or are correlated in the wrong direction, then it is concluded that the link feature is certainly not causing changes in others, thus it is noted that the theory is not correct.  
Data correlation is examined in our study between three link features, PRR, retransmission count and CPDF (measured in percentage) from the packet traces captured from the 802.15.4 testbed.  
Figure 8: KLSVM Model: A visual intuition of the network behavior.  
As earlier seen in Figure 5, the change in one link's feature, reacted by an equivalent unit change in the other link's feature directly or indirectly. In other scenarios, variables are said to be uncorrelated in amount when one data variable does not show any movement in another data variable in a specific direction.  
Figure 9 shows data correlation can be positive or negative with the two link features moving in the same direction. An increase in one link feature results to a corresponding increase in the other link feature and vice versa. Then, the link features are considered to be positively correlated. On the contrary, when the two link features move in the different direction in such a way that an increase in one link feature will result in the decrease in the other link feature and vice versa, it is empirical said to be negatively correlated.
# The Effectiveness of Frenotomy on Speech in Adults
## 3 Results
The comparison on pronunciation before and after frenotomy showed a difference in articulation of dental phonemes, especially those which require a close contact with the hard palate /sz/, /z/, /cz/, /dz/, /t/, /d/, /n/. The most significant changes happened to patients who were originally diagnosed with severe and moderate classes of TT, according to [PERSON] [2]. They changed in such a pattern: severe-moderate, moderate-mild. As the changes occur, the pronunciation of the phonemes mentioned above improved significantly. Table 5 presents the obtained results for all nineteen phonemes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phoneme Tested** & **Before Frenotomy** & **After Frenotomy** & **Significance** \\ \hline /s/ & 4.28 \(\pm\) 0.91 & 4.75 \(\pm\) 0.63 & \(p\) \textless{} 0.01 \\ /c/ & 4.25 \(\pm\) 0.95 & 4.78 \(\pm\) 0.48 & \(p\) \textless{} 0.01 \\ /z/ & 4.1
# Fast Flow Reconstruction via Robust Invertible \(n\times n\) Convolution
## 2 Related Work
Emerging Convolution is obtained by chaining specific invertible autoregressive convolutions [30] and speeding up this layer through the use of an accelerated parallel inversion module \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Description** & **Function** & **Reverse Function** & **Log-Determinant** \\ \hline ActNorm [29] & \(\mathbf{y}=\mathbf{x}\odot\gamma+\beta\) & \(\mathbf{x}=(\mathbf{y}-\beta)/\gamma\) & \(\sum\log|\gamma|\) \\ \hline Affine Coupling [32] & \(\mathbf{x}=[\mathbf{x}_{a},\mathbf{x}_{b}]\) & \(\mathbf{y}=[\mathbf{y}_{a},\mathbf{y}_{b}]\) & \(\sum\log|s(\mathbf{x}_{b})|\) \\  & \(\mathbf{y}_{a}=\mathbf{x}_{a}\odot s(\mathbf{x}_{b})+t(\mathbf{x}_{b})\) & \(\mathbf{x}_{a}=[\mathbf{y}_{a}-t(\mathbf{y}_{b})]/s(\mathbf{y}_{b})\) & \\  & \(\mathbf{y}=[\mathbf{y}_{a}\mathbf{x}_{b}]\) & \(\mathbf{x}=[\mathbf{x}_{a}\mathbf{y}_{b}]\) & \\ \hline \(1\times 1\) conv [29] & \(\mathbf{y}_{:,ij}=\mathbf{W}\mathbf{x}_{:,i,j}\) & \(\mathbf{x}_{:,ij}=\mathbf{W}^{-1}\mathbf{y}_{:,i,j}\) & \(h.w.\log|\det\mathbf{W}|\) \\ \hline
**Our Shift Function** & \(\mathbf{y}_{c,ij}=\alpha_{c}\mathbf{x}_{c,ij}+\beta_{c}\) & \(\mathbf{x}_{c,ij}=[\mathbf{y}_{c,ij}-\beta_{c}]/\alpha_{c}\) & \(h.w.\sum_{c}\log|\alpha_{c}|\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative invertible functions in several generative normalizing flows. implemented in Cython. Invertible Periodic Convolutions transform data to the frequency domain via Fourier transform; this alternative convolution has a tractable Jacobian determinant and inverse. However, these invertible \(n\times n\) convolutions require more parameters; therefore, these have an additional computational cost compared to our proposed method. **Lipschitz Constant:** [PERSON] et al. [37] developed a theory that any residual blocks satisfying the Lipschitz Constant can be invertible. Hence, [PERSON] et al. proposed an invertible residual network (i-ResNet) as a normalizing flow-based model. Similar to [29; 31; 32; 35], i-ResNet is learned by optimizing the negative log-likelihood in which the inverse flow and Jacobian determinant of the residual block can be efficiently approximated by the stochastic methods. Inheriting the success of [PERSON] theory, [PERSON] et al. [38] proposed an \(L_{2}\) self-attention that allows the self-attention of the Transformer networks [44] to be invertible.