esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Methods
### Taguchi Method and ANOVA  
The Taguchi method, which was proposed by Dr. [PERSON] of Japan, introduces the orthogonal table into experimental designs. It spread quickly around the world due to multiple advantages, including a high reproducibility of experimental results, a scalability of configuration experiments, a lower number of tests, simple operation, and easy configuration. Using this method, the optimal parameter combination under corresponding horizontal conditions can be realized through some experiments. In this study, the heating depth, current, and frequency were selected as parameters to carry out simulation experiments based on the L9 orthogonal table involving three factors and three levels that was designed by Taguchi method. Table 3 presents the levels of three factors. The specific experimental design is shown in Table 4. Finally, the experimental results were first analyzed based on the signal-to-noise (S/N) ratio and then divided by optimization objectives into three characteristics: nominal-the-best, larger-the-better, and smaller-the-better [20], which correspond to Equation (10), Equation (11), and Equation (12), respectively:  
\[\frac{Signal}{Noise}=-10 log_{10}\left(\frac{\mu^{2}(n-1)}{\sum\limits_{i=1}^{ n}\left(y_{i}-\mu\right)^{2}}\right) \tag{10}\]  
\[\frac{Signal}{Noise}=-10 log_{10}\frac{1}{n}\sum\limits_{i=1}^{n}\frac{1}{y_{i }^{2}} \tag{11}\]  
\[\frac{Signal}{Noise}=-10 log_{10}\frac{1}{n}\sum\limits_{i=1}^{n}y_{i}^{2} \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Channel Depth/mm** & **Current/A** & **Frequency/KHz** \\ \hline A1 & 5 & 1000 & 30 \\ A2 & 7 & 1000 & 30 \\ A3 & 9 & 1000 & 30 \\ B1 & 9 & 800 & 30 \\ B2 & 9 & 1200 & 30 \\ C1 & 9 & 1000 & 20 \\ C2 & 9 & 1000 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation experiment scheme of induction heating.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \(\mu_{r}\) (\(\cdot\)) & \(\sigma\) (S/m) & \(\rho\) (kg/m\({}^{3}\)) & K (W/(m\(\cdot\)K)) & \(C_{p}\) (J/kg\(\cdot\)K) \\ \hline Air & 1 & 1 & 1.18 & 0.025 & 1005 \\ Copper & 1 & \(5.9\times 10^{7}\) & 8960 & 400 & 385 \\ H11(1.2343) & 55 & \(10^{7}\) & 7700 & 45 & 460 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters.  
In the above equations, \(n\) represents the number of experiments in each group; \(y_{i}\) refers to the results of a single experiment; and \(\mu\) is the sample mean. It is necessary in this experiment that the higher the mean temperature after heating, the better, which is classified into the larger-the-better characteristic. The range of temperature at five points, denoted by \(s\), is calculated with Equation (13) and is used to characterize the temperature uniformity:  
\[s=y_{\text{max}}-y_{\text{min}} \tag{13}\]  
where \(s\) represents the range of temperature at measuring points; \(y_{\text{max}}\) and \(y_{\text{min}}\) are maximum and minimum temperatures of these five points, respectively. Then, the extent to which each factor affects the experimental results is determined through the analysis of variance (ANOVA).
## 5 Analysis of the VIV Model Test Data of Flexible Riser with Staggered Buoyancy Elements
### Optimal Excitation Parameters  
A flexible riser with 2/2 configuration of bare and buoyancy elements at 1.0 m/s flow speed is used to derive the optimal dataset of excitation parameters. In the present analysis, simulations are carried out using two different datasets of excitation parameters i.e., one for the bare parts and one for the buoyancy sections of the riser, to obtain an optimal set of excitation parameters. The predicted responses from VIVANA are compared with the measured results.  
By considering the ranges of \(\hat{f}_{CF}\), \(\gamma_{A/D}\) and \(\gamma_{Ce}\), 18650 simulations have been performed. The current direction is perpendicular to the riser model; the added mass coefficient is taken as 1.0; and the effect of the Reynolds number effect is not considered in the present analysis.  
The optimized excitation coefficient parameter sets corresponding to the bare and the buoyancy sections are presented in Tables 9 and 10, respectively. The corresponding contour curves are presented in Figure 11. The predicted RMS A/D, with an optimized set of parameters is presented in Figure 12.  
Figure 11: The optimal excitation parameter set for 2/2 configuration at 1.0 m/s flow speed for (a) bare part of the riser; (b) buoyancy section of the riser.  
Figure 12: Predicted CF RMS A/D with the obtained optimal parameter set for the 2/2 configuration at U = 1.0 m/s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Comparison**} & **Mode and f\({}_{\text{R}}\)** & **Mode and f\({}_{\text{B}}\)** & **Max. RMS** & **Max. RMS** & **Max. RMS** \\  & **(Hz)** & **(Hz)** & **(A/D)\({}_{\text{R}}\)** & **(A/D)\({}_{\text{B}}\)** & **Curvature(I/m)** \\ \hline Measurement & -11 and 5.15 & 3 and 1.82 & 0.200 & 0.71 & 0.0095 \\ Default parameters & 14 and 5.800 & 5 and 2.100 & 0.250 & 0.35 & 0.016 \\ Optimal parameters & 11 and 5.15 & 3 and 2.00 & 0.201 & 0.72 & 0.0086 \\ \hline \hline \end{tabular} F\({}_{\text{R}}\) and F\({}_{\text{B}}\) are Response frequencies for bare and buoyancy parts, respectively. Max. RMS A/D\({}_{\text{R}}\) and Max. RMS A/D\({}_{\text{B}}\) are Maximum Root Mean Square Amplitude ratios for bare riser buoyancy.  
\end{table}
Table 11: Comparison of the prediction and measurements for the 2/2 configuration at U = 1.0 m/s.
## 4 Experiments
### Results  
This section describes the results when performing the three proposed missions using Gazebo simulator and the RB1 robot platform manufactured by Robotnik [31]. We have illustrated (Figure 9) an example of Mission 2 experiment showing the paths followed by the RB1 robot using Naive and MERLIN.  
Figure 8: Gazebo zenith view and map deployed for the experimental validation.  
Table 1 presents the results of Missions 1 and 2 after running it five times with different lists. It presents the robot performance when 6 or 20 points are established during the regular operating mode of the task. These values overview the significant difference between approaches. Graphically, Figure 10 shows the time difference when facing Mission 1 and Mission 2 given the tasks associated, notwithstanding, it is also clear the time needed for solving these missions is considerably less. The same behavior is also presented when measuring the traversal distance, the values depicted in Figure 11 also present better results when the robot deploys MERLIN architecture. When compared, MERLIN shows fewer fingerprints during robot traversal at home as seen in Figure 9.  
Besides, the low differences between median and average time (arithmetic means) plus the evaluation of the coefficient of variation, which defines the ratio of dispersion of frequency distribution and presents values under 1, outlines that, at this stage, we can trust in the repeatability of our experiments.  
Finally, we perform an extensive experiment aiming to evaluate the robot performance with longer missions. This experiment runs around 120 Pols, again canceling half of the requests. This scenario presents a duration of 2154 s when using MERLIN versus the 3379 s needed when the naive approach is presented. There is a difference in traversed distance of 369 m (MERLIN, 475 and Naive, 844) when using MERLIN.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{c}{**Mission 1**} & \multicolumn{2}{c}{**Mission 2**} \\ \hline
**Approach** & **Avg Time [Median]** & **Trav. Distance** & **Avg Time (Seconds)** & **Trav. Distance** \\  & **\(\pm\) SD (Seconds)** & **\(\pm\) SD (Meters)** & **\(\pm\) SD (Seconds)** & **\(\pm\) SD (Meters)** \\ \hline Naive & 178.20 [178.19] \(\pm\) 6.03 & 46.65 [46.92] \(\pm\) 2.05 & 556.82 [557.54] \(\pm\) 9.89 & 138.82 [139.20] \(\pm\) 4.94 \\ \hline MERLIN & 104.46 [110.66] \(\pm\) 12.30 & 21.72 [23.64] \(\pm\) 3.94 & 369.14 [373.31] \(\pm\) 18.06 & 80.23 [78.50] \(\pm\) 6.26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results for each mission.  
Figure 10: Time needed for fulfilling the missions using Naive and MERLIN approaches.  
Figure 9: Paths of experimental approach in Mission 2 with 20 Pols.
###### Abstract  
In this paper, we propose an approach based on ensemble learning to classify histology tissues stained with hematoxylin and eosin. The proposal was applied to representative images of colorectal cancer, oral epithelial dysplasia, non-Hodgkin's lymphoma, and liver tissues (the classification of gender and age from liver tissue samples). The ensemble learning considered multiple combinations of techniques that are commonly used to develop computer-aided diagnosis methods in medical imaging. The feature extraction was defined with different descriptors, exploring the deep learning and handcrafted methods. The deep-learned features were obtained using five different convolutional neural network architectures. The handcrafted features were representatives of fractal techniques (multidimensional and multiscale approaches), Haralick descriptors, and local binary patterns. A two-stage feature selection process (ranking with metaheuristics) was defined to obtain the main combinations of descriptors and, consequently, techniques. Each combination was tested through a rigorous ensemble process, exploring heterogeneous classifiers, such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes. The ensemble learning presented here provided accuracy rates from 90.72% to 100.00% and offered relevant information about the combinations of techniques in multiple histological images and the main features present in the top-performing solutions, using smaller sets of descriptors (limited to a maximum of 53), which involved each ensemble process and solutions that have not yet been explored. The developed methodology, i.e., making the knowledge of each ensemble learning comprehensible to specialists, complements the main contributions of this study to supporting the development of computer-aided diagnosis systems for histological images.  
Keywords:ensemble learning; handcrafted features; deep-learned features; two-stage feature selection method; histological images +
Footnote †: journal: Medical Imaging
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 2 Materials and Methods
### Dataset  
The DDI-Extraction 2013 corpus [6] is a collection of biomedical texts containing sentences from the DrugBank database and MedLine abstracts. The DrugBank database focuses on providing information on medicinal substances, while MedLine is a more general database of scientific publications from health-related sectors. The corpus has been manually annotated by two expert annotators and is considered the benchmark dataset for the text-based DDI extraction task which includes the recognition of drug named entities and the interaction classification of the drug pairs.  
The dataset contains separate XML files where each one constitutes a document, which is further separated into its individual sentences. For each sentence, the drugs (entity) it mentions are listed and, for each possible pair of them, an interaction pair (relation pair) is defined. Therefore, \(n\) drugs define \(n(n-1)/2\) pairs of interactions. Highlighted elements are characterized by unique identifiers (id) that reveal their position in the XML tree hierarchy. The corpus is split into a single training set and two separate test sets for both drug recognition and interaction classification tasks. Table 1 provides a summary of the corpus's main features and statistics for the predefined train and test datasets splits.  
Drug entities and interactions are classified into categories (types) based on the context of the sentence they are mentioned in. In the majority of them, the named entities concern drugs intended for human use and are classified as \"drug\", \"brand\" and \"group\" types, while other substances are classified as \"drug_n\". Similarly, the interaction types between two drugs when administered simultaneously are categorized as follows:  
* Effect: These are changes in the effect of a substance on the body, such as the appearance of symptoms and clinical findings.; The results of such effects are also referred to as pharmacodynamic properties of drugs.
* Mechanism: Refers to modifications in the absorption, distribution, metabolism and excretion of drugs, characteristics that constitute their pharmacokinetic properties. In other words, it concerns how the concentration of a substance in the body is affected by the presence of the other substances;
* Advice: Refers to descriptions containing recommendations or advice regarding the simultaneous use of two drugs;
* Int: Assigned in the case where the existence of an association between two drugs is mentioned, without any additional information indicating its type;
* Negative: It refers to the absence of interaction between two substances.
# Textual Data Science for Logistics and Supply Chain Management
## 3 Results
### Correspondence Analysis  
Correspondence analysis (CA) is a technique that is used to scale the row and column categories of a frequency table. In our application, this frequency table is the DTM with the interviews as row categories and the words as column categories. The computation of these scores is based on a singular value decomposition (SVD) for a given dimensionality. After some normalizations of the SVD output (see [PERSON] (2018) [23] for more technical details), the resulting category scores can be plotted, which is usually done in 2D or 3D. Correspondence analysis (CA) simultaneously shows associations among words and managers [31; 32].  
We applied CA for each company separately and used a simple frequency cutoff for the words. In order to avoid cluttering the resulting CA maps, we used the 20 most frequent words from the respective interviews. For each company, we employed a two-dimensional solution using the _anacor_ package [33] and produced the symmetric CA maps shown in Figure 2. The within-rows distances and the within-column distances correspond to \(\chi 2\)-distances. Mx stands for the respective manager, and the distance to the topics shows the extent to which they \"correspond\" with those themes. Similar to a principal component analysis or (exploratory) factor analysis [34], a correspondence analysis is a useful multivariate graphical technique for exploratory research. It helps to detect relationships among categorical variables. In our study, the positioning of the managers allows for an easy identification of different work priorities. For example, in company A, manager M1 is in close proximity to terms such as \"market,\" \"years,\" \"forecast,\" and \"budget,\" indicating that (s)he is involved in planning and scheduling. The term \"bad,\" which is closely above M1, implies potential problems, which might warrant further investigation. Additionally, the distances between the respective managers and even the words might offer some useful insights. In company C, for example, M2 is distant from the other managers, indicating that (s)he has different tasks and/or preferences as compared to the other ones. In company B, one can see that the terms \"budget\" and \"storage\" on the top are distant from the rest of the terms but that they are in relative proximity. This might be a first indicator that the storage in this company either takes up a significant part of the budget or needs to better budgeted. Again, further insight can only be gained by following up with the company, but the CA illustrates that in this specific company storage is apparently a topic that is closely linked to budgeting.
## 3 Proposed Method
### Weight Parameter Estimation  
Once the time-frequency masks of the target speaker are estimated, they can be integrated in the weight parameter estimation of the cWMM together with dictionaries \(\mathbf{a}_{k}\) and \(\eta_{k}\). In known environments, the spatial dictionaries can be trained using pre-collected data. Though accurate localization is expected in this case, training data collection would be cumbersome. For a general test environment, we consider the direct sound propagation vector [33] as the spatial centroid instead. This vector depends on the array geometry only and is given by:  
\[\mathbf{a}_{k}^{\text{direct}}(f)=\frac{1}{\sqrt{M}}[e^{j\omega\tau_{k,1}},e^ {j\omega\tau_{k,2}},...,e^{j\omega\tau_{k,M}}]^{T} \tag{9}\]  
where \(j\) is the complex unit, \(\omega\) is the angular frequency, and \(\tau_{k,m}\) is the observed delay of the direct sound in the \(m\)-th microphone. \(\tau_{k,m}=\mathbf{d}_{k}^{T}\mathbf{r}_{m}/c\) with \(\mathbf{d}_{k}\) the unit vector in the \(k\)-th coming direction, \(\mathbf{r}_{m}\in\mathbb{R}^{3}\) the coordinates of the \(m\)-th microphone, and \(c\) the sound velocity. The concentration parameter \(\eta_{k}\) is decided through empirical analysis, and we set \(\eta_{k}=5\) here, as suggested in [27], which means equal importance is put on different frequencies. For modeling the background noise, \(\eta_{k}\) is set to zero, which leads to a uniform distribution.  
The weight parameters are estimated by the maximization of the weighted likelihood function (6), and this is achieved by the following gradient ascent-based updates [26]:  
\[\alpha_{k}^{it}(t) = \alpha_{k}^{it-1}(t)+\lambda\sum_{f=1}^{F}\frac{1}{\sum_{k=0}^{K} \alpha_{k}^{it-1}(t)w_{k}(t,f)\delta(t,f)}w_{k}(t,f)\delta(t,f) \tag{10}\] \[\alpha_{k}^{it}(t) = \frac{1}{\sum_{k=0}^{K}\alpha_{k}^{it}(t)}\alpha_{k}^{it}(t) \tag{11}\]  
Figure 1: Architecture of the speaker-aware time-frequency selection neural network. The grey components represent non-linear activations, which are \(\text{ReLU}(x)=\max(0,x)\) for the feed-forward layers.  
where \(\alpha_{k}(t)=\frac{1}{K+1}\) for initialization, \(w_{k}(t,f)=\mathcal{W}(\mathbf{z}(t,f);\mathbf{a}_{k}^{\text{direct}}(f),\eta_{k})\), the learning rate \(\lambda=0.01\), and the maximum iteration \(it_{max}=3\). After the time-frequency selection, the maximum of the mixture weights would correspond only to the target speaker. The processing pipeline of the proposed algorithm is summarized in Figure 2.
# A Multibody Model Calibration Method for Vehicle Misuse Testing
## 2 The Overall Calibration Workflow with Commercial Software
### Misuse Test Simulation with Commercial Software  
Figure 3 depicts an Adams/Car simulation of the vehicle misuse test where the vehicle passes over a speed bump on a digital road surface. As with the road tests, the time  
Figure 3: Vehicle misuse testing in Adams/Car of passing over a speed bump.  
Figure 2: Overall calibration workflow for vehicle misuse tests: test processes in cyan, Adams/Car calibration in yellow, and theoretical auxiliary analysis in orange.  
domain responses are recorded. As displayed in Table 2, the recorded responses primarily include the driving speed \(v\), the vertical wheel center force \(f_{\mathrm{z}}\), the vertical wheel center acceleration \(a_{\mathrm{z}}\), the shock absorber compression \(d_{\mathrm{s}}\), and the lower control arm tension \(f_{\mathrm{c}}\). With these responses, the vehicle's driving posture and force distribution are essentially defined. The driving speed can be measured using a GPS sensor, the vertical wheel center force using a six-component force meter, the accelerations using acceleration sensors at a specific positions, the shock absorber compression using a cable displacement sensor, and the arm tensions using strain gauges attached to the components. It is notable that the six-component force meter is utilized exclusively for Class I obstacles and not for Class II obstacles. Class I obstacles include speed bumps [19; 28; 29; 30] and square roadblocks. Class II obstacles include square potholes and slope potholes, with the chassis clearance being 30 mm higher than that for Class I obstacles.  
The road test responses allow for calibration of the corresponding model parameters, which are listed in the third column of Table 2. In other words, these parameters directly affect the respective responses. For instance, the recorded driving speed \(v\) is itself a model parameter. The vertical wheel's center force \(f_{\mathrm{z}}\) is determined by the vehicle's total mass \(m\), the moment of inertia \(J\), and the centroid position \((x,y)\). The subsequent analysis reveals a strong correlation between the vertical wheel's center acceleration \(\tilde{z}_{\mathrm{w}}\) and the shock absorber compression \(d\), both of which are determined by the shock absorber's stiffness \(k_{\mathrm{s}}\), the damping \(c_{\mathrm{s}}\), the preload \(f_{\mathrm{s}}\), and the wheel's center mass \(m_{\mathrm{w}}\). Although the GPS sensor records altitude changes, it does not calibrate the vehicle's vertical height due to its measurement error, which can be as large as 0.1 m.  
It is important to note that measurement channels that have failed should be identified and excluded from the experimental data. Sensor failure occurs more frequently in misuse tests than in durability tests due to the harsh response in the vehicle components when they come into contact with obstacles at high driving speeds.
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 3 Problem Formulation  
Given that the placement of IAB donors is predefined, which is mainly due to the availability of the fixed (optical) connections to the core network, our main objective is to minimise the number of nodes that need to be deployed while covering all the considered areas. This initiative lays the groundwork for broader network scalability. We first formulate and solve the problem for a one-hop scenario and then extend it to a multi-hop one.  
Based on the presented system model, Figure 2 can be roughly translated into the two-dimensional map of Figure 3. We consider deploying IAB donors and nodes at candidate locations \(j=(x_{j},y_{j})|\forall j\in J\) to provide coverage service. For simplicity, \(j\) represents the node's candidate location, while \(i\) denotes the location where a donor has been deployed. The grid to be covered is defined as \(k=(x_{k},y_{k})|\forall k\in K\), ensuring that the power received at the grid center meets or exceeds the minimum \(SNR_{0}\) requirement, thereby providing the necessary data rate service to users (access side).
## 3 System Model and Problem Formulation
### Network Model  
We consider an IoT network as illustrated in Figure 1, where \(M\) servers serve \(N\) smartphones. Smartphones collect health data with \(L\) possible labels and execute DML training for local model weights. We assume each smartphone uploads local weights to its nearest server. As raw IoT data never leave the corresponding smartphones in DML training, privacy leakage is reduced [12].  
To further secure DML for smart health, a permissioned blockchain is used [32]. Only verified entities can join the proposed DML system. We assume servers are honest and do not launch attacks. Servers store complete records of all blocks, while smartphones only store block headers to verify and download global weights [33]. However, smartphones  
Figure 1: Blockchain-enabled DML for smart health: an example with 4 smartphones, 3 MEC servers and 4 data labels (\(N=4\), \(M=3\), \(L=4\)).  
may become free-riders by contributing very little while still downloading the global model [8]. A detailed threat model is introduced later in this section.