esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The association between genetics and lifestyle factors is crucial when determining breast cancer susceptibility, a leading cause of deaths globally. This research aimed to compare the body mass index, smoking behavior, hormonal influences, and BRCA gene mutations between affected patients and healthy individuals, all with a family history of cancer. All these factors were then utilized as features to train a machine learning (ML) model to predict the risk of breast cancer development. Between 2020 and 2023, a total of 1389 women provided detailed lifestyle and risk factor data during visits to a familial cancer center in Italy. Descriptive and inferential statistics were assessed to explore the differences between the groups. Among the various classifiers used, the ensemble of decision trees was the best performer, with a 10-fold cross-validation scheme for training after normalizing the features. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve and its area under the curve (AUC), alongside the accuracy, sensitivity, specificity, precision, and F1 score. Analysis revealed that individuals in the tumor group exhibited a higher risk profile when compared to their healthy counterparts, particularly in terms of the lifestyle and genetic markers. The ML model demonstrated predictive power, with an AUC of 81%, 88% sensitivity, 57% specificity, 78% accuracy, 80% precision, and an F1 score of 0.84. These metrics significantly outperformed traditional statistical prediction models, including the BOADICEA and BCRAT, which showed an AUC below 0.65. This study demonstrated the efficacy of an ML approach in identifying women at higher risk of breast cancer, leveraging lifestyle and genetic factors, with an improved predictive performance over traditional methods.  
breast cancer risk; risk factors; machine learning; statistical models Article
## 4 Results and Discussion
### Analysis of the Variable Importance  
Using global sensitivity study by the evolutionary SVM model, the variable importance is calculated, as shown in Figure 11. It can be observed that grout types have the most significant influence on the strength of coal-grout composite with an influencing percentage of 47.6%, followed by curing time (32.8%), while and coal-grout ratio is the least sensitive variable with an influence percentage of 19.6%. It should be noted that the importance score is obtained by the dataset used in this paper. More accurate results can be obtained if more data samples are included in the dataset in the future.  
Figure 10: Radar plot of different ML algorithms to compare different statistical metrics.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
###### Abstract  
Bluetooth Low Energy (BLE) has gained very high momentum, as witnessed by its widespread presence in smartphones, wearables and other consumer electronics devices. This fact can be leveraged to carry out opportunistic sensor data collection (OSDC) in scenarios where a sensor node cannot communicate with infrastructure nodes. In such cases, a mobile entity (e.g., a pedestrian or a vehicle) equipped with a BLE-enabled device can collect the data obtained by the sensor node when both are within direct communication range. In this paper, we characterize, both analytically and experimentally, the performance and trade-offs of BLE as a technology for OSDC, for the two main identified approaches, and considering the impact of its most crucial configuration parameters. Results show that a BLE sensor node running on a coin cell battery can achieve a lifetime beyond one year while transferring around 10 Mbit/day, in realistic OSDC scenarios.  
Bluetooth Low Energy; Bluetooth Smart; opportunistic data collection; sensor networks; modeling; performance evaluation; beacons; Internet of Things +
Footnote †: journal: Sensors
## 4 Results and Discussion
### Mapping Bisgaard Standard Levels to the Implemented Clusters  
The aim of this part is to compare the clustering results with the chosen standard hearing levels by [PERSON] [15]. He chose seven flat and moderately sloping standard  
Figure 5: Silhouette plots 8 clusters for 25,307 audiograms stage 1 and 20,956 audiograms for stage 3.  
Figure 6: Silhouette plots 10 clusters for 25,307 audiograms stage 1 and 22,002 audiograms for stage 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Criterion**} & \multicolumn{2}{c}{**8 Clusters**} & \multicolumn{2}{c}{**10 Clusters**} \\ \cline{2-5}  & **Stage 1** & **Stage 3** & **Stage 1** & **Stage 3** \\ \hline Silhouette coefficient & 0.4506 & 0.5616 & 0.4750 & 0.5507 \\ Calinski-Harabasz Value & \(1.8447\times 10^{4}\) & \(1.9315\times 10^{4}\) & \(1.8026\times 10^{4}\) & \(1.9870\times 10^{4}\) \\ Davies-Bouldin Value & 1.0751 & 0.9189 & 1.0673 & 1.0404 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different clustering evaluation criteria for audiograms population.  
audiograms; named N1 to N7 and three steep sloping ones named S1 to S3 [15]. The eight clusters implemented by this work mapped N1, S1, and N2 in the same cluster, N4 and N5 to the same cluster and N6 and N7 to the same cluster. Meanwhile, N3, S2, and S3 are classified in different clusters. However, for the 10 clusters, S1 and N2 are mapped to the same cluster and N6 and N7 to the same cluster. Thus, N1, N3, N4, N5, S2, and S3 are in different classes. The mapping results are shown in Figure 7, where the x-axis represents the 10 standards N1-N7 and S1-S3, while the y-axis is the cluster number in our work. These mapping results are also displayed in Table 6 for further clarification and comparison.
###### Abstract  
BeiDou system navigation messages are modulated with a secondary NH (Neumann-Hoffman) code of 1 kbps, where frequent bit transitions limit the coherent integration time to 1 millisecond. Therefore, a bit synchronization algorithm is necessary to obtain bit edges and NH code phases. In order to realize bit synchronization for BeiDou weak signals with large frequency deviation, a bit synchronization algorithm based on differential coherent and maximum likelihood is proposed. Firstly, a differential coherent approach is used to remove the effect of frequency deviation, and the differential delay time is set to be a multiple of bit cycle to remove the influence of NH code. Secondly, the maximum likelihood function detection is used to improve the detection probability of weak signals. Finally, Monte Carlo simulations are conducted to analyze the detection performance of the proposed algorithm compared with a traditional algorithm under the CN0s of 20-40 dB-Hz and different frequency deviations. The results show that the proposed algorithm outperforms the traditional method with a frequency deviation of 50 Hz. This algorithm can remove the effect of BeiDou NH code effectively and weaken the influence of frequency deviation. To confirm the feasibility of the proposed algorithm, real data tests are conducted. The proposed algorithm is suitable for BeiDou weak signal bit synchronization with large frequency deviation.  
bit synchronization; NH code; differential coherent; Beidou System +
Footnote †: journal: Sensors
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 1 Introduction
### Deep Learning in Remote Sensing  
Deep learning approaches have been successfully used for many machine learning tasks including face detection [20], object recognition [21], and machine translation [22]. Benefiting from both theoretical and technical advances [23; 24], they have shown to be extremely effective when there exists a relationship between the dimensions of the data such as for images, audio, or text. The applications to remote sensing of the two main deep learning architectures--Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)--are presented in the following.
## 4 Conclusions  
In this study, we first determined the antioxidant properties of the leaves, flowers, and stems to identify the appropriate parts of the plant that could be used to produce Genova tea with superior antioxidant properties and quality. The results showed higher levels of antioxidant activity in the leaves and flowers; hence, leaves with the highest yield were used in the tea production experiment.  
Fourteen treatment conditions were set up and examined in detail, focusing on the S times (0, 1, 2, 3, 5, and 10 min), drying methods, and temperatures (-56, 25, 40, 50, 60, 70, 80, 90, and 100 \({}^{\circ}\)C). Our results showed that TPC, DPPH, H-ORAC, and rosmarinic and chicoric acid content values were significantly higher after 2 min of steam-heat treatment compared with that NS (\(p<0.05\)), especially for the rosmarinic acid. The green color faded after steam-heat treatment and drying at temperatures \(>50\)\({}^{\circ}\)C. The primary aroma components of Genova were Linalool, _trans_-alpha-bergamotene, and 2-methoxy-3-(2-propenyl)-phenol, determined through GC-MS analysis. Among these, Linalool and 2-methoxy-3-(2-propenyl)-phenol were significantly reduced by S treatment, while _trans_-alpha-bergamotene was not. In summary, steam-heating treatment for 2 min is better for retaining high antioxidant properties, drying at \(<\)50 \({}^{\circ}\)C is better for retaining green color, and the aroma components can be better retained with the absence of S treatment. Materials with high antioxidant properties can be used in the food and pharmaceutical industries, whereas materials with aromatic components can be used in cosmetics.  
This study was conducted with the aim of tea production; however, our findings also indicate the possibility of producing an improved Genova dried product, which can be applied in food products, such as tea, as well as in cosmetic and pharmaceutical applications. This study measured only TPC and rosmarinic and chicoric acids as antioxidant components. Identifying other polyphenol components and clarifying the effects of S time, drying method, and temperature on such components will provide novel insights into new developments and applications. Finally, the aroma analysis was evaluated using GC-MS; therefore, more biological evaluation, e.g., human sensory testing, should also be considered in future studies.  
Conceptualization, Y.T.; investigation, Y.T.; data curation, Y.T. and M.K.; writing--original draft preparation, Y.T. and M.K.; writing--review and editing, Y.T. and M.K.; visualization, Y.T. and M.K.; supervision, Y.T.; project administration, Y.T.; funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript.  
This study was supported by \"KAKENHI, Grant-in-Aid for Scientific Research (B) (21H00808)\", Ministry of Education, Science, and Culture of Japan.  
Data Availability Statement: Data is contained within the article.  
We would like to thank [PERSON] for providing experimental material.  
The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 4 Multi-Agent Communication and Networking
### Routing Protocols in Ad-Hoc Networks  
There are several ways of classifying routing protocols. Link-state (LS) protocols flood the network with lists of \"neighbors\" for all nodes, so each node can reconstruct the network topology [37; 38]. Distance-vector (DV) protocols by contrast, have each node share its shortest-paths-to-all-other nodes (i.e., the distance-vector) with its neighbors [39]. All nodes then infer the shortest path to every other node. As a general rule, LS protocols require more overhead than DV protocols. However, DV protocols do not provide nodes any knowledge of the network topology and are thus vulnerable to the so-called \"count-to-infinity\" problem [40].  
Both LS and DV protocols prioritize shortest paths over all other routes. The metric for distance is usually the number of hops, but can also include the delay or cost. A more relevant metric than distance for multi-robot networks (where safety is a concern) is reliability; the system tolerates lower data rates provided communication is sufficiently reliable. LS and DV protocols are not well equipped for these \"low-bandwidth-delay-sensitive\" applications.  
Other protocol classifications exist as well. Pro-active protocols periodically update their routing tables to ensure the entries are always fresh. They are advantageous in safety-critical applications that require reliable communication. Reactive protocols on the other hand, only initiate updates when requested routes are not offered in existing tables. They tend to be more nimble and efficient by design, and are useful in applications with strict constraints on power consumption [41; 42; 43].
# Formal Matters on the Topic of Risk Mitigation: A Mathematical Perspective
## 2 Formalizing Risk
### Hazard  
Assume for simplicity that we are looking at the case of a single hazard expressed as the probability of occurrence of a dangerous event hitting a specific area, with no intensity information. Formally, this translates to a _pointwise hazard_ probability density function \(h:\Omega\to[0,1]\) such that, for any _area of interest_\(A\subseteq\Omega\), the probability of the hazard affecting the area \(A\) is  
\[H(A)=\int_{A}h(x,y)\mathrm{d}x\mathrm{d}y.\]  
Hazard may depend on the location (coordinates) directly, or implicitly through some other spatial property that can be affected by human action (e.g., many geophysical flows may be affected by building ditches and barriers).  
We can make this dependency explicitly by writing \(h:\mathcal{T}\times\Omega\to[0,1]\), where \(\mathcal{T}\) is a family of functions \(T\) defined in \(\Omega\) and with values in some appropriate codomain \(\mathcal{D}_{\mathcal{T}}\). As a practical example, \(T\) might be a mathematical description of the topography of \(\Omega\), and \(h(T,x,y)\) is the pointwise hazard associated with a geophysical flow whose behavior depends on the given topography. In this case, we would have \(\mathcal{D}_{\mathcal{T}}=\mathbb{R}\), i.e., the set of real numbers describing the pointwise altitude a.s.l. of the area of study.  
Note that \(T\) may have \"long range\" effects, in the sense that a change in the value of \(T\) at some point \((x_{0},y_{0})\) may affect the hazard in points \((x,y)\
eq(x_{0},y_{0})\): for example, building an embankment is a local alteration of the topography that can reduce hazard in all points downstream of the structure. For this reason, \(h\) must depend explicitly on \(T\) as a function, rather than simply as \(h(x,y)=h(T(x,y),x,y)\).  
In general, man-made structures have an influence on hazard even if that is not their intent. For example, buildings and roads can influence geophysical flows, and coastal/river structures can influence flooding hazard. While the influence of such structures could be incorporated in \(T\), to simplify notation, we will separate this into an additional dependency of hazard on some \(B\in\dot{B}\) that will in turn depend on exposure-related elements that will be presented momentarily.
## 2 Materials and Methods
### Determination of Total Polyphenol Content (TPC) and Antioxidant Capacities (FRAP and TEAC)  
Melon wine samples were diluted in water (1:5) and their total polyphenol content (TPC) and antioxidant capacities were determined, using a multiscan plate spectrophotometer (Tecan infinite M200, Mannedorf, Switzerland). For TPC, [PERSON]'s reagent was used and performed as detailed by [PERSON] et al. [14], using gallic acid as the standard (mg gallic acid equivalent/L of must or wine, mg GAE/L). Ferricing reducing antioxidant capacity (FRAP) was measured by the increment of absorbance at 593 nm due to the reduction of ferrous cation Fe + 3 by antioxidants at 37 \({}^{\circ}\)C and pH 3.6 [15] and was evaluated according to cation ferrous Fe + 2 linear calibration (mmol Fe\({}^{+2}\)/L of wine). Trolox equivalent antioxidant capacity (TEAC) was performed according to [16], measuring the reduction of radical ABTS at 734 nm, and Trolox was used as the equivalent standard, with data expressed as mmol Trolox equivalent/L of wine (mmol TE/L of wine).
# ADSSD: Improved Single-Shot Detector with Attention Mechanism and Dilated Convolution
## 3 Method
### Adssd
#### 3.2.2 Multi-Branch Dilated Convolution  
In view of the problem of the detection performance regarding small objects being degraded due to the low resolution of the receptive field of deep networks, dilated convolution can be used to solve it. Inspired by trident networks ([PERSON] et al., 2019) and the module that [PERSON] et al. (2021) proposed, we proposed a multi-branch dilated convolution.  
As shown in Figure 7, we introduced the MDCM to aggregate context information. We first used the \(1\times 1\) convolution to reduce the amount of computation. Then we used \(3\times 3\) convolution with different dilation rates to obtain context information from the feature map.  
Figure 6: Residual CBAM.  
Figure 7: Multi-branch dilated convolution module (MDCM).  
Faced with a gridding problem caused by the dilated convolution, we were inspired by [PERSON] et al. [23] to introduce hierarchical feature fusion (HFF). We can see the improved MDCM in Figure 8. This improved method does not increase the complexity of the module and leads to slower detection speed. Finally, we used skip connections to speed up learning. The dilation rate in our model is defined as [3,5,7]. To ensure detection accuracy and speed, we embedded the module into the backbone network.  
In this study, we used dilated convolution with a convolution kernel size of \(3\times 3\). Our formula for calculating the dilated convolution kernel size is as follows:  
\[k^{\prime}=k+(k-1)(r-1) \tag{5}\]  
where \(k\) is mean kernel size, and \(r\) is the dilated rate.  
We use \(R_{n}\) to represent the receptive field of each pixel in the \(n\)th layer; \(k^{\prime}\) is kernel size of dilated convolution; and \(s_{i}\) is the stride. The \(R_{n}\) is calculated as:  
\[R_{n}=R_{n-1}+\big{(}k^{\prime}-1\big{)}\times\prod_{i=1}^{n-1}s_{i} \tag{6}\]  
Table 1 shows the receptive field size of the dilated convolution calculated by the above formula.
## 3 High-Quality ToF Camera Data Acquisition Strategies  
To improve the data acquisition accuracy of existing time-of-flight (ToF) cameras (Boston Dynamics, Waltham, MA, USA), deep learning algorithms have primarily been used to adjust sensor values based on the clustering of object shapes. However, this method has not focused on achieving the millimeter-level precision required in industrial settings. Ithas instead concentrated on recognizing and differentiating the forms of general objects. To obtain more accurate and detailed depth information necessary in industrial environments, there is a need to develop deep learning algorithms that enhance the accuracy of sensor acquisition values. A practical method could be to use the depth of ToF pixels obtained from real environments and the corresponding exact distance as labels for training to improve the accuracy of each pixel's depth value. The main reason such methods have not been previously attempted is due to the practical difficulty of labeling the exact distance values of pixels measured by ToF sensors (Boston Dynamics, Waltham, MA, USA) This paper proposes a method for acquiring precise millimeter-scale depth values of pixels from ToF sensors to be used as labels. This method involves simulating a ToF sensor in a simulation environment set up exactly like the real environment, using a 3D model, and using the accurate depth values obtained as labels, as shown in Figure 1. This approach leverages the benefits of parts and process 3D models available in the product development processes already used in industrial settings. For example, in automobile development, cars are produced based on the 3D models of parts created during the design and manufacturing processes. Furthermore, to automate the manufacturing process, 3D models and simulation environments of the production process and surrounding objects are established. By utilizing these existing industrial processes, precise millimeter-scale depth values of ToF sensor pixels can be easily labeled.  
As shown in Figure 1, the entire process involves setting up a simulation environment using a 3D model identical to the actual product and simulating a ToF camera to acquire 3D depth information from the same location and viewpoint as in real environments. Even with precise settings, discrepancies in acquisition position and viewpoint can occur. To resolve this issue, shape matching using the ICP algorithm is performed to align the shapes of the data acquired in reality and in simulation. However, due to noise from the ToF sensor, the performance of the ICP algorithm may degrade, necessitating pre-data processing. Additionally, to eliminate the resolution discrepancy between the high-resolution depth information obtained in the simulation and the 400 \(\times\) 300 resolution of the ToF sensor, the resolution of the depth information is adjusted through downsampling after 3D model alignment using ICP. Finally, accurate labels for the depth information of ToF pixels obtained are extracted and used to train a deep learning algorithm, thus developing an AI algorithm to enhance the accuracy of ToF sensing values.
## 2 Materials and Methods
### Data Analyses  
Using a customized Matlab Program version R2024b (Math WorksTM Inc., Natick, MA, USA), force signals were filtered using a bidirectional fourth-order Butterworth filter with a cut-off frequency of 30 Hz. To minimize potential initial-phase adjustment and early termination, the central 14 s of each trial were analyzed by excluding the initial and final 3 s of the recorded force data.  
Bilateral ankle-dorsiflexion force control capabilities were assessed by calculating four variables: (a) mean force, (b) force accuracy (root mean square error: RMSE), (c) force variability (standard deviation: SD) and (d) force regularity (sample entropy: SampEn). Regarding force regularity, lower SampEn values (i.e., close to zero) reflect more consistent and predictable force output patterns, while higher values suggest increased irregularity in force production. SampEn can be calculated using the following:  
\[SampEn=(x,m,r,N)=ln\left[\frac{C_{m}(r)}{C_{m+1}(r)}\right]\]  
We used 2 for \(m\) and \(r\) = 0.2 \(\times\) SD of force data [39; 40]. Before statistical analyses, we confirmed the normality of distribution for all dependent variables using Shapiro-Wilk W test. To estimate potential tDCS effects on bilateral force control capabilities, we used a paired \(t\)-test on mean force, RMSE, SD and SampEn, respectively. Furthermore, we performed two-way repeated measures ANOVA (Stimulation Condition \(\times\) Limb Side; 2 \(\times\) 2) on mean force, SD and SampEn to investigate effects of bilateral tDCS protocol on each leg side (i.e., left or right). For post hoc analysis, we applied [PERSON]'s pairwise comparisons. All statistical analyses were conducted using the IBM SPSS Statistical 28 (SPSS Inc., Chicago, IL, USA) and we set an alpha level at 0.05.
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 4 \(\boldsymbol{\
abla}\) with Uncertain Parameters and White Noise  
In this section, \(\boldsymbol{\
abla}\) (or \(\boldsymbol{\Psi}_{\text{sub}}^{i}\)) is first appropriately modeled with underlying parameters within a range to include the effect of uncertainties in their knowledge, and to account for initial artificial white noise. Subsequently, with the help of extensive simulations, it is shown that \(\boldsymbol{\Psi}_{\text{max}}\) can be used in place of \(\boldsymbol{\
abla}\) to form test statistics and PLs.