esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography +
## 2 Materials and Methods
### Machine Learning Algorithms and Neural Networks  
For classification, different machine learning models were tested: Support Vector Machine (SVM) [42], Decision Tree [43] using the recursive partition (rpart) function [44], neural network (Nnet) [45], linear discriminant analysis (LDA) [46], and random forest (RF) [47]. To tune the hyperparameters of the defined models, a grid-search method [48] was used to test all possible combinations of the specific hyperparameters for each model. All the ML analysis were performed in R. Finally, neural networks were used for the multiclass classification problem, to evaluate its performance, even on a small dataset. The network used in this work presents a relu activation function of the layers, an optimization function rmsprop, and the evaluation is based on accuracy. The division into testing and training was made using 80% of the data for training and 20% for testing. The training of the models was performed using a batch size equal to 1 (having a reduced dataset available is not necessary to further divide the training set) and the insertion of a callback that allows to store the model that has layer weights such that the maximum accuracy is reached. An empirical evaluation was made on the number of epochs.
# Model-Guided Manufacturing of Transducer Arrays Based on Single-Fibre Piezocomposites
## 2 Materials and Methods
### Transducer Assembly  
At first, the PZT fibre disc is connected with the PCB. The PCB holds 19 round-shaped pads for connecting the transducers, and one 0402 socket for attaching a miniaturised temperature sensor. To avoid short circuits caused by displaced adhesive, a concentric groove is milled around each pad. Seventeen drill holes are located behind the transducers after the assembly (see Figure 5a). With a depth of 4.5 mm, these drill holes hold space for the backing material. Three more drill holes are used for pinhole mounting [21]. Conductive adhesive is stencil printed to the pads (see Figure 5b) and the PZT disc then automatically placed on the PCBs (Datacon EVO2200, Besi, Radfeld, Austria). The next assembly step contains manual soldering of the temperature sensor into the designed recess. Then, the connection of the ground electrode by filling the designated holes with conductive adhesive (see Figure 5c) is done. As a safety margin, two redundant ground connections are foreseen.  
One single layer aids in matching the acoustic impedance of the piezoceramic material to water. The TMM4 material and its thickness was previously evaluated to obtain suitable acoustic matching and bandwidth [17]. TMM4 is available in sheets with a designed thickness of 425 m and a 35-m copper layer on top. After milling the disc shape, the copper layer is structured (FeCl\({}_{3}\) wet etching, see Figure 6a). This ensures a uniform and parallel adhesive bond between the TMM4 and the PZT fibre disc.  
Figure 4: Preparation of the PZT fibre disc: (**a**) top view of the uniform sputtered ground electrode; (**b**) bottom view of the PZT fibre disc with sputtered electrodes which peeled off during cleaning; and (**c**) electrode print using conductive adhesive.  
For bonding the matching layer, 42 mg of adhesive (EPO-Tek 301-2) is dispensed to the centre of the PZT fibre disc (see Figure 6b). For acoustic backing, polyurethane (Flexovoss K6S, Vosschemie, Uetersen, Germany) is mixed with tungsten powder with a weight ratio of 1:2.5. This compound is then dispensed into the drill holes of the PCB (see Figure 6c). Finally, a plug is soldered to the PCB and the TAS are glued (Bondit B-45, Reltec Llc, Santa Rosa, CA, USA) into cylindrical stainless steel housings. Figure 7 shows the finalised TAS.  
Figure 5: (**a**) Conductive conduction process of PCB - PZT fibre disc; (**b**) stencil printing conductive adhesive onto the designated pads; and (**c**) automatic pick and place of the PZT fibre disc.  
Figure 6: (**a**) Finalising the TAS assembly by etching the copper layer of the TMM4 disc; (**b**) dispensing 42 mg of adhesive on top of the PZT fibre disc for pick and place of the matching layer; and (**c**) application of the backing material.  
Figure 7: Finalised set of 10 TAS in stainless steel housing (30 mm diameter and 70 mm length). On top, the front side of the matching layer (white surface) is visible.
# An Improved Small Target Segmentation Model Based on Mask Dino
## 2 Related Methods
### Mask Dino Network  
[PERSON] et al. proposed a unified framework for object detection and segmentation called Mask Dino, which extends the DINO model by employing a mask approach. The backbone network achieves excellent pre-training results on both the ResNet-50 [21] backbone and the Swin Transformer pre-trained models. Inspired by other unified models for image segmentation, the authors utilize DINO's content query embeddings by adding a Mask prediction branch that supports all image segmentation tasks (instance, panoptic, and semantic). This branch performs mask classification on high-resolution pixel embedding maps (input image resolution) obtained from the backbone and the Transformer encoder features. The Mask branch predicts binary masks by simply generating each content query embedding from the pixel embedding map. Key components from DINO are expanded through shared architecture and training procedures. Mask Dino adopts a unified, enhanced query selection method using encoder-dense priors to predict masks from top-ranked tokens, initializing mask queries as anchor points for segmentation tasks. A unified mask denoising training is introduced to accelerate segmentation training. Subsequently, a hybrid bipartite graph matching algorithm is used to achieve precise real-value-to-mask matching.  
The Mask DINO model performs excellently under different backbone network configurations. However, upon reviewing the literature [22], it has been observed that in scenarios with complex backgrounds and imbalanced target categories, Mask DINO may still encounter issues of false detection and missed detection when handling small object segmentation.
###### Abstract  
The fracturing performance of shale directly influences the effectiveness of shale gas development. To investigate the impact of bedding on the anisotropic mechanical properties and failure modes of shale under different stress paths, a shale model with randomly generated bedding planes was established using RFPA [3]. Uniaxial compression, direct tension, and triaxial compression numerical simulations were conducted. The results reveal the following key findings: (1) With an increase in the bedding angle, the uniaxial compressive strength of shale shows a U-shaped change trend, while the tensile strength gradually decreases. Under the two loading conditions, the failure mechanism of the samples is significantly different, and the influence of the bedding distribution position on the direct tensile failure mode is more significant. (2) The confining pressure reduces the brittleness and anisotropy of shale by altering the internal stress distribution and inhibiting the propagation of microcracks. When the confining pressure increases from 0 MPa to 22.5 MPa, the strength increases by about 41% when the bedding angle is 30\({}^{\circ}\), while the strength of 0\({}^{\circ}\) bedding only increases by 29%. (3) The frictional constraint effect plays a significant role in shale strength. Frictional stresses influence the strength near the interface between the bedding and the matrix, while the regions outside this interface maintain the original stress state. In shale with inclined bedding, shear stress promotes slip along the bedding planes, which further reduces the overall strength. The research findings hold significant guiding value for optimizing fracturing designs and enhancing the efficiency of shale gas development.  
shale; bedding plane; mechanical properties; failure modes; numerical simulation Article
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
## 3 Results and Discussion
### Time Course of Bioflocculant Production
The decline in pH may be indicative of the production of organic acids during metabolism by the microorganism [59; 37] or may be attributed to the acidic nature of the biofoculant produced by the bacteria [60]. A biofoculant produced by _Bacillus licheniformis_ X14 reached its maximum flocculating activity at 48 h while cell growth reached a maximum at 20 h [57]. Time course profiles for production of biofiloculants differ with different microorganisms. However, the majority of studies reported in the literature show a parallel relationship between biofoculant production and cell growth with the maximum flocculating activity being reached in early stationary phase. This suggests that most biofoculants are produced by biosynthesis through nutrient absorption during their growth, and not by cell autolysis [51; 61; 62].
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 2 Model Order Selection
### Information Criteria
#### 2.1.2 Information Criteria for Time Series  
Consider the problem of estimating the rank \(r\) of the signal \(\mathsf{S}=(s_{1},\ldots,s_{N})^{\mathrm{T}}\) by the observed noisy time series \(\mathsf{X}=\mathsf{S}+\mathsf{N}\).  
The parametric model of signals of rank \(r\) can be considered as a nonlinear model with \(k=2r\) parameters [21]. In the case of a noisy signal with Gaussian white noise, the maximum likelihood estimate \(\widehat{\mathsf{S}}\) of the signal \(\mathsf{S}\) obtained from the time series \(\mathsf{X}\) can be obtained by means of the nonlinear least-squares method. Numerically, the least-squares estimate can be obtained using the VPSN method [19] or the MGN method [21]. In both cases, an initial approximation is required, which can be obtained using the Cadzow iterations [22; 24].  
This approach to information criteria is not without its limitations. First, the nonlinear least-squares method is a computationally expensive procedure due to its iterative nature. Additionally, the range over which the rank \(r\) is searched slows down the computation. Second, as demonstrated by numerical experiments (see Section 4), such methods may converge to local minima of the target function, which may result in an overestimation of the rank due to an overestimated error.
# A Differential Measurement System for Surface Topography Based on a Modular Design
## 5 Experimental Verification
### Step Height Measurement in a Large Area  
The step height made by gauge blocks was measured at four different locations in the system measurement area of 100 mm \(\times\) 100 mm. The scan length of each measurement was 50 mm. At each location, the measurements were repeated five times. Figure 10(a) shows the experimental setup and Figure 10(b) shows the profile measured by the differential sensors. The differential setup is discussed in Section 2. Sensor 1 (confocal sensor) was used to measure the heights from the top, while sensor 2 (film interferometer) was used to measure the straightness error from the bottom.  
It was shown that the straightness error of the linear guide was effectively compensated for. Table 3 shows the measurement results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Ra (\(\mu\)m)**} & \multicolumn{2}{c}{**Deviation**} & \multicolumn{2}{c}{**Deviation**} & \multicolumn{2}{c}{**Rq (\(\mu\)m)**} & \multicolumn{2}{c}{**Deviation**} \\ \cline{2-9}
**Sample** & **HQU** & **Mahr** & **(\(\mu\)m)** & **Deviation** & **HQU** & **Mahr** & **XR20** & **(\(\mu\)m)** & **Deviation** \\ \hline
1 & 0.589 & 0.602 & 0.602 & 2.2\% & 0.735 & 0.714 & 0.021 & 2.9\% \\
2 & 1.493 & 1.471 & 1.471 & 1.5\% & 1.746 & 1.737 & 0.009 & 0.5\% \\
3 & 2.736 & 2.728 & 2.728 & 0.2\% & 3.238 & 3.234 & 0.004 & 0.1\% \\
4 & 5.842 & 5.810 & 5.810 & 0.6\% & 6.753 & 6.728 & 0.025 & 0.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Roughness measurement results.  
Figure 11: Step height measurement: (**a**) Step height measurement setup, (**b**) Measured profile.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Location 1** & **Location 2** & **Location 3** & **Location 4** \\ \hline Step height measured (\(\mu\)m) & 100.1910 & 100.1073 & 99.9984 & 99.9834 \\  & 100.1284 & 100.2008 & 99.9857 & 99.9637 \\  & 100.1179 & 100.1414 & 99.9284 & 99.9806 \\ (Nominal: 100.080 \(\mu\)m) & 100.2042 & 100.1783 & 100.0852 & 100.0523 \\  & 100.2156 & 100.1290 & 99.985 & 100.0361 \\ Average (\(\mu\)m) & 100.1715 & 100.1514 & 99.9965 & 100.0032 \\ Repeatability \(\sigma\) (\(\mu\)m) & 0.0451 & 0.0378 & 0.05648 & 0.03859 \\ Reproducibility \(\sigma^{\prime}\) (\(\mu\)m) & \multicolumn{2}{c}{0.09293} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Step height measurement results.  
The standard deviations of the repeated measurements at the same location and different locations were calculated, in order to represent the system's repeatability and reproducibility. The data in Table 3 shows that the repeatability and the reproducibility were within 100 nm.
## 6 Conclusions  
In this paper, to address the problems of poor timeliness awareness and limited flexibility in selecting reporting nodes within LEO satellite networks, a dynamic reporting nodes selection method of network awareness based on active-passive integrated network telemetry is proposed. This method combines active and passive telemetry on the awareness path and constructs an APINT model to select the optimal reporting node based on the constraints of the inter satellite transmission time and status of SGLs. Furthermore, the awareness path is divided into multi segments, and the APINT model is used to select the optimal reporting node for each segment, achieving flexible selection of multiple reporting nodes. To enable dynamic selection of reporting nodes across various service paths in multiple service scenarios, a lightweight Q-learning algorithm is proposed. Through the pre-planning of virtual boundaries, the computational complexity of Q-learning is reduced, and the optimal number of reporting nodes on each service path is dynamically determined. Through simulation, it was found that the proposed method has good performance and can be flexibly applied to the multi-service scenarios in LEO satellite networks.  
Conceptualization, H.D. and T.D.; methodology, H.D.; software, H.D. and S.W.; validation, H.D. and Z.L.; formal analysis, T.D.; investigation, T.D.; resources, S.W.; data curation, D.Z.; writing--original draft preparation, H.D.; writing--review and editing, Q.Z. and D.Z.; visualization, H.D.; supervision, T.D.; project administration, T.D.; funding acquisition, T.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the National Natural Science Foundation of China (62331027), and in part by the Young Elite Scientists Sponsorship Program by CAST (2022 QNRC001).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
Author [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company Space Star Technology Co., Ltd., The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots
## 4 The SWARMs Ontology
### Communication and Networking Ontology  
This information model, shown in Figure 7, describes the communication links available in SWARMs to interconnect the different agents involved in a global mission execution and supervision, i.e., the ashore control station, support ships, USVs, AUVs, and ROVs.  
The communication network should allow the data exchange in different environments (underwater and surface). The underwater environment is especially challenging as the propagation delay is significant, communication links are highly unstable and channel bandwidth is very limited.  
In particular, the link between surfaced AUVs, USVs or buoys and the support ship is made through a radio frequency (RF) connection. In addition, satellite communication could be used with the CCS hosting the MMT, when offshore. For the underwater communication, acoustic modems are used with AUVs whereas ROVs are connected to the support ship by cables, which are used to transmit command and control signals between the operator and the ROVs.  
Figure 6: The environment recognition and sensing ontology.  
Figure 7: Communication and networking ontology.
## 2 Related Work
### Security Standards and Security Requirements Framework
Every study and standard has highlighted nearly the same aspects of security such as security policies, objectives, threats, security audit of people, threat agents, network security, cryptography, physical security, disaster management, assets management, access methods, and accountability and auditability of people. There is not a single standard that defines the mechanism to design the secure IS for organizations that covers all these security aspects. There must also be a proper audit system for the security management to keep check on the suspected security issues on the whole. There is the need to model the security requirements not only related to information, but it should make the organization's system secure from physical access, natural disasters, network, operational, and at applications' level threats.
## 3 Results and Discussion
Compared to Beijing, Baltimore presents similar ambient and mains  
Figure 5: Yearly solar radiation values for both the conventional and adsorption systems, with the corresponding maximum values of \(Q_{\rm saving}\) presented for each location. water temperature evolutions and only slightly higher solar radiation values. However, the fact that the solar radiation is higher during the summer months leads to a lesser difference between \(\mathrm{Q_{backup,conv}}\) and \(\mathrm{Q_{backup,ads}}\), thus resulting in a less prominent \(\mathrm{Q_{saving}}\) evolution during that period. The evolution for Lisbon is identical to that in Baltimore (Figure A2 in Appendix A), except for the lower \(\mathrm{Q_{saving}}\) values at each timestep (and thus the resultant annual savings--170.8 MJ in Baltimore versus 86.1 MJ in Lisbon), since the solar radiation and mains water temperature values tend to be higher in the Portugal's capital throughout the year. On the other hand, the temperatures and the solar radiation evolutions are very similar between Cape Town and Canberra (Figure A1 in Appendix A); however, the solar radiation is slightly higher in the summer months in Cape Town (especially between November and February), and the mains water temperature is also higher. This leads to a higher yearly solar radiation (Figure 5) and a less prominent (or even nonexistent) \(\mathrm{Q_{saving}}\) evolution during the summer period (Figure A2 in Appendix A), which accounts for a smaller annual value of \(\mathrm{Q_{saving}}\)--30.5 MJ in Cape Town compared to that of 73.3 MJ in Canberra. In order to further improve the system performance in the colder regions, where the energy savings are higher, one would need to modify the system configuration (which is out of the scope of the present study). In this regard, considering the parameters listed in Table 1, the system efficiency would possibly improve by increasing the solar collector area, since more solar energy would be gathered and stored during the periods with higher availability of solar radiation. However, additional care must be taken, since the adsorption module dimensions would probably need to follow this increment to be able to store the thermal energy surplus. This could also lead to the need of increasing both tanks' volumes, in order to fit the adsorption module. Therefore, this assessment may not be straightforward as it would seem to be, and further studies are required to properly analyze this effect.
## 6 Conclusions  
In this letter, we analyze the OP performance, EC, and average BER of a dual-hop FSO transmission system with fixed-gain AF relay over \(\mathcal{F}\) turbulence. Additionally, we present a tight asymptotic derivation in terms of elementary functions to obtain additional insights into the performance of the proposed system at high SNR. As a result, the simulation results match with the analytical ones for various SNR ranges. Consequently, when the effects of the pointing error and turbulence decrease, the performance can be significantly improved. Moreover, the diversity order is impacted by the pointing error parameter \(z\) and one turbulence parameter \(a\), but not by \(b\). The results also reveal that the Monte Carlo simulation of the analysis results is independent of the channel model for a dual-hop system when the parameters of the model are properly determined.  
All authors discussed the contents of the manuscript and contributed to its presentation. [PERSON] designed and implemented the proposed scheme, analyzed the simulation results, and wrote the paper under the supervision of [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Research Program through the National Research Foundation of Korea (NRF-2019R1A2C1005920).  
The study did not involve humans or animal studies.  
The study did not report any data.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AWGN & Additive white Gaussian noise \\ FSO & Free-space optical \\ CDF & Cumulative distribution function \\ OOK & On-off keying \\ PDF & Probability density function \\ SNR & Signal-to-noise ratio \\ AF & Amplify-and-forward \\ BPSK & Binary phase-shift keying \\ QPSK & Quadrature phase-shift keying \\ BER & Bit error rate \\ \end{tabular}  
Figure 7: Average BER versus \(\rho\) of \(\mathcal{F}\) and GG turbulence channel models under varying effects of pointing errors.
## 4 Analog Front-End
### Transmitter Stage  
The output buffer of the transmitter is directly connected to the node joining impedance matching network and receiving amplifier. By putting the buffer in a high-impedance state, the node can be released during signal reception, just being loaded by buffer's parasitic capacitance. This topology was implemented for minimum circuit footprint, although energy is dissipated into receiving amplifier in the case of signal transmission. The rectangular output signal with a peak-to-peak voltage of 1.8 V, i.e., a fundamental component of \(\hat{U}_{1}=1.15\) V according to a Fourier series decomposition, and the network's input impedance of 700 \(\Omega\) (conjugate complex matching to \(R_{\text{a,in}}\)) yield an output power of the transmitter being  
\[P_{\text{TX}}=\frac{\hat{U}_{1}^{2}}{2R_{\text{a,in}}}=0.94\text{ mW}. \tag{26}\]
## 3 Privacy and Security Threats in IoT Domain
### Homomorphic Encryption  
HE model, as proposed [23], is a type of encryption that facilitates performing computations on ciphertext. The final encrypted result, once decrypted, is identical to the plaintext result. It is useful for applications, especially IoT datasets, where data privacy is an issue, such as in smart home environments [19] where sensitive data are being generated and stored frequently. HE is classified into three different forms based on the complexity of the operations it supports:  
* Partially Homomorphic Encryption (PHE): Supports either addition or multiplication but not both.
* Somewhat Homomorphic Encryption (SHE): Supports limited addition and multiplication operations.
* Fully Homomorphic Encryption (FHE): Supports arbitrary addition and multiplication operations, enabling any computation on encrypted data.