esa-sceva/satcom-chunk-collection huggingface dataset:
# Tensor Rank Regularization with Bias Compensation for Millimeter Wave Channel Estimation
## 1 Introduction
Through numerical experiments, we find that our method significantly improves rank estimation success at the expense of slightly more iterations. The rest of the paper is organized as follows. Section 2 briefly introduces some tensor basics. The system model is discussed in Section 3. In Section 4, we propose a novel CP decomposition-based joint method for mmWave channel estimation. The computational complexity of the proposed method and the conventional CP decomposition method without known rank are analyzed in Section 5. Simulation results are shown in Section 6. Conclusions are provided in Section 7. Notations: We use the following notations throughout this paper: \(y\) is a scalar, \(\mathbf{y}\) is a vector, \(\mathbf{Y}\) is a matrix, and \(\mathcal{Y}\) is a tensor. \(\mathbf{Y}^{\mathrm{T}}\), \(\mathbf{Y}^{\mathrm{s}}\), \(\mathbf{Y}^{\mathrm{H}}\), and \(\mathbf{Y}^{\mathrm{t}}\) are transpose, conjugate, conjugate transpose, and Moore-Penrose pseudoinverse, respectively. \([\mathbf{Y}]_{i,j}\) and \([\mathbf{Y}]_{:,j}\) are the \(i\)-th row and the \(j\)-th column of the matrix \(\mathbf{Y}\). \(\mathbf{A}\odot\mathbf{B}\), \(\mathbf{A}\odot\mathbf{B}\), and \(\mathbf{A}\odot\mathbf{B}\) denote the Kronecker product, the Hadamard product, and the column-wise Khatri-Rao product. \(\mathsf{a}\mathsf{c}\mathsf{b}\) denotes the outer product, which is also known as the tensor product. Let Rank (\(\mathbf{Y}\)) and \(k_{\mathcal{Y}}\) denote the rank and Kruskal-rank of a matrix \(\mathbf{Y}\), respectively. Let \(\mathrm{Re}(\mathbf{Y})\) and \(\mathrm{Im}(\mathbf{Y})\) denote the real part and the imaginary part of \(\mathbf{Y}\). \(\mathbf{d}(\mathbf{Y})\) denotes a vector of diagonal entries of \(\mathbf{Y}\), and \(\mathbf{D}(\mathbf{y})\) denotes a diagonal matrix constructed from \(\mathbf{y}\).
## 3 Results and Discussion
### OCGA with Gradient Structure for Efficient Microwave Absorption
Firstly, the large opening cells at the bottom of OCGA-G can improve the impedance matching between the surface and the air and reduce the reflection of microwave, which make large amounts of microwave enter the aerogels (Figure S8). Then, with the transmission of microwave into interior OCGA-G, the smaller, packed graphene cells provide more reflection interfaces, which gradually increase dielectric loss according to previous multiple absorption mechanism. To sum up, by means of good gradient impedance and enhanced microwave loss capability, the ultralight OCGA-G shows a lower value of RL and smaller matched thickness than the others. Figure 8: Effect of cell distribution on the microwave absorption performance. (**a**) Schematic description of OCGA-G. SEM images of (**b**) top region, (**c**) middle region and (**d**) bottom region of OCGA-G. (**e**) RL of OCGA-m. (**f**) RL of OCGA-P. (**g**) RL and (**h**) EAB of OCGA.
## 1 Introduction
The use of interactive machine learning has also been proposed, eliciting human preferences for preserving some attribute values over others for anonymization [17]. The topic of differential privacy has been investigated by comparing two differential privacy algorithms and evaluating the results by applying three machine learning algorithms to anonymized and raw data [18]. In addition, privacy-preserving protocols for three classifiers have been proposed [19]. The effect of anonymization algorithms on the performance of machine learning classifiers has been explored in several studies [20; 21; 22]. A novel anonymization algorithm, information-based anonymization for classification given k (IACk) based on normalized mutual information was introduced in [20], the effect of which on the performance of machine learning models was tested using decision trees, naive Bayes, logistic regression and SVM. Another anonymization algorithm (non-homogeneous generalization with sensitive value distribution, NSVDist), based on an information loss metric was introduced in [21]. The authors compared NSVDist with the Mondrian [23], privacy-aware information sharing (PAIS) [24] and sequential anonymization (SeqA) [25], and evaluated their effect on the performance of the naive Bayes, SVM, W-J48 and W-JRip classifiers. A comparison of the anonymization algorithms Mondrian, optimal lattice anonymization (OLA) [26], top-down greedy anonymization (TDG) [27] and the k-nearest neighbor clustering-based anonymization method [28], regarding their impact on the performance of the k-NN, SVM, XGBoost and random forest classifiers, was made in [22]. Since the purpose of some of these studies has been the introduction of novel anonymization methods (IACk [21], NSVDist [22]), it can be noted that the effect of anonymization methods on machine learning performance has become an important consideration for their evaluation and validation. The object of this paper was the application of machine learning algorithms to a clinical dataset that had been subjected to anonymization using the Mondrian algorithm with various parameter values. The concept of this paper and the dataset used originated in the MODELHealth project, the main object of which has been the development of a software platform for the harmonization and anonymization of electronic health record data, with the goal of utilizing them as input to machine learning models [29,30].
## 3 Results
### EMI-Based Monitoring
#### 3.2.2 Beam \"B200a-J\"  
Figure 13 depicts the shear stress of the \"B200a-J\" in terms of mid-span deflection and the crack width acquired by the crack meter transducers. As it was designed, the beam exposed brittle shear failure, forming a feature critical diagonal crack on the right span. One crack meter transducer is placed in the middle of the beam, monitoring the propagation of flexural cracking, while the other two are attached to monitor the widening rate of the right and left shear diagonal cracks. Figure 14 presents the crack meters' locations and the cracking pattern of the beam at each loading level. Figure 13 also depicts in a red circle the values of shear stress and mid-span deflections, at the time of the experimental voltage measurements, through the EMI-based method (damage states).  
Further, for \"B200a-J\", seven EMI measurements were conducted. The initial measurement was conducted in the healthy state (pristine condition), while the six subsequent were acquired during the loading augmentation and cracking development. Figure 14 mentions these states as \"Dam1-Dam6\", respectively. According to the illustrated cracking pattern, obviously, the beam failed in the right span. Thus, according to their positions, the PZTs with the highest involvement are SA3, SA4, J3, and J6. Figure 15 presents comparative typical voltage response curves in terms of the set frequency range for PZTs J3 and J6.  
Figure 12: Voltage responses for beam “B500-J”.  
Figure 14: Cracking pattern of beam “B200a-J”.  
Figure 15: Voltage responses for beam “B200a-J”.  
Figure 13: Mechanical response for beam “B200a-J”, EMI measurements, and crack meters.  
From the observation of Figure 15, apparently, the PZT positioned on the failure side shows significant alterations in the voltage response curves with significant peak and curve shifting. Further, the curves' variations commence at early damage states (Dam1), which is an essential indicator of the method's effectiveness in detecting the possibility of slippage, the occurred damages, and the structural degradation of the retrofitting scheme (PZT J3).  
As presented also in Figure 13, the development of the fatal shear right crack starts at Dam2, while Dam3 starts the recording of the widening. Between the 4 th and 5 th damage states there is an abrupt increase from approximately 1.5 mm to 6 mm, which is reflected also by the cracking pattern pictures of Figure 14. In addition, right after the completion of Dam5, a second abrupt increment of the crack width was observed (from 6 mm to \(\sim\)12 mm). Thereafter, concrete spalling of the jacket's right shear span occurred and the transducer had been protectively removed.
## 3 Cross-Layer Design for Mobile _Ad Hoc_ Networks  
The characteristics of wired networks allow us to design a TCP/IP protocol stack with strict boundaries and to design protocols at different layers independently. However, this design is not flexible enough to cope with the characteristics and restrictions of MANETs [17, 18].  
Cross-layering means the interaction of a layer with any other layer in the protocol stack. The _ad hoc_ research community recognizes that cross-layering can provide significant performance benefits. A cross-layer design may eliminate redundant tasks found on adjacent layers and benefit from the stack wide layer interdependencies to optimize overall network performance. Recent studies reveal that cross-layer integration result in significant improvement in terms of energy efficiency [19, 20].  
A cross-layer design applied to service discovery refers to service discovery solutions integrated into a routing protocol that disseminate service discovery and route discovery messages throughout the network at the same time. Traditionally, service discovery is placed at the application layer, arguing that cross-layer solutions violate a modular layered approach. An application layer service discovery solution means that clients must first discover the server that provides the service they desire, and then, once clients know the address of the server, they must initiate a separate routing process in order to obtain a route to the service provider. This means that two separate processes are required for any client intending to access a service, a service discovery and a route discovery process. Both of them would require the exchange of a large number of messages. In an _ad hoc_ network, each message sent through the network consumes significant network bandwidth, as well as computation and battery power.  
Figure 3: MANET message format.  
Cross-layer service discovery solutions reduce significantly the amount of transmitted messages and the consumed bandwidth in the network [21, 22, 23]. A node requesting a service discovers the service and the route to the service provider at the same time. Cross-layer service discovery solutions also reduce the time to access a server, since the discovery of the server and the route to reach the server are obtained at the same time and not sequentially, as in the application layer service discovery approach.
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 7 Drone Detection  
### Experiment Description  
The experiment was carried out at the campus of the Technical University of Lodz, Poland, owned by a cooperative 5G network used as a source of illumination for passive radar. The main idea of the measurement was to record the signal reflected from a cooperative target in various scenarios possible in a 5G-network-based passive radar. The measurement setup and target are shown in Figure 18. The target of interest was a drone (DJI M600 PRO), illustrated in Figure 18b. Its size was ca. 1 m in width and length, ca. 40 cm in height and was made of plastic. The target's RCS, for the operating frequency of about 3.44 GHz, is not known and needs to be characterized [45]. For measurements, the authors used a dual-channel radar receiver whose antennas (connected to corresponding channels of the recorder) were mounted as in Figure 18b. The reference antenna was pointed towards the 5G base station. The second antenna was observing the illuminated space where the drone was flying, as shown in Figure 18c. The surveillance antenna was hidden behind the wall to minimize the influence of the reference signal in the surveillance channel (DPI leakage). As shown in Figure 19, the surveillance channel was composed of a relatively high gain amplifier with a gain above 20 dB. Thus, the antenna position was dictated to avoid the signal saturation resulting from the presence of the transmitter at a relatively low range. The reference channel has a gain of ca. 10 dB. Both signals are  
Figure 17: Probability of detection as a function of signal filling, \(P_{\mathrm{fa}}=10^{-8}\).  
passed through to the SDR based on the Ettus USRP X310, digitized and stored on the hard drive. To ensure frequency synchronization, the GPS signal is used. Data storage is carried out using a mid-class computer with an Intel Core i7-9700 2.66 GHz processor, 32 GB RAM, SSD drive (separate drive for each channel) and the Ubuntu operating system. The data were processed off-line after signal reception. The drone was recorded during several maneuvers, and the selected results are illustrated in the further part of this section.  
Figure 18: Drone and the recorder during the trials with the drone trajectory superimposed on the map. (**a**) Cooperative target. (**b**) Measurement setup. (**c**) GPS trajectory of the drone. The red dashed arrows indicate the drone’s direction of flight.
## 6 Results and Discussion
### Prediction Error Analysis  
The localization prediction error is categorized as: (1) poor recognition pointing to the same room and (2) poor recognition pointing to a different room. Poor recognition pointing to the same room means that the learning model predicted a different reference point, but in the same room wherein the actual true reference point exists, whereas poor recognition pointing to a different room means that the predicted reference point is in a different room to where the true reference point exists.  
For the proposed augmented CWT Morse-based feature set, the poor recognition pointing to a different room accounts for 4.7% of the total number of dataset instances, while the poor recognition pointing to the same room instances account for 0.2% of the total number of dataset instances. The summarized analyses for the entire feature set formulations and deep learning models are presented in Figure 9(a),b.  
In Figure 9(a), the proposed CWT using CNN generates the least error for out-of-room predictions. This result is for the Morse-based feature set. This implies that the number of predicted reference positions which are different from the actual reference positions are the least compared the the other feature sets. The augmented CWT-PST using CNN offered the highest number of wrongly predicted positions. This is mainly because the larger the size of the dataset, the greater the number of errors, yet this factor may not affect the accuracy. However, in the in-room predictions, the unaugmented CWT-PSD using CNN offers the least prediction error compared to all the other feature sets. Its prediction error performance appears better than that of the proposed augmented CWT features. However, although the number of wrongly predicted positions for the proposed augmented CWT is  
Figure 10: (**a**) Morse and (**b**) Morlet-based prediction error results.  
higher, they appear in the same room. This implies that the localization error performance for the proposed CWT is better than that of the unaugmented CWT using CNN.  
The Morlet-based prediction error results in Figure 10b show that the proposed augmented CWT using CNN offered the lowest prediction error, implying that it had the least number of wrongly predicted reference positions which are outside the actual room. The augmented CWT-PSD using ANN performed the worst in prediction error, implying that it had the highest number of predicted reference points outside the actual room.  
The in-room predictions show that the proposed augmented CWT features was still the best performer in terms of prediction error, followed by the unaugmented CWT using CNN. The observations for both in-room and out-of-room predictions show that CNN is the best learning model for prediction of indoor positions with minimum error.  
For brevity, only two confusion matrices generated for specific reference point prediction are presented. The two confusion matrices shown in Tables 8 and 9 are the ones generated by the proposed augmented CWT feature dataset based on the CNN model and the CWT feature dataset based on the CNN model without augmentation.
# An Experimental Evaluation of Path-Based Product Line Integration Testing and Test Coverage Metrics
## 5 Experimental Evaluation of the Method
### Answers to Research Questions  
Through two experiments I obtained the following answer to each RQ:  
* _The answer to RQ1_, MM-path-based SPL integration testing eliminates redundant testing. All xx-MM-paths identified in Experiment 1 of Section 5.2 are independent and do not require retesting in each product testing if they are covered during platform testing based on test coverage decisions. MM-path-based integration testing fundamentally prevents redundant testing. Additionally, MM-path-based SPL integration testing simplifies test coverage measurement and resolves redundancy issues. As demonstrated in Experiment 1 of Section 5.2, the proposed method and metrics provide clarity regarding the scope of platform testing and product testing based on test coverage determination. In summary, MM-path-based SPL integration testing facilitates easy test coverage measurement.
* _The answer to RQ2_: As depicted in Figure 5, cc-MM-path coverage yields the highest benefit, while vv-MM-path coverage results in the lowest benefit. Specifically, the cost of cv-MM-path coverage was higher than that of testing in a stand-alone manner due to the increased expense associated with developing stubs and drivers when adopting cv-MM-path coverage. In contrast, adopting vc-MM-path coverage requires only the development of drivers. Consequently, the cost of vc+cv-MM-path coverage is lower than that of cv-MM-path coverage because vc-MEPs of vc+cv-MM-paths help offset the cost incurred by cv-MM-paths.
* _The answer to RQ3_: MM-path-based SPL integration testing efficiently supports testing method calls across different modules with a reduced test set. In Experiment 2 of Section 5.3, applying the proposed method and metrics to the elevator product line using defined xx-MM-paths demonstrates that test cases cover all method calls without redundancy. This is contingent upon the test input adequately covering method invocations while considering interactions that may lead to defects.
## 2 Materials and Methods
### Extraction Procedures  
The LAB strains were grown in MRS broth at 37 \({}^{\circ}\)C for 48 h under anaerobic conditions. The broths were centrifuged at 13,000\(\times\)\(g\) for 7 min and the supernatants were filtered (filter pore size 0.22 \(\upmu\)m) to prepare cell-free supernatants (CFSs). A non-inoculated MRS broth medium (no bacteria added) was used as a blank matrix (negative control).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Standard Compounds** & **UV (nm)** & **Retention Time (min)** \\ \hline (A) 4-Hydroxybenzoic acid & 251, 269, 344 & 19.3 \\ (B) DL-p-Hydroxyphenyllactic acid & 228, 274, 395 & 16.3 \\ (C) Phenyllactic acid & 216, 255, 394 & 28 \\ (D) 3-(4-Hydroxyphenyl) propionic acid & 235, 277, 342 & 26.4 \\ (E) Hydrocinnamic acid & 220, 255, 315 & 38 \\ (F) Methylcinnamic acid & 251, 270, 293 & 41.3 \\ (G) Salicylic acid & 235, 302, 386 & 36 \\ (H) 1,2-Dihydroxybenzene & 229, 271, 390 & 15.7 \\ (I) 3,4-Dihydrocinnamic acid & 222, 324, 314 & 21.7 \\ (I) Vanillic acid & 253, 268, 294 & 21.3 \\ (K) 3,4-Dihydroxyhydrocinnamic acid & 222, 255, 323 & 45.3 \\ (L) Ferulic acid & 225, 234, 322 & 28.7 \\ (M) Benzoic acid & 230, 273, 388 & 34.2 \\ (O) 4-Hydrocinnamic acid & 227, 239, 310 & 27.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Maximum absorbance (\(\uplambda_{\max}\)) of 14 standard compounds determined by HPLC-UV/DAD.
## 3 Results
### Implementation Process and Results
MDM integration via the REST API was achieved as planned thanks to the active support by MDM operators and provided seamless access from the annotation interface to the more than 500,000 annotated data elements available in MDM. Annotations in the MDM portal are performed using UMLS codes. To check for existing annotations in the MDM portal while searching terminologies in the Collaborative Annotation Tool, a selected search result is translated to a set of UMLS codes using the UMLS Metathesaurus. If at least one UMLS code exists, the web service of the MDM portal is called using its RESTful API and provides all data elements annotated using this set of UMLS codes. The annotated data elements from the MDM portal are added to the table with the known annotations as displayed in Figure 3.  
Support for cross-terminological annotation using existing inter-terminology mappings was successfully implemented using the UMLS Metathesaurus as a mapping source. UMLS describes the medical terminology using UMLS concepts and relationships between them. A single UMLS concept describes a single medical term and contains atoms, which are codes from different terminologies that are semantically equivalent. Codes that are semantically equivalent to already annotated codes can thus be shown to the user in the CoAT UI. This feature is made accessible to the user with the cross-mapping dialogue shown in Figure 4. When a user annotates a code to a medical data item, the code is looked up in the UMLS Metathesaurus. If a UMLS concept containing this atom and other atoms from the supported terminologies is found, a button to open the cross-mapping dialogue is enabled. The dialogue shows all available semantically equivalent codes and allows the user to add them as annotations.
## 3 Results and Discussion
### AE Behaviors and Fatigue Crack Growth Mechanism
Fatigue damage before the crack tip--including slips, dislocations, fractures between crystals bonds, and so on--is caused by cyclic plastic deformations. Fatigue damage before the crack tip is the main factor leading to the further advancement of the crack front. The fatigue damage accumulation process releases around half of the total energy. When fatigue damage reaches critical status, the critical area of the material along the crack growth distance cannot bear the stress and ruptures in a brittle manner owing to a large number of tensile crack fractures and a small number of shear crack fractures; then, the crack tip advances a distance. The energy released by a single tensile crack fracture is quite small, but a large number of tensile crack fractures make the total accumulated energy slightly less than the energy released by the fatigue damage. The energy released by a single shear crack fracture is larger than that released by a single tensile crack fracture, but the total accumulated energy is the least. Moreover, it is worth noting that shear crack fractures are rarely observed in fatigue crack growth tests with CT specimens. Shear crack fractures appear as a discontinuity in acoustic emission signals, and a large amount of energy is carried by each signal. The mechanism of shear crack fractures is introduced in the next section.