esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 3 Different Channel Models  
This section discusses three different ways of modeling the average channel gain (\(\widetilde{\beta}_{il}\)) between AP location \(l\) and user location \(i\). Section 3.1 discusses a model that is based on the Euclidean distance between the locations. Section 3.2 introduces our proposed graph-based approach for modeling the channel gain. Section 3.3 calculates the channel gain based on channels that have been simulated with an RTS.
## 4 Engineering Applications
### Effectiveness of Data Augmentation  
Table 6 lists the comparisons between, with and without data augmentation. Decision tree is adopted to classify normal and cracking samples [23]. Three metrics are adopted to evaluate the performance, namely precision, recall and F1-score. Detailed metric computations can be found in the literature [24]. For normal samples, the three metrics with data augmentation are slightly lower than or equal to those without data augmentation. However, for cracking samples, these metrics are higher. For instance, 'Wind_35' achieves  
Figure 5: Four original samples and their reconstructed result.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper Parameter** & **Symbol** & **Value** \\ \hline Factor in (13) & \(\varepsilon\) & 0.001 \\ Scale factor in (13) & \(\gamma\) & 1 \\ Translation factor in (13) & \(\beta\) & 0 \\ Learning rate in (14), (15) and (17) & \(lr\) & 0.0002 \\ Weight in (16) & \(\alpha\) & 10 \\ Iteration Number & - & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Details of hyper-parameter values.  
precision (0.73), recall (0.76) and F1-score (0.75) with data augmentation, which are slightly lower than that without data augmentation (precision: 0.74, recall: 0.78, F1-score: 0.76). The recall value improves from 0.22 to 0.26 and F1-score increases from 0.23 to 0.25, implying the effectiveness of data augmentation. With the data augmentation approach, the average value of the cracking samples are 0.133, 0.135 and 0.135, meaning 19.8%, 23.8% and 22.7% improvement on these three metrics.  
We draw the results with data augmentation in histograms, as shown in Figure 8. For normal samples, these three metrics draw down. This is because too few cracking samples will be overwhelmed and nearly all these samples are classified into normal samples. Therefore, from Wind_5 to Wind_35, the recall value is bigger than precision (note that the F1-score is determined by the former two metrics). With an increase in the amount of cracking samples, some normal samples are misclassified into cracking samples, causing a small recall value. For cracking samples, with the imbalance ratio alleviating, all the three metrics increase. It can be concluded that RTAE is effective for improving the recognition of wind blade cracking samples.  
Figure 6: Loss value change during the iteration.  
Figure 7: Four original samples and their reconstructed results.
## 2 Methodology
### Periodontal Assessment  
Periodontal clinical assessment of all patients included bleeding on probing (BOP), clinical attachment loss (CAL) and pocket depth (PD) measurements following the criteria of the American Academy of Periodontology [27], which were used at the time. Accordingly, periodontally healthy patients (pilot test, Groups A1 and A2) were considered as those who  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Group A1** & **N** & **Baseline** & **2 Weeks** & **4 Weeks** \\ \hline \multirow{4}{*}{\#11} & Mean & 12 & 1.00 & 0.83 & 1.17 \\ \cline{2-5}  & Std. & & & & \\  & Deviation & & 2.045 & 1.642 & 1.267 \\ \cline{2-5}  & Median & & 0.50 & 0.50 & 1.00 \\ \hline \multirow{4}{*}{\#13} & Mean & 12 & \(-\)1.50 & \(-\)1.83 & \(-\)1.75 \\ \cline{2-5}  & Std. & & & & \\  & Deviation & & 2.111 & 1.749 & 1.545 \\ \cline{2-5}  & Median & & \(-\)1.50 & \(-\)2.00 & \(-\)2.00 \\ \hline \multirow{4}{*}{\#16} & Mean & 12 & 0.17 & \(-\)0.50 & \(-\)1.08 \\ \cline{2-5}  & Std. & & & & \\  & Deviation & & 2.329 & 2.067 & 2.021 \\ \cline{2-5}  & Median & & 0.00 & \(-\)0.50 & \(-\)1.50 \\ \hline \multirow{4}{*}{\#13} & Mean & **N** & **Baseline** & **2 Weeks** & **4 Weeks** \\ \cline{2-5}  & Mean & 11 & 1.18 & 1.00 & 0.91 \\ \cline{2-5}  & Std. & & & & \\  & Deviation & & 2.089 & 2.646 & 1.868 \\ \cline{2-5}  & Median & & 1.00 & 1.00 & 1.00 \\ \hline \multirow{4}{*}{\#16} & Mean & 11 & \(-\)0.64 & \(-\)1.36 & \(-\)1.45 \\ \cline{2-5}  & Std. & & & & \\  & Deviation & & 1.859 & 1.859 & 2.067 \\ \cline{2-5}  & Median & & \(-\)1.00 & \(-\)1.00 & \(-\)2.00 \\ \hline \multirow{4}{*}{\#11} & Mean & 11 & 1.09 & 0.27 & \(-\)0.55 \\ \cline{2-5}  & Std. & & & & \\  & Deviation & & 2.914 & 2.867 & 2.382 \\ \cline{2-5}  & Median & & 0.00 & \(-\)1.00 & \(-\)1.00 \\ \hline \multirow{4}{*}{\#13} & Mean & **N** & **Baseline** & **2 Weeks** & **4 Weeks** \\ \cline{2-5}  & Mean & 9 & 3.56 & 3.33 & 3.44 \\ \cline{2-5}  & Std. & & & & \\  & Deviation & & 3.127 & 3.428 & 3.812 \\ \cline{2-5}  & Median & & 3.00 & 3.00 & 3.00 \\ \hline \multirow{4}{*}{\#13} & Mean & 9 & 0.56 & 0.11 & \(-\)0.33 \\ \cline{2-5}  & Std. & & & & \\  & Deviation & & 3.283 & 2.713 & 2.915 \\ \cline{2-5}  & Median & & 0.00 & 0.00 & 0.00 \\ \hline \multirow{4}{*}{\#16} & Mean & 9 & 1.89 & 3.78 & 2.00 \\ \cline{2-5}  & Std. & & & & \\  & Deviation & & 5.036 & 4.868 & 5.244 \\ \cline{2-5}  & Median & & 0.00 & 4.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tooth mobility mean values and standard deviation for the 3 consecutive visits for Groups A1, A2 and A3 and for teeth #11, #13 and #16.  
displayed bleeding on probing <2%, no clinical attachment loss and pocket depth <3 mm. For Group A3, periodontitis was diagnosed, the subjects showed bleeding on probing >10% and a pocket depth >3 mm was detectable in \(\geq\)2 teeth. All patients participating in Group A3 were treated for periodontitis previous to participating in the study.
## 3 Results and Discussion
### Concrete FPZ Characteristics  
The internal and external characteristics of FPZ in concrete are identified by DIC and AE techniques quantitatively during the mode I fracture extension process. Although the measurement of FPZ length has attracted the attention of most researchers, the quantitative measurement of FPZ length has not yet been realized until now. The crack tip position and FPZ length is measured automatically and quantitatively by the DIC technique proposed in this presentation. The length of FPZ obtained by DIC and AE technique are in good agreement. The maximum of FPZ length is about 20 mm, the experimental results agreed with the hypothesis that the length of FPZ is about three times the maximum aggregate size [24].  
The internal micro-cracking process is monitored by AE technique. The cracking source of AE events are located precisely by the proposed location methods. The results show that the internal AE event propagation process is completely consistent with the displacement and strain localization process obtained by DIC method on the surface of concrete specimen. During the concrete crack extension process, the FPZ corresponds to the surface deformation localization band and the internal AE event band. The width of fracture process zone is 3 mm on the surface of specimen and 10 mm in the interior of specimen. It means the internal influence area of crack in concrete is larger than that of FPZ observed on concrete surface.  
The measurement of stress intensity factors at crack tip is usually a difficult problem during the FPZ extension process. The SIFs are derived from the displacement field around the FPZ tip by the DIC technique proposed in this presentation. The stress intensity factors indicate the distribution characteristics of the stress field around the FPZ. It should be mentioned that the emission energy  
Figure 12: Location of AE events in FPZ (Unit: mm).  
Figure 13: Strain localization ahead the crack tip (Unit: mm).  
monitored by AE sensor is a relative parameter, which can't be directly applied to quantify the real released energy of AE event. The comparative analysis of AE results based on deformation field and SIFs will help to explain and analyze the experimental results of AE in the FPZ of concrete.
# Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging
## 2 Gnlcsa
### Preprocess in Range Direction by Generalized Chirp Scaling
The difference between the broadside SAR and the squint SAR after the LRWC is that the nearest slant range of the latter also turns to \(\big{(}R_{0}+V_{r}\eta_{p}\sin\theta_{s}\big{)}\), which means that the Doppler rates of the squint SAR after the LRWC are azimuth variant. We define that:  
\[R_{LRWC}=R_{0}+V_{r}\eta_{p}\sin\theta_{s} \tag{6}\]  
With an inverse fast Fourier transform (IFFT) along range direction, we reach the signal of Equation (5) in 2D time domain:  
\[ \begin{array}{ll}s_{1}^{\prime}(\tau,\eta)&=\omega_{r}(\tau-2(R(\eta;R_{0})+ V_{r}\eta\sin\theta_{s})/c)\omega_{a}\bigl{(}\eta-\eta_{p}\bigr{)}\\ &\cdot\exp\Bigl{[}-j4\pi\frac{f_{0}}{c}\bigl{(}R(\eta;R_{0})+V_{r}\eta\sin \theta_{s}\bigr{)}\Bigr{]}\exp\Bigl{(}-j\pi K_{r}(\tau-2(R(\eta;R_{0})+V_{r} \eta\sin\theta_{s})/c)^{2}\Bigr{)}\end{array}  \tag{7}\]We can obtain the range history of the target \((R(\eta;R_{0})+V_{r}\eta\sin\theta_{s})\) after the LRWC, which is different from that of the raw squint data. As shown in Figure 2, the LRWC for squint data brings more benefits when processes the range cell migration (RCMC), range compression (RC), and the secondary range compression (SRC). In general, to reach the requirement of high-resolution in range direction, the pulse with wide bandwidth is needed in the SAR system. References [18; 21] explain in detail that the conventional CSA and RDA are not capable of processing the RCMC/RC/SRC with wide bandwidth. Due to the limited space, this paper will not repeat the methods of precise focusing along the range direction for the wide bandwidth SAR.
# Zeroize: A New Method to Improve the Utilization of 5G Networks When Running VoIP over IPv6
## 4 Zeroize Method Performance Analysis
### Channel Capacity (Ch-Capacity)  
The Ch-Capacity of the proposed Zeroize method is presented in this section. The Ch-Capacity was analyzed for each channel bandwidth between 100 and 1000 kbps. Losing the packets designates that the channel is saturated. Hence, the Ch-Capacity of a channel equals the number of calls afore losing the packets and is measured accordingly. Figures 4-6 explore the Ch-Capacity for the Zeroize method in comparison to the S-RTP, SVF, and ZSP methods with G.723.1, G.726, and LPC codecs, respectively. The Ch-Capacity when using the Zeroize method outperformed (i.e., more calls) the S-RTP method. The difference in the Ch-Capacity increased with the existing bandwidth. This condition was due to eliminating the payload by utilizing the superfluous field (source IPv6 address) of the header to carry the payload. However, the Ch-Capacity when using the Zeroize method underperformed (i.e., fewer calls) the SVF and ZSP methods. This condition was because of the header of the IPv6 protocol header being twice the IPv4 protocol header and consuming more bandwidth for each call. The size of the superfluous fields of the SVF and ZSP methods was more than that of the Zeroize method. The Ch-Capacity of the SVF and ZSP methods was the same as the LPC codec. This condition was because the size of the LPC voice data was 14 bytes, which was smaller than the superfluous fields of the SVF method (17 bytes) and the ZSP method (19 bytes). Therefore, the superfluous fields of the two methods could carry the entire 14-byte voice data of the LPC codec. The extra two-byte superfluous fields of the ZSP method were not utilized when using a codec with voice data (e.g., LPC codec) smaller than the superfluous fields.  
Figure 5: Ch-Capacity (G.723.1 codec).  
The Ch-Capacity was also calculated using mathematical equations to validate the results. The bandwidth needed by each call depends on the used codec and the packet header length. Equation (1) can be used to find the bandwidth needed by each call:  
\[C_{bw}=\frac{(L_{pld}+L_{h})*\left(\frac{C_{br}}{L_{pld}*8}\right)*8}{1000} \tag{1}\]  
where \(C_{bw}\) is the bandwidth needed by each call, \(L_{pld}\) is the packet payload length, \(L_{h}\) is the packet header's length, and \(C_{br}\) is the bit rate of the used codec. For instance, assume the G.728 codec is used with a bit rate of 16 kbps, and the IPv6 protocol is in the packet header. Then, the bandwidth needed by each call when using the Zeroize method is 96 kbps. On the other hand, the bandwidth needed by each call when using the S-RTP method is 112 kbps. Therefore, the saving in the Ch-Capacity is around 14.3% when using the G.728 codec. The saving in the Ch-Capacity varies from one codec to another, based on the voice data length of each codec.
## 5 Experiments
### Ablation Studies  
The observed performance enhancement of our algorithm can be primarily attributed to the strategic design of four essential modules: the Feature Construction Module (FCM), the Feature Learning Network Module (FLNM), the Node Feature Matching Module (NFMM), and the Geometric Consistency Discrimination Module (GCDM). To assess the effectiveness of these modules, we conducted ablation studies.  
Figure 6: Effects of initial translation distance on rotation error,translation error and running time.  
Figure 7: Registration result with initial translation error of 10 m and noise. The orange and blue boxes represent the two registered subgraphs, and the gray represents the overlapping area.  
In each ablation experiment, one module was systematically removed while the remaining modules were preserved, ensuring that all other experimental conditions remained constant to evaluate the contribution of each module. The Mean Squared Error of Rotation (MSE(R)) and the Mean Squared Error of Translation (MSE(t)) of the final registration outcomes were employed as metrics to assess the efficacy of each module. At the same time, in calculating the contribution ratio of each module, we use the indicator values of the complete model (Ours) as a benchmark, calculate the relative difference between each error indicator and the ablation model, and take the average based on the decrease ratio. After normalization, we obtain the contribution ratio of this module.  
The results of the ablation studies, as presented in Table 7, indicate that each of the designed modules contributes positively to the performance enhancement of the registration network. Notably, the Feature Learning Network Module (FLNM) exhibits an improvement of two orders of magnitude compared to a basic fully connected layer network module, underscoring the significance of learning features from neighboring nodes and associated edge features in enhancing the capability to identify corresponding point pairs. The experimental findings unequivocally demonstrate that these four modules play critical roles in the performance enhancement of the network to varying extents.
# Jointing Principles in AMC--Part 1: Design and Preparation of Dry Joints
## 4 Preliminary Finite-Element-Analysis
### Material Models and Properties  
The steel plates are regarded considering a linear-elastic stress-strain relationship with a Young's modulus of 200.000 MPa. The concrete is modelled with a non-linear material model to detect failure. Therefore, the \"total strain-based crack model\" (TSBCM) is used [29]. The TSBCM is based on an approach from the Model-Code 2010 [30] for compression, and it uses a brittle stress-strain relationship for tensile behaviour. Figure 9 shows the material models for compression and tensile behaviour. As the FE analyses are preliminary, the material \"tailored\" properties of 3D-printed concrete are not considered.  
The joint between the concrete elements is modelled as an interface with linear elasticity and Coulomb Friction [7]. As the material parameters of the concrete model are set as fixed, the input values for the interface model need a modification for dry joints. Figure 9 shows the Mohr Coulomb friction model.  
Figure 8: The geometric model for FE analysis: (**a**) full FE model (**b**) half FE model and (**c**) interface connection.  
Assuming that dry joints have no cohesion, parameter \(c\) equals zero. The friction coefficient of concrete is approximately \(\mu~{}=~{}[0.60\)-\(0.70]\) according to the literature [31] and assumes a smooth surface texture. The friction coefficient will be measured precisely in the experimental investigations depending on the subtractive post-processing (CNC, circular saw) and adapted later. However, for the first evaluations, it is set to \(\mu~{}=~{}0.70\). Furthermore, it is assumed that the dilatancy angle of the Coulomb Friction Model equals the angle of friction with \(\psi~{}=~{}\mu~{}=~{}0.70\), according to [32].  
The interface model also requires stiffness-moduli in perpendicular (normal) and horizontal (shear) directions. For a rigid connection, used in the vertical direction, the normal-stiffness-modulus is set to a value, which is higher than the concrete Young's modulus. For the shear-stiffness-modulus, the value selected is much smaller than in the vertical direction to represent a hinge. Table 9 lists the input values of the interface model.
## 4 Issues of Opportunistic Routing in Asynchronous WSN
### Control Packet Overhead  
Since the next hop will be selected dynamically in opportunistic routing, it is necessary to collect link quality information and other useful information for the calculation of routing matrices in order to prioritize the forwarder list and select the highest potential forwarder. However, in order to collect such information for calculations, the sender needs to use a large number of control packets. These extra control packet overheads are a large source of energy consumption. Moreover, after ranking, the forwarder list sender also needs to let the forwarders know about their rank and act accordingly. As such, there is additional overhead for these control packets. For some protocols, this calculation is performed by the sender, whereas for other protocols, this is performed by the sink. Nevertheless, in both cases, the protocols need to send control packets in order to let the neighbor nodes know the information, which affects the network and increases energy usage.
## 5 Multiplexing Readout  
Large arrays with thousands of pixels are nowadays routinely fabricated and the demand from future experiments is to scale to higher number of pixels, towards the ambitious target of 100 kilo-pixels or more. A further improvement of the existing cryogenic multiplexing read-out technology is a must, to reduce the dissipation from the SQUID amplifiers, the readout, and bias wiring, and to minimize the size of the interconnections and the harness complexity. It is not our purpose to discuss the details of the existing cryogenic multiplexing systems, which have been well reviewed in [15]. In this section, we will give a short overview of the state-of-the-art of the different multiplexing readouts. SQUID-based multiplexing techniques for TES bolometers and micro-calorimeters have been developed and operated at MHz frequencies using only few of the many orthogonal basis sets conceivable [12], such as time-division multiplexing (TDM) [9; 125], MHz frequency-division multiplexing (MHz-FDM)[12; 126; 127], and Walsh code-division multiplexing (CDM) [10; 125; 128]. The state-of-the-art of each of these techniques is currently limited by the practical implementation. The resources required by multiplexed signals will be independent of the applied basis set, when optimally dimensioned and with full signal loading. The choice of which multiplexing readout to use should be then based on the specific system requirements, such as, for example, cooling power, mass, volume, harness length, and electromagnetic interferences compliance.  
Microwave frequencies multiplexing schemes (GHz-FDM), using microwave SQUID multiplexing (uw-mux), is becoming a mature technology both for transition-edge sensors [11; 129] and magnetic microcalorimeters [130], in particular for ground-base applications.  
The number of pixels presently multiplexed in each multiplexing scheme is far below the fundamental limit set by the the information theory [131]. This is partially due to the fact that the cryogenic environment and the lithographic fabrication set stringent constrains that make it difficult to optimally implement any multiplexing architectures. Much R&D is still needed towards the optimization of the practical implementation of the multiplexing schemes in the focal-plane design to accommodate the complex cryogenic electronics. It is likely that for multiplexing a 100,000-pixels array, a hybrid multiplexing configuration will be required [132].
# Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
## 3 Results  
### Three-Study Merged Dataset Models  
Key results for the Three-Study Merged Dataset Models are given in Table 4.  
Differences in accuracy between models were explored. For this, the three-study merged dataset was split into training and test sets using an 80:20 ratio. The models were trained on the training dataset and accuracy on the test set (\(n\) = 731) was compared for the five classifiers. Exact McNemar tests (two-tailed) were then used to make all ten possible pairwise comparisons. To adjust for the number of comparisons, a p threshold of 0.01 was used. Using this methodology, there were no significant differences in accuracy between the three most accurate models (voting classifier, XGB and CatBoost). Likewise, there were no significant differences in accuracy between the two least accurate models (KNN and logistic regression). However, the three most accurate models (XGB, voting classifier and CatBoost) were all significantly more accurate than the two least accurate models (KNN and logistic regression).  
Hyperparameter tuning for the XGBoost resulted in the use of the following settings: number of estimators was 300, maximum depth = 5, learning rate was 0.01, base score was 0.5, the booster was the gradient boosting tree, minimum child weight was 5, regularization lambda was 0.8, regularization alpha was 0.3, column sample by tree was 0.5, and subsample was 0.8.  
Gini importance was computed for each feature, on the basis of the XGBoost model. Table 5 gives these importances, and also class distributions for each feature, which can aid the interpretation of these feature importances. The feature with the highest Gini importance was prostatectomy prior to baseline, which was much more common in good responders (70%) than bad responders (37%).  
A receiver operating characteristic (ROC) curve plots the tradeoff between the true positives rate and the false positives rate. The true positive rate is the proportion of positive cases correctly classified (also called'sensitivity' or'recall'). The false positive rate is the proportion of negative cases incorrectly classified as positive. The ROC curve for the Three-Study Merged Dataset Models is shown in Figure 2.