esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of the Fatigue Life of a Bridge Crane Girder Using S-N Method
## 4 Discussion  
The assessment of the girders of the bridge crane 22/5 t-28.5 m manufactured in 1970 consisted of inspection of the structure, strain gauge measurement, stress state assessment, and assessment of fatigue damage accumulation.  
* Inspection of parts of the steel support structure revealed that the welded joints (accessible from the footbridge) did not show any flags of damage and were without significant corrosion. The auxiliary steel elements on the bridge and the crab, footbridge and protective railing were intact. It could be concluded that the visual inspection did not reveal any corrosion.
* Strain gauge measurements showed that the stress increments and the stresses from self-weight and other effects did not exceed the allowable stress values. The experimentally determined dynamic lift coefficient was less than that specified in STN 270103 standard. Based on the strain gauge measurements analysis, it could be concluded that the girders of the bridge crane were satisfactory in terms of strength.
* The assessment of the fatigue damage accumulation showed that under the operating modes considered, the current technical state of the crane's steel structure had a sufficient fatigue life reserve.
* The differences in the values of the normal stresses determined from the strain gauge measurements at the center of the girders A (location A1) and B (location B3) of the bridge crane were, in some cases, greater than 50%. This phenomenon occurs regularly when the crab passes through the center of the girder. It is due to the attachment of the rails to the girders. Although on girder B, the rail near locations B3 and B4 was welded to the top flange, even at these stress levels, girder B was satisfactory in terms of both strength and fatigue life. Since there was a significantly higher level of damage in the areas around the welded rail, the authors recommended that the rail attachment to the girders be carefully checked when the rails were replaced.
* The effect of welding the rail to the top flange of the girder was evident from a comparison of the measured normal stresses on girders A and B (Figure 13). This welding significantly increased the damage level in the corresponding cross-section, i.e., reduced the fatigue life of girder B.
* The stress values at locations A1 and A2 on girder A and at locations B3 and B4 on girder B were different during all operational measurements, although their nature was the same. This fact confirmed the deformation (twisting) of the girders' cross-section and the crab's associated slope, i.e., the height difference of the girders. For this reason, it was also recommended to check the wear of the crab wheels.
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 4 Fundamental Limits of Caching: A Cross-Layer Perspective
### Memory Costs  
As noted previously, a cost of caching is increased memory cost, which has to pay the memory cost, which can be reduced by efficiently reusing memory in the time domain. The memory cost is determined by not only how many bits are cached, but also how long they are cached. Memory is wasted if a content item is cached much earlier than being requested or evicted too late after being unpopular. Unfortunately, due to the lack of the request time prediction, how to reuse memory efficiently in the time domain has long been ignored.  
A queueing model is formulated in [21] to reveal the limits memory cost and determine whether and how long a content item should be cached based on RDI. Little's Law is adopted to rigorously characterize the relationship between the memory cost and the caching time, i.e., the average memory consumption is the product of the mean caching time and the content arrival rate \(\lambda\). Given the maximum caching time \(t\), the hit ratio is given by \(r=P(t)-P(0)\). Hence, the memory cost and the hit ratio are related as  
\[s(r)=\lambda\int_{0}^{P^{-1}(r+P(0))}xdP(x)+(1-r)P^{-1}(r+P(0)). \tag{1}\]  
Equation (1) provides an important insight on the caching policy design, i.e., fixing the maximum caching time is suboptimal when Equation (1) is non-convex. Instead, using different \(t\) in a time-sharing manner achieves a smaller memory cost characterized by the lower envelope of Equation (1), as shown in Figure 3. In practice, content items may have different RDIs, with which the maximum caching time for each content class needs optimization. An online algorithm is developed based on the method of Lagrange multipliers to maximize the overall memory efficiency without content placement knowledge. The queueing approach can be also adopted to realize memory-efficient edge caching at BSs [22].  
Memory scheduling becomes more challenging in the following three scenarios. First, memory-efficient scheduling with coded caching remains open because the hit ratio of coded caching is still unknown. Second, the hit ratio can be increased by dropping less popular items when the memory is full. This makes the eviction policy more complicated [24]. Finally, the joint scheduling of memories and wireless links generalizes the concept of cross-layer design by involving both the communication and memory units. Deep learning and deep reinforcement learning are expected to play key roles in dealing with the dynamic nature of user requests and radio environments [33; 34; 35].
## 3 Materials and Methods
### Class Association Rules  
This subsection focuses on finding \"interesting\" associations between attributes and class labels in the dataset: the generation of strong CARs. The generation of ARs (CARs) is expressed in two steps: (1) finding the frequent itemsets (the itemsets that satisfy the user-specified conditions) in the dataset and (2) association rule generation from frequent itemsets. While the second step is straightforward, the first step is extremely crucial in the construction of strong CARs. Before generating the CARs, we describe the basic rule evaluation metrics, which we use later. Rule-evaluation metrics for the rule \(\{A\Rightarrow B\}\) are expressed below:  
\[supp\ (A\Rightarrow B)=supp\ (A\cup B)=\frac{supp\_count\ (A\Longrightarrow B)}{T} \tag{1}\]  
where \(supp\_count\ (A\Longrightarrow B)\) is the number of records that include both \(A\) and \(B\). \(T\) is the total number of records.  
Figure 1: Overview of the developed associative classification algorithm.  
Confidence of the rule is a percentage value that shows how often each item in \(B\) appears in records that contain items in \(A\). It computes the strength of the rule, that is, the correlation between antecedent and consequent.  
\[conf\ (A\Rightarrow B)=\frac{supp\ (A\cup B)}{supp\ (A)} \tag{2}\]  
The lift of the rule is computed by dividing the confidence by the expected confidence. The expected confidence is the support of the consequences of the rule.  
\[lift\ (A\Rightarrow B)=\frac{conf\ (A\Rightarrow B)}{supp\ (B)} \tag{3}\]  
A dynamic algorithm called \"APRIORI\" is utilized to generate the class association rules. APRIORI is significantly more time-efficient compared to other frequently used mining algorithms, easy to implement, and sound enough to make fair comparisons with other AC methods. APRIORI uses a bottom-up approach that generates a 1-frequent itemset (includes one item only), whose support is greater than the user-specified threshold in the initial stage. In the next step, the 2-frequent itemset is found based on the 1-frequent itemset, and this procedure lasts to produce all the frequent itemsets. The APRIORI algorithm was simplified in our case because we generated the frequent itemsets containing class attributes.  
Class association rules are generated from frequent itemsets found by APRIORI, which satisfy a user-defined minimum confidence threshold. Since the APRIORI generates frequent itemsets containing class attributes, the CAR-generation step is straightforward. For each itemset \(F\) containing Class \(C\), all nonempty subsets of \(F\) are generated, and then every nonempty subset \(S\) of \(F\), strong CAR \(R\), is outputted in the form of \"\(S\)\(\rightarrow\)C\" if, \(Confidence\ (R)\geq min\_conf\), where \(min\_conf\) is the minimum confidence threshold.
## 2 Materials and Methods  
### Laser Setup  
A schematic of the experimental setup is displayed in Figure 1. Two blue multimode pump diodes (Nichia, NUBM07E, 465 nm) were each collimated with a f = 4.2 mm aspheric lens and shaped by a single cylindrical lens telescope by vertical stacking (not shown). Two additional green diodes were usually used with the system, but their beam transport path was blocked for the purposes of this experiment. One of the blue diodes was overlapped with the second harmonic output (532 nm) of a pulsed Nd:YAG laser (Edgewave GmbH, BX-611e) using a dichroic mirror. According to manufacturer specifications, this pulsed laser has a minimum pulse duration of 1 ns, high beam quality with M\({}^{2}=1.1\) and a maximum pulse repetition rate (PRR) of 100 kHz. During first tests the counter-propagating blue diode was damaged by the remaining transmission of the YAG through the crystal, even while using one dichroic mirror for green wavelength rejection in its path. So after replacement, one additional dichroic filter was installed to protect it. We assume that even after considering 90% absorption in the Ti:sapphire crystal, a separation ratio of >1/1000 is required for safe operation due to the high peak intensity of the YAG laser. Both diodes and the Nd:YAG laser were focused into the Ti:sapphire crystal using f = 60 mm achromatic lenses.  
The folded bow-tie cavity uses a 4 mm thick Brewster-cut Ti:sapphire crystal (GT Advanced technologies, FOM = 150). Curved mirrors with a radius of curvature of 75 mm are positioned at an angle of incidence of 8 degrees to compensate the astigmatism introduced by the crystal. As the acute angle of reflection would necessitate a very long-stretched cavity geometry to accommodate other optical mounts, two folding mirrors attached to piezo actuators pick up the laser light close to their edge and redirect the beam. To achieve unidirectional emission from the ring-laser an optical diode based on a 3 mm thick TGG crystal placed in a cylindrical Neodymium ring magnet and an adjustable \(\lambda/2\) waveplate are installed. Frequency selection is performed with a three-plate birefringent filter (Sirah  
Figure 1: Optical layout of the laser system.  
Lasertechnik) and self-made etalon (R = 4%, FSR \(\approx\) 18 GHz). Assembly of the air-spaced etalon was performed by aligning two single-side AR-coated wedged windows inside a laser cavity and gluing them onto a ring piezo actuator so that the uncoated sides were parallel to each other. Cavity mirrors were HR-coated for 700-950 nm at 0-20\({}^{\circ}\) AOI (R = 99.9% and T > 0.05%) and highly transmissive for 460-520 nm. An output coupler (OC) with T = 2% from 700-900 nm was used. While this OC does not maximize the output efficiency of the system, it allowed for a wide wavelength tuning range of >160 nm in CW mode, if using all four pump diodes.
## 3 Results
### Classification Results on Hold-Out Training Set  
The training set of hold-out validation sampling was made up of 635 clinically negative patients with 421 negative sentinel lymph node cases (control cases) and 214 positive cases at postoperative histological survey.  
Figure 1 shows the performance of the three classifiers considered in terms of AUC value (median, first and third quartiles' values) at increasing the number of features selected on the hold-out training set (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(n\) **Patients** & \(n\) **Positive** & & \(n\) **Patients** & \(n\) **Positive** \\ \hline Overall & 907 & 296 & ER & & \\ Age \$ & & & Negative & 94 & 29 \\
21–40 & 33 & 14 & Positive & 813 & 267 \\
41–50 & 128 & 46 & PgR & & \\
51–60 & 151 & 50 & Negative & 202 & 65 \\
61–70 & 164 & 54 & Positive & 705 & 231 \\
71–80 & 114 & 33 & Kief7 \({}^{\star}\) & & \\ \(>\)80 & 45 & 17 & Negative & 518 & 148 \\ Diameter \({}^{\star}\) & & & Positive & 389 & 148 \\ T1a & 46 & 6 & HER2/neu & & \\ T1b & 191 & 30 & 0 & 676 & 227 \\ T1c & 420 & 132 & 1 \({}^{\star}\) & 123 & 33 \\ T2 & 250 & 128 & 2 \({}^{\star}\) & 59 & 24 \\ Grading \({}^{\star}\) & & & 3 \({}^{\star}\) & 49 & 12 \\ G1 & 274 & 62 & Multiplicity \$ & & \\ G2 & 418 & 150 & Negative & 710 & 205 \\ G3 & 215 & 84 & Positive & 197 & 91 \\ Histologic & & & In situ component & \\ type \$ & & & & \\ Ductal & 704 & 246 & Absent & 397 & 133 \\ Lobular & 147 & 41 & Present & 510 & 163 \\ Special & 56 & 9 & & \\ type & & & & \\ \hline \hline \end{tabular} \({}^{\star}\)_p_-value Spearman Test \(<\) 0.05; \({}^{\$}\)_p_-value Chi-Square Test \(<\) 0.05; \({}^{\$}\)_p_-value Mann–Whitney Test \(<\) 0.05.  
\end{table}
Table 1: Observed number of patients with positive sentinel lymph nodes according to the considered prognostic factors.  
Figure 1: AUC distribution of the three classification models trained on a hold-out training set by increasing the number of features according to our feature selection order as indicated in the table. The solid line stands for the median yielded by 100 ten-fold cross validation, while the shaded region corresponds to the interquartile range.  
The logistic regression classifier seems to always achieve slightly higher performances than the others, although overcoming the Naive Bayesian classifier of a few percentage points. The best performance was reached with seven features for the Naive Bayesian and logistic regression classifiers, whereas eight features were needed for the RF classifier.  
In Table 3, we report the performances obtained for each classifier by using the subset of features that maximized performance. Although the performances were comparable in terms of accuracy, the logistics regression classifier reached an accuracy of 71.5% and showed a better trade-off between sensitivity and specificity than the other two classifiers (69.4 and 66.9%, respectively) when seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status, were considered.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 2 System Model and Problem Formulation
### Sensing Model  
As shown in Figure 1b, each GBS frame consists of \(N\) symbol periods and the UAV frame is synchronized with the GBS frame. Each symbol period is \(1/B\) seconds. To avoid interference with GBS communication, the UAV uses a periodic sensing scheme to determine the state of the GBS. In each UAV frame, \(N_{s}\) symbol periods are used to sense the state of the GBS and \(N-N_{s}\) symbol periods are used to transmit information to the secondary users when the GBS is idle.  
We assume that the UAV employs the energy detection method for spectrum sensing. Since the sensing is not perfect, the sensing results may not be correct. Based on [34], for a given detection probability \(P_{d}\), the false alarm probability \(P_{f}\) can be calculated as  
\[P_{f}=\mathcal{Q}\Bigg{(}\sqrt{2\gamma_{p}+1}\mathcal{Q}^{-1}(P_{d})+\gamma_{ p}\sqrt{\frac{N_{s}f_{s}}{B}}\Bigg{)}, \tag{1}\]  
where \(f_{s}\) is the sampling frequency at the UAV, \(\gamma_{p}\) is the signal-to-noise ratio (SNR) of the sensed GBS signal at the UAV, and \(\mathcal{Q}(\cdot)\) is the Gaussian function, with \(\mathcal{Q}(x)=\int_{x}^{\infty}e^{-(t^{2}/2)}\,dt/\sqrt{2\pi}\). Since \(P_{f}\) is determined by \(N_{s}\), we use \(P_{f}(N_{s})\) to replace \(P_{f}\).
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 3 Clustering Metrics
### Silhouette Index (SI)  
The Silhouette Index (SI) is a metric used to assess cluster validity. Its value can vary from (\(-1<SI<1\)). It measures how similar observed data is to other observations in its group compared to the samples allocated to the group closest to it. The \(SI\) values close to \(-1\) indicate the data was erroneously entered in the target group, while values close to zero show the data could be in its target group as well as in some other group. \(SI\) values close to 1 indicate that the data are correctly allocated [10; 69; 70].  
Equation (19) presents the formula for calculating the \(SI\) Index:  
\[SI=\frac{1}{N}\sum_{i=1}^{N}\frac{b_{i}-a_{i}}{\max(b_{i},\ a_{i})} \tag{19}\]  
where \(N\) is the number of clusters, and \(a_{i}\) is given by Equation (20):  
\[a_{i}=\frac{1}{N_{k}}\sum_{x_{j}\in C_{k}}dist\left(x_{i},x_{j}\right) \tag{20}\]  
and \(N_{k}\) is the number of objects in a specific cluster, \(b_{i}\) is given by Equation (21):  
\[b_{i}=min_{h\in\{1,\dots,K\},h\
eq k}\left(\frac{1}{N_{h}}\sum_{x_{j}\in C_{h }}dist\left(x_{i},x_{j}\right)\right) \tag{21}\]  
It means that \(a_{i}\) is the mean distance between object \(i\) and all other data points in the same cluster, and \(b_{i}\) is the minimum mean distance of object \(i\) to all points in any additional cluster \(h\), of which this object does not belong.
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 3 Method
### Proposed Network Model for the Deep Convolutional Autoencoder  
Figure 1 depicts the system model in this work. With the aid of medical professionals, such as nurses, the patients who live in rural areas can regularly collect their PCG signals using electronic stethoscopes; then, an encoder continuously compresses the PCG signals before transmitting these compressed data to the remote site via a noisy communication link. Doctors use a decoder to continuously decompress the received compressed PCG signals at the remote site, so that doctors  
max pooling operations, the final layer outputs a vector with length 187. This means the encoder compresses each segment with 6000 samples into each feature map with 187 samples. Note that one batch normalization (Batch Norm) was used in the encoder, such that a stable distribution of activation values throughout training to alleviate the internal covariate shift problems [41].  
Table 2 lists the decoder's detailed architecture, which decompresses each feature map with 187 samples to reconstruct the corresponding PCG segments, each with 6000 samples. Note that, except for the convolutional network and up-sampling operations, the last layer is a fully connected network (Dense) reconstruct the original segment. The total number of trainable parameters for the encoder and decoder are 180,945 and 18,124,705, respectively. This implies the computational cost at the encoder is much less than that at the decoder. Note that the PCG signals can continuously partition into segments and then be compressed with the proposed encoders. The remote side then continuously receives each compressed feature maps. After decoding, the decompressed segments can be used to combine into the original PCG signals. Thus, even though each segment contains roughly only four or five cardiac cycles, which may be insufficient for diagnostic or monitoring purposes, our segment-by-segment signal processing approach could achieve a good CR and a low PRD without compromising continuity for the long-term monitoring.
## 4 Results
### Minimize the Power Consumption
#### 4.2.1 Translation along Z-Axis  
We solve the inverse kinematic and dynamic problems for the translation along Z-axis and check that all the _StudyVariables_ are suitable GP combinations in this case. We obtain the total power consumption for each suitable GP combination and observe that the GP combination that consumes the highest power is (H, L, R) = (0.75, 0.3, 0.6) m: it consumes a total power of 2.1812 W. For this trajectory, we have two best GP combinations: (H, L, R) = (0.35, 0.2, 0.2) m and (H, L, R) = (0.3, 0.2, 0.2) m. Both combinations consume a total power of 0.5224 W. Figure 8 shows the power consumption for each suitable GP combination.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{\(traject(t)=C+A\cdot sin(\omega t)\)} \\ \hline  & **C** & **A** & \(f=\omega/(2\cdot\pi)\) & **t** \\ \hline X- and Y-axes & 0 & 3\({}^{\circ}\) & 2.7 Hz & 4 s \\ \hline Z-axis & 0.5 m & 0.003 m & 2.7 Hz & 4 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Harmonic trajectories definition.  
Figure 8: Power in the actuators for all the suitable and best GP for translation about Z-axis.
# Compositional Modeling of Dimethyl Ether-CO2 Mixed Solvent for Enhanced Oil Recovery
## 3 Results
### Effects of DME Injection on Oil Recovery
#### 3.1.2 Displacement Efficiency  
Oil saturation, oil viscosity, and MMP were compared to confirm the displacement efficiency. Figure 4 shows the oil saturation at the end of the WAG cycle. After three WAG cycles, the oil saturation in Case 1 remains relatively greater than Case 4. As the content of DME increases, the MMP between the injection gas and oil decreases, which has a greater effect on reducing the oil viscosity and IFT, both of which are advantageous for oil flow. Figure 5 shows the oil viscosity after 0.6 PV injection. When the initial oil viscosity was 2 mPa s, it decreased to 0.56 mPa s in Case 1 and to 0.36 mPa s in Case 4. For Case 4, the lowest oil viscosity value was 35% less than that for Case 1. In addition, in Case 4, the deep blue area, in which oil viscosity was less than 0.5 mPa s, was more widespread. The deep blue area in Case 4 was 2902.8 m\({}^{3}\), and in Case 1, the deep blue area did not exist. The values at the middle of the reservoir were compared to evaluate the fluid properties and residual oil component after displacement. As shown in Figure 6, after some point, the oil viscosity of Case 4 was greater than that of Case 1 because the intermediate component was displaced (Figure 7a). The mole fraction of the heavy component (C\({}_{28+}\)) increases (Figure 7b) because the heavy component remains behind the flood front. MMP was measured using a slim tube (Table 7). Figure 8 shows the IFT of the swept area in the middle of the reservoir for the cases, where the IFT value of Case 4 is less than that of Case 1. The lowest IFT value of Case 4 was 0.012 mM/m. The values for Cases 1, 2, and 3 were 0.054 mM/m, 0.115 mM/m, 0.485 mM/m, which were 350%, 858%, and 3941% greater than Case 4.  
Figure 5: Oil viscosity when 0.6 PV solvent was injected for (**a**) Case 1, (**b**) Case 2, (**c**) Case 3, and (**d**) Case 4.  
Figure 6: Comparison of the oil viscosity for Cases 1 and 4 at the midpoint of the reservoir.