esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Flow around the SD7003 Airfoil
#### 3.2.1 Comparison of Different Goal Functions  
As previously mentioned, we have access to various high-fidelity values for this test case, including wall pressure distribution CP, skin friction distribution CF, lift CL and drag CD coefficients. This allows us to define four different objective functions. We tested all these goal functions and compared the results across the different optimizations. Each optimization alters the turbulent viscosity field differently, consequently affecting all other fields.  
The differences are visible in Figure 8 below.  
As a result, we observe different outcomes in terms of both local and global coefficients. There is an evident difference in the shape of the pressure and skin friction coefficients, as illustrated in Figure 9.  
We also illustrate in Table 2 below the results in terms of CL and CD for each optimization.  
Additionally, Figures 10 and 11, along with Table 3, illustrate the differences in turbulent viscosity, as well as local and global coefficients for \(\alpha=8^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c} \hline  & & **CL** & **CD** \\ \hline \(\alpha=4^{\circ}\) & LES & 0.59 & 0.021 \\  & SA & 0.551 & 0.0099 \\ \hline \(\alpha=8^{\circ}\) & LES & 0.92 & 0.043 \\  & SA & 0.895 & 0.0435 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of lift and drag coefficients for SD7003 airfoil.  
Figure 8: Turbulent viscosity results after field inversion optimization for the SD7003 airfoil at \(\alpha=4^{\circ}\). (**a**) CL optimization, (**b**) CD optimization, (**c**) CP optimization, and (**d**) CF optimization.  
From both angles of attack, we observed the same effects. The use of objective functions based on the error on global coefficients yields good results in terms of global coefficients prediction but fails to predict the correct shape distributions, missing import flow features like separation. On the contrary, the use of local distributions in the goal function provides more physical insights and allows us to achieve corrected fields that are closer to the reference one.  
\begin{table}
\begin{tabular}{c c c} \hline  & **CL** & **CD** \\ \hline LES & 0.59 & 0.021 \\ SA & 0.551 & 0.0099 \\ SA (FI\(-\)CL) & 0.586 & 0.0147 \\ SA (FI\(-\)CD) & 0.571 & 0.0191 \\ SA (FI\(-\)CP) & 0.571 & 0.0052 \\ SA (FI\(-\)CF) & 0.602 & 0.011 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of lift and drag coefficients for SD7003 airfoil at \(\alpha=4^{\circ}\).  
Figure 10: Turbulent viscosity results after field inversion optimization for the SD7003 airfoil at \(\alpha=8^{\circ}\). (a) CL optimization, (b) CD optimization, (c) CP optimization, and (d) CF optimization.  
Figure 9: Comparison of local coefficients for different goal function definitions for SD7003 at \(\alpha=4^{\circ}\). (a) Pressure coefficient. (b) Skin friction coefficient on the suction side.
## 2 Materials and Methods
### Hyperspectral Imaging System  
The line-scan-based HSI technique was used in this study as it allows rapid detection of the physical and chemical information of a sample in terms of the spectral data. These data are obtained for each spatial pixel of a captured image and a three-dimensional (3D) hyperspectral cube is simultaneously produced [27; 28; 29]. Figure 1 is a conceptual diagram of the Vis/NIR HSI system based on the line-scan method employed in this work.  
The main components are a 400-1000-nm low-light sensitive electron-multiplying charge-coupled-device (EMCCD; MegaLuca, Andor Technology Inc., Belfast, Northern Ireland) camera including a detector array of 1002 vertical and 1004 horizontal pixels thermo-electrically cooled to \(-20\,^{\circ}\)C via a two-stage Peltier cooler; an imaging spectrograph (VNIR Hyperspec, Headwall Photonics Inc., Fitchburg, MA, USA) for line-scan imaging; a Schneider-Kreuznach Xenoplan 1.4/23 C-mount lens (Schneider Optics, Hauppauge, NY, USA); six 100-W quartz-tungsten halogen lamps.  
Line-scan imaging was achieved when light from the line-scanned field of view (FOV) was passed through the slit of the imaging spectrograph. Here, to produce a 2D image featuring the spatial (horizontal axis) and spectral (vertical axis) dimensions, the light was dispersed and then projected by the dispersive gratings of the EMCCD detector array. The lamps were integrated with fiber-optic line lights (Fiber-Lite, Dolan-Jenner Industries Inc., Lawrence, MA, USA) positioned 500 mm from the sample and 220 mm apart to illuminate the line-scan FOV at forward and backward angles of approximately 10\({}^{\circ}\) with respect to the vertical. The samples were transported by a programmable motorized translation sample holder (25 mm \(\times\) 25 mm \(\times\) 3 mm; Velmex, Inc., Bloomfield, NY, USA) under illumination, and their hyperspectral line-scan images were taken using a 0.2-mm incremental step.
## 3 Problem Statement  
A data center is a place where both software and hardware work together to power computers and communication tools [26]. These tools help run various online services and handle massive amounts of data. With the growth of the Internet, the number of data centers has also increased to keep up with the demand for these services. Some of these centers can support bandwidths greater than 4 GB per second, made possible by multiple fiber-optic connections. Network congestion occurs when too much data fills the network queues, causing packet loss [27]. This happens when the rate of data arriving at an output port is faster than it can be transmitted, leading to an overload. In WSN, congestion often happens when there are too much data being sent. If the data matches the network's capacity, all packets reach their destinations, but when there are too much data, the network cannot keep up, causing some packets to be lost. In cases of severe traffic, network performance drops sharply, and no packets may reach their destinations. Congestion in WSN is also influenced by factors like multiple input lines sending packets to a single output line at the same time, which creates queues [28]. Without enough memory to store these packets, they are discarded. Additionally, slow processing speeds can worsen network congestion. Figure 1 illustrates data transmission in a network under  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author(s)** & **Proposed Method** & **Advantages** & **Disadvantages** \\ \hline
**Antoniou,** **Pitsillides**[23] & Flock-based Congestion Control (Flock-CC) & Effective load balancing, resilient to node failures, scalable across network sizes & Delays in high-traffic scenarios, limited applicability in environments with rapid topology changes \\ \hline
**Alipio and Bures**[24] & Deep Reinforcement Learning Cache-Aware Congestion Control (DRL-CaCC) & Improved data throughput and reduced packet loss by optimizing cache usage dynamically & High computational complexity limits feasibility in real time, resource-constrained WSN environments \\ \hline
**Mazloomi,** **Gholipour**[25] & Fuzzy Structure and Genetic-Fuzzy (FSFG) & 32\% improvement in packet delivery rate, 28\% reduction in end-to-end delay & Complexity may restrict usability in highly resource-constrained networks, requiring further optimization \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._different congestion scenarios. In (a), packets are successfully sent from the sender to the receiver, with solid arrows representing data transmissions and dashed arrows indicating acknowledgments (Ack). In (b), network congestion causes packet loss, marked by the \"X\" symbol, meaning the receiver did not receive the packet. The solid arrows show successful transmissions, while dashed arrows represent acknowledgments. The label Loss indicates that a packet was dropped due to congestion, likely because the queue was full. This results in retransmissions, increased delays, and reduced network efficiency.
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## Appendix A  
**Proof of Lemma 1.** The Lagrange variable \(\lambda^{*}\) is obtained from (20) as displayed in Equation (A1)  
\[\lambda^{*}=\frac{B_{sch}(1+\zeta^{*}_{w})\psi_{w}}{ln2(1+P_{w}\psi_{w})}\] (A1)  
When \(w=1\), then substitute to \(\zeta^{*}_{w}\) in the equation above to produce Equation (A2)  
\[\lambda^{*}=\frac{B_{sch}\big{(}1+\zeta^{*}_{1}\big{)}\psi_{w}}{ln2(1+P_{w} \psi_{w})}>0\] (A2)  
Figure 9: System energy efficiency vs. iterations.  
From Equation (20), substitute w = 1, 2, 3, to all variables to produce Equations (A3) and (A5)  
\[\frac{\partial L}{\partial P_{1}}=\frac{B_{sch}\big{(}1+\zeta_{1}^{*}\big{)}\psi_{ 1}}{ln2\Big{(}1+P_{1}^{*}\psi_{1}\Big{)}}-\lambda^{*}=0\] (A3)  
\[\frac{\partial L}{\partial P_{2}}=\frac{B_{sch}\big{(}1+\zeta_{2}^{*}\big{)} \psi_{2}}{ln2\Big{(}1+P_{2}^{*}\psi_{2}\Big{)}}-\lambda^{*}=0\] (A4)  
\[\frac{\partial L}{\partial P_{3}}=\frac{B_{sch}\big{(}1+\zeta_{3}^{*}\big{)} \psi_{3}}{ln2\Big{(}1+P_{3}^{*}\psi_{3}\Big{)}}-\lambda^{*}=0\] (A5)  
The relation between \(\zeta_{1}^{*}\) and \(\zeta_{2}^{*}\) can be obtained from Equations (A6) and (A8)  
\[\frac{\partial L}{\partial P_{1}}=\frac{\partial L}{\partial P_{2}}=0\] (A6)  
\[\frac{B_{sch}\big{(}1+\zeta_{1}^{*}\big{)}\psi_{1}}{ln2\Big{(}1+P_{1}^{*}\psi _{1}\Big{)}}=\frac{B_{sch}\big{(}1+\zeta_{2}^{*}\big{)}\psi_{2}}{ln2\Big{(}1+P _{2}^{*}\psi_{2}\Big{)}}\] (A7)  
Arranging Equation (A7) to obtain Equation (A8),  
\[\zeta_{2}^{*}=1-\frac{\psi_{1}\big{(}1+P_{2}^{*}\psi_{2}\big{)}\big{(}1+\zeta _{1}^{*}\big{)}}{\psi_{2}\big{(}1+P_{1}^{*}\psi_{1}\big{)}}>0\] (A8)  
Similarly, the relation between \(\zeta_{1}^{*}\) and \(\zeta_{3}^{*}\) can be obtained in Equations (A9) and (A10)  
\[\frac{\partial L}{\partial P_{3}}=\frac{\partial L}{\partial P_{1}}=0\] (A9)  
\[\frac{B_{sch}\big{(}1+\zeta_{3}^{*}\big{)}\psi_{3}}{ln2\Big{(}1+P_{3}^{*}\psi _{3}\Big{)}}=\frac{B_{sch}\big{(}1+\zeta_{1}^{*}\big{)}\psi_{1}}{ln2\Big{(}1+ P_{1}^{*}\psi_{1}\Big{)}}\] (A10)  
Arranging Equation (A10) to obtain Equation (A11),  
\[\zeta_{3}^{*}=1-\frac{\psi_{1}\big{(}1+P_{3}^{*}\psi_{3}\big{)}\big{(}1+\zeta _{1}^{*}\big{)}}{\psi_{3}\Big{(}1+P_{1}^{*}\psi_{1}\Big{)}}>0\] (A11)  
According to the above expressions, it can be concluded that the closed-form optimal power allocation can be obtained from Equation (22) as shown in Equation (A12).  
\[P_{w,s}^{*}=\frac{1}{\psi_{w}}\Big{(}2^{\frac{R_{w}}{B_{sch}}}-1\Big{)},\ \forall w\in M\] (A12)  
From Equation (21), setting w = 1, we obtain Equation (A13).  
\[P_{1,s}^{*}=P_{max}-\sum_{w=2}^{M}P_{w,s}^{*}\] (A13)  
\(\sqcap\)\(\sqcup\)
## 5 Performance Evaluation
### Performance Results
#### 5.3.2 Kernel-Level Benchmark
Kernel-level RCU performs better than user-level RCU due to some implementation differences. The major differences are the introduction of the EBR algorithm to wait for the memory recycling of logically deleted nodes, the use of non-blocking rcu_call() API, and the use of optimized locks inside sv6 variant. Based on the performance results of machines with more CPU cores for the RCU benchmark [25], we anticipate a more significant performance degradation as the number of threads increases. The Grace Period and Write Back overhead for log reclamation, which are causes of RLU performance decrease, have a more significant effect as the number of execution threads increases. It is analyzed that the performance of MV-RLU is decreased due to overhead related to the log reclamation at the update ratio of 50%, which will be discussed in the next section.
## 4 Results of Numerical Simulation  
### Anchorage without Supplementary Reinforcement  
The numerical model described in Section 3 was created for a specimen without supplementary reinforcement. It was possible to analyze the formation of the first cracks, the failure mechanism and then determine the fundamental differences in resistance concerning other input parameters, especially the compressive strength of concrete. The numerical simulation results were compared with the experimentally obtained data from the test of specimen 3C. The relationship between load and displacement was used as the main parameter for comparing both analyses. Based on the graphs in Figure 15, it is possible to state a satisfactory agreement between the experiment and the numerical simulation. The correlation coefficient reaches a value of up to 0.96.  
The deformation of the anchorage area in the specimen without supplementary reinforcement and the propagation of cracks larger than 0.5 mm is shown in Figure 16. The cracks' positions correspond very well to the theoretical model, which assumes the propagation of cracks and the formation of a concrete cone at an angle of 35\({}^{\circ}\)[51].
# Simultaneous Quantitation of Isoprenoid Pyrophosphates in Plasma and Cancer Cells Using LC-MS/MS
## 3 Results and Discussion
### Assay Validation  
#### 3.2.1 Specificity and Selectivity  
The specificity of the intended method was established by screening the charcoal-stripped blank plasma from six independent sources. Chromatograms of blank plasma (six batches) contained no co-eluting peaks greater than a peak area of 20% of the analyte area at the LLOQ concentration. There were no co-eluting peaks > 5% of the area of the IS. The retention time for GPP, FFP, GGPP and the IS were 3.6, 5.8, 7.5 and 4.6 min, respectively. The representative UPLC chromatograms with neat blank, standard spiked in the buffer (Figure 4), blank charcoal-stripped plasma, standard spiked in charcoal-stripped plasma at 10 ng/mL concentration, shown in Figure 3 indicating that all endogenous interfering peaks were resolved at the retention times of the analytes. Analytes and IS peak retention time were stable with a relative standard deviation (% R.S.D.) well within the acceptable limit of \(\pm\)5%.
# Machine Learning-Based Highway Truck Commodity Classification Using Logo Data
## 3 Methodology  
In this section, we describe the developed approach in detail. Logos, serving as the outward expression of brands, often consist of letters or texts with large variations in colors, font styles, and graphical figures. Logos can appear anywhere on truck bodies, making it hard to leverage any prior knowledge of context and placement. It becomes more challenging when moving to truck images of low resolutions, poor light and weather conditions, and diverse view angles. The existing work on logo detection relies on large datasets of sufficient fine-grained annotations on logos such as bounding boxes and logo types, which, however, are often unavailable in real-world traffic scenarios. In this paper, we have developed techniques following broad approaches for text-based and image-based logos as follows:  
* For text-based logos, we employ state-of-the-art solutions for text detection [25; 26] and recognition [27; 28] to obtain raw text predictions with high accuracy. These predictedlogo texts were matched to our built commodity database by comparing these texts with recorded company names via simple string matching algorithms;
* To identify image-based logos that do not contain text information, we developed a novel two-stage image-based approach: a universal logo detection stage that outputs all potential logo positions within images, followed by a logo recognition stage designed to incorporate various advanced image representations. If the universal logo detector returned bounding boxes with high confidence scores, we cropped out the corresponding image regions to obtain logo candidate images. Each logo image was processed by our developed model to obtain the image features. These features were matched with pre-computed image features stored in a database containing ground truth logo images of interest. It is considered as a correct match if the matching score surpasses a certain threshold; otherwise, we ignore this logo candidate;
* For an automated system, it is not known a prior whether a given truck contains text- or image-based logos (or neither). We integrated the text-based approach and image-based approach using the pipeline shown in Figure 1. This pipeline naturally combines the advantages of both approaches in one integrated approach, and the later experiments in Section 4 validate its effectiveness in improving the performance of commodity classification.
# Dynamic Deflection Measurement on Stiff Bridges with High Piers by Preloaded Spring Method
## 2 Preloaded Spring Method and the Related System
### Principle of Static Deflection Measurement  
In Figure 4, the tensile wire-spring system is assumed to have only degrees of freedom in \(y\)-direction. The sleeve can be equivalent to rigid bars, in which the bending stiffness and axial stiffness tend to infinity (\(EI\rightarrow\infty\), \(EA\rightarrow\infty\)). The stretched wire and the compression spring are equivalent to a series damping spring with an initial preload. Sites B and B\({}^{\prime}\) have the same displacement, and so do sites C and C\({}^{\prime}\). When the tensile stiffness \(k_{w}\) of the wire tends to infinity, the relative displacement of sites C and B\({}^{\prime}\) is the static deflection of the bridge.  
The composite stiffness \(K_{c}\) of the SWSS can be calculated as the following:  
\[K_{c}=\left.\left(k_{s}\cdot\frac{E_{w}A_{w}}{L}\right)\right/\left(\frac{E_{w }A_{w}}{L}-k_{s}\right)=\frac{k_{s}E_{w}A_{w}}{E_{w}A_{w}-k_{s}L} \tag{1}\]  
where \(k_{s}\) is the stiffness of the compression spring, which is finite; \(E_{w}\), \(A_{w}\), and \(L\) are the elastic modulus, cross-sectional area, and length of the stretched-wire.  
The measurement error \(\varepsilon\) caused by the stretched wire and the compression spring can be obtained by the following equation:  
\[\varepsilon=\frac{\Delta y_{1}}{\Delta y}=\frac{K_{c}L}{E_{w}A_{w}}=\frac{k_{ s}L}{E_{w}A_{w}-k_{s}L}=\frac{E_{w}A_{w}}{E_{w}A_{w}-k_{s}L}-1 \tag{2}\]  
where \(\Delta y_{1}\) is the relative displacement of sites A and B; \(\Delta y\) is the relative displacement of sites A and C.  
Equation (2) indicates that four factors influence the measurement error \(\varepsilon\) of the static displacement of the SWSS: the compression spring stiffness, elastic modulus, stretched-wire length, and cross-sectional area of the stretched-wire. The measurement error \(\varepsilon\) increases with the stretched-wire stiffness and decreases with the compression spring stiffness and stretched-wire length.
# LogBD: A Log Anomaly Detection Method Based on Pretrained Models and Domain Adaptation
###### Abstract
Most of the existing approaches learn normal patterns from many normal logs, and the models examine the abnormal log sequences on the basis of the learned normal patterns. Most of the methods use static word embedding methods, such as Word2 Vec or Glove, but words may have different meanings in different contexts, and dynamic word embedding methods are needed. The above methods are also not studied for cross-system log anomaly detection; they are all for a specific system. The models obtained from training, therefore, do not work well on new systems. In this paper, we propose LogBD to build a cross-system log anomaly detection model to detect anomalies in both source and target systems, using the semantic and sequential relationships of log messages to achieve anomaly detection results with very few logs in the target system. The main contributions of this paper are as follows:  
1. BERT [7] is used to extract the semantic features of log messages and feed the log sequence vector with preserved semantic information into a neural network, which learns the normal patterns and semantic information of logs and is better able detect abnormal log sequences. 2. The log sequence vector is input into a temporal convolutional network (TCN) [8] for data mapping, and the hypersphere objective function is used to map the log sequences into the hypersphere space. The distance between normal log sequences and abnormal log sequences to the center of the sphere differs as a function of the distance mapped to the center of the sphere, enabling us to determine anomalies. 3. Domain adaptation learning is used to train the TCN so that it can extract the common features of log data from different systems and apply the \"knowledge\" learned from the source system data directly to the target system data, thus enabling the detection model to detect anomalies from multiple systems. The remainder of this paper is organized as follows: the relevant knowledge and related work are presented in Section 2; the details of our study are presented in Section 3; the performance of the proposed model is evaluated in Section 4; a summary of this work is provided in Section 5.
## Materials and Methods
### Data  
#### 2.2.1 TerraSAR-X and TanDEM-X SAR  
Our study was based on a pair of TerraSAR-X and TanDEM-X Complex Synthetic Aperture Radar (COSAR) Single Look Complex (SLC) observations collected in pursuit monostatic StripMap (SM) mode, acquired on February 21, 2015, during the TanDEM-X Science Phase. The pursuit monostatic TanDEM-X acquisition called \"TerraSAR-X Like products\" was composed of two single independent TerraSAR-X images [35]. Thus, each image could be processed independently, unlike bistatic TanDEM-X images which need synchronization. The TerraSAR-X and TanDEM-X SAR data fully covered most of the Petermann Glacier. The carrier frequency of the X-band radar was 9.6 GHz(3.1 cm in wavelength) with horizontal-horizontal polarization at 35.33 degrees of the incidence angle. The range and azimuth pixel spacing were approximately 0.91 m and 1.90 m, respectively. The height of ambiguity was calculated as \(-10.63\) m at \(-527.27\) m of the perpendicular baseline. The temporal baseline was only 9.98 s, which was a sufficiently short favorable condition to maintain coherence even on rapidly moving surfaces. The characteristics of the TerraSAR-X and TanDEM-X SAR are summarized in Table 1. To retrieve the topographic information for the entire Petermann Glacier area, two consecutive SAR observations were concatenated.  
Figure 1: (**a**) Location map (geodetic coordinates) of the Petermann Glacier showing the TerraSAR-X/TanDEM-X swath marked by the black frame. (**b**) Multi-spectral Landsat-5 TM optic image acquired on June 15, 2009, before the calving event that occurred during August 2010. (**c**) Landsat-8 OLI captured on March 30, 2015, after both calving events in 2010 and 2012. The glacier loss in the terminus areas is clearly detected through the two optic remotely-sensed observations. (**d**) TerraSAR-X SAR amplitude image on February 21, 2015, over the Petermann Glacier in Northwest Greenland.