esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Space Time Adaptive Processing Methods for Synthetic Aperture Radar
## 7 Conclusions  
Two improved STAP methods for SAR based on piecewise sub-apertures are proposed in this paper. In these methods, the entire synthetic aperture time is divided into several sub-apertures and the interference can be considered as stationary in each sub-aperture. The first algorithm is an element space STAP implementation, which is designed for interference cancellation and azimuth focusing in SAR by the piecewise data constraint. The second algorithm is the piecewise data constrained GSC, which is equivalent to the element space STAP approach if there is no signal mismatch. The proposed algorithms are validated through the model simulation and measured data to lay the foundation for further airborne applications.  
Conceptualization, S.S. and L.T.; methodology, S.S. and X.N.; validation, S.S. and X.N., resources, L.T. and X.Z.; writing--original draft preparation, S.S.; writing--review and editing, Y.B. and L.T.; supervision, X.Z. and P.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61976113.  
The authors declare no conflict of interest.
# CD44 Depletion in Glioblastoma Cells Suppresses Growth and Stemness and Induces Senescence
## 3 Results
(**d**) Hypoxia-related genes VEGFA, LOX, and BCL2 mRNAs were quantified in the respective conditions by RT-PCR and demonstrated after normalization to TBP. Asterisks show significant differences compared to the respective control condition: * \(p<0.05\), *** \(p<0.01\)**. (**e**) Extreme limiting dilution assay (ELDA) was carried out of either parental U251 MG or CD44 KO clones; stemness capacity was evaluated after cells had been grown under non-adherent conditions for ten days. Steeper slopes indicate higher stem cell frequency. A table demonstrating average stem cell frequency per group is shown. All graphs show the average \(\pm\) SEM values from at least three independent experiments. To evaluate the effect of CD44 depletion in stemness maintenance in sphere-like conditions, we performed an extreme limiting dilution assay (ELDA) in both parental cells and CD44 KO clones. Notably, all five CD44 KO clones exhibited diminished stem cell frequency compared to the control cells, stressing the importance of CD44 for self-renewal ability (Figure 2e).
# A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network
## 3 Proposed Strategy
### The Metrics  
We applied common statistical techniques to evaluate the classification problem:  
1. True Positive (TP): a packet is classified correctly by the model into its category of behavior. The result is a True Positive.
2. False Positive (FP): a packet belongs to a category of behavior but is classified into the wrong category. This result is a False Positive.
3. True Negative (TN): a packet is classified correctly by the model as not belonging to a category of behavior. This result is a True Negative.
4. False Negative (FN): a packet does not belong in a category of behavior, but the model incorrectly classifies the packet into the category. The result is a False Negative.  
From these statistics, we used accuracy, precision (PC), recall (RC), and F1 score (F1) metrics to evaluate the model's efficiency during testing, and also, for observation, we applied a confusion matrix (CF) to illustrate the relationships between each PC and RC of each attack class [69].
## 4 Experiment Setup and Results  
### Experiment Setup  
We set up an experimental setup to measure the temporal behavior of SBS intensity, as shown in Figure 5. We employed a low-noise single-frequency laser (NKT, Koheras Basik Y10, Copenhagen, Denmark) with a linewidth of 20 kHz as the seed source, delivering an output power of 10 mW. The seed light was then amplified through three stages of pre-amplification, each stage comprising 1 m of polarization-maintaining ytterbium-doped fiber (PM YDF) with an absorption coefficient of 250 dB/m, and the pump light had a central wavelength of 980 nm, resulting in an amplified output power of 50 mW. Signal generation involved an arbitrary waveform generator (AWG, Tektronix 70001B, Portland, OR, USA) to produce both unoptimized and optimized MLS signals, but the signal emitted from the AWG was discrete. We accessed a low-pass filter (LPF) to flatten the signal so that the phase of the light could be kept on the \(\pi\) modulation, and the signal was then amplified by an RF amplifier and fed into an EOPM to broaden the phase of the pre-amplified seed light. The main amplification stage utilized a 4.5 m long ytterbium-doped fiber with an absorption coefficient of 4.95 dB/m, and the pump optical center wavelength was 980 nm. After the main amplifier, we connected it to the circulator, which could isolate the backward light and prevent the upstream amplification stage from being damaged, and also, it could be used to test the spectral characteristics and waveforms of the backward light. A 99/1 2 \(\times\) 2 fiber splitter was then plugged in, with 99% of the ports used to output the laser and 1% of the rear ports used to test the return optical power. We used a 1000 m passive fiber as the gain fiber for the SBS effect, which was used to amplify the SBS effect, and finally pumped the stripping at the output with an angle of 8\({}^{\circ}\) to strip the pump light and prevent end face reflection.  
Figure 4: Time-domain waveforms of the output laser and backward Stokes light at (**a**) original PRBS-9; (**b**) MLS1, PRBS-9; (**c**) MLS2, PRBS-9.
# Clustering Approach for Reliable Wireless Communication
## 2 Materials and Methods
### MUSA Codes Selection
However, increasing the code length results in a throughput decrease, as data bits occupy more resources due to the number of additional bits added with spreading. The cross-correlation of any two code sequences, \(x\) and \(y\) of length, \(L\), is defined in [54] using the dot product, \(x\times y^{H}\), where \(H\) is the transpose conjugate of \(y\) (the Hermitian operator). The cross-correlation matrix (C) for the 3 orthogonal codes in Equation (1) is computed in Equation (2):  
\[C= \begin{bmatrix}c_{1}\\ c_{2}\\ c_{3}\end{bmatrix} * \begin{bmatrix}c_{1}\\ c_{2}\\ c_{3}\end{bmatrix} ^{H}= \begin{bmatrix}4&0&0\\ 0&6&0\\ 0&0&6\end{bmatrix}  \tag{2}\]  
The autocorrelation is neither fixed nor equal to the code length value, while the cross-correlation is zero. Codewords with a length of 4 proposed by the 3 GPP TR 38.812 [52] were used in this analysis and can be seen in Table 1.  
Figure 2: QPSK spreading sequences and four code examples.
## 1 Introduction
The scheme is used for each node to decide on forwarding the data packet based on its current state related to the link quality, location and remaining energy level. As a result, the strength of each approach is used to mitigate the weakness of the other. Simulation performance results show how the ROR framework is effectively able to utilize the CR capabilities to improve communication in CRSN when compared with similar solutions. It is important to note that the contribution of this work is the implementation of an efficient and reliable geographical forwarding for CRSNs. By this, we do not seek to provide another study of the energy and reliability trade-offs pertaining to geographical forwarding, which has been detailed in the literature [18, 19, 20, 21], except were necessary. In this regard, this work will be the first that implements lossy link-aware geographic forwarding for CRSNs. The rest of the paper is organized as follows: In Section 2, we provide a review of related and relevant works on routing in CRSN. Detailed operational functions of the modules that make up ROR are presented in Section 3, along with the system/network model. For verification of the packet forwarding principle of ROR, in Section 4, a theoretical operation analysis is presented along with simulation results. In Section 5, both the performance evaluation of ROR and the comparative evaluation of ROR are discussed. Finally, the paper is concluded in Section 6.
## 4 Platform Design
### Proof of Location (PoL)
\(\delta\) is the standard deviation and depends on the hardware and antenna used as well as on the noise. We tried to define the location of 2 smartphones through Wi-Fi RSSI. RSSI values of a hotel Wi-Fi access point were measured in an urban area of Kuala Lumpur using two different smartphones, a SAMSUNING GALAXXYAS30 and an OPPO F11. RSSI readings were recorded each 2-meter interval walking from the hotel main entrance following the itinerary illustrated in Figure 15 for 50 times, to deduce the standard deviation model:  
Since there is no standard industrial norm for computing RSSI, its measurement for the same distance is different in the two phone devices. In such an architecture with no unified protocol in RSSI measurement, the anchor node needs to have a reference database with a d(RSSI) model for each device. We kept the same distance function as in Equation (1) with personalized \(\lambda\) and \(\delta\) and for each device as shown in Figure 16.  
The Wi-Fi access point is of 5 GHz with a wavelength of 6 cm, the PoL is deduced according to Equation (3), the access point Wi-Fi signal power transition is \(P_{T}=25\) dBm. The measured RSSI corresponding to each distance in the entire 50 testing sets was used to compute the distance standard deviation for each device. In our case, the RSSI in a given position varies randomly over time from one test set to another, the values that are measured  
Figure 16: Mapping diagram illustrating how each consumer node is mapped to its own distance computation model in the Anchor server node. Figure 15: The itinerary followed. for each distance in each training set are provided in Equation (7). The corresponding results are reported in Figures 17 and 18. \[\sigma=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N=50}RSSI_{i}-u} \tag{7}\]  
where, \(u=\frac{1}{N}\sum\limits_{i=1}^{N=50}RSSI_{i}\)  
The mean standard deviation for device 1 is 2.2625, whereas it is 2.6500 for device 2. By fixing \(\delta\) for each device, the next step is to deduce \(\gamma\) for each one of them using best fitting curve method between the set of distances \(d_{1},d_{2},\ldots,d_{15}\) and the set of corresponding RSSI mean averages \(RSSI_{av10}\), \(RSSI_{av1}\), \(\ldots\), \(RSSI_{av15}\), using Equation (5). This is depicted in Figures 19 and 20. Figure 17: Standard deviation for device 1. Figure 18: Standard deviation for device 2.
## 1 Introduction
In this study, we aim to improve the understanding of the relationship between phenology and biomass production in the Sahel. For this purpose, we use phenological variables derived from satellite observations (for a review of the methods see [PERSON] and [PERSON] [44]), as they can effectively map phenological events over large areas for an extended time period. Key phenological variables are derived from the analysis of FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) data from SPOT-VEGFATION instrument for the period of 1998-2012. The cumulative FAPAR value (CFAPAR) over the growing season is used as an indicator of biomass production, as CFAPAR appears to be a suitable proxy of GPP, biomass production [38, 45, 46, 47, 48, 49, 50] and of crop yield (e.g., [51, 52, 53]). The correlation between CFAPAR and the key phenological variables and an indicator of the maximum productivity attained during the growing season is then mapped over the Sahel. Finally, ground measurements of herbaceous aboveground biomass collected over rangeland sites in Senegal are used to assess the strength of the link between CFAPAR and biomass production and to test the consistency of the correlations between CFAPAR and phenological variables.
# A Review of MEMS Scale Piezoelectric Energy Harvester
## 2 Research Progress of PEH
### Research and Improvement of Piezoelectric Modes
#### 2.3.2 Piezoelectric Energy Harvester in \(d_{15}\) Mode  
By comparing the properties of the piezoelectric materials listed in [PERSON]'s thesis [49], materials have a higher \(d\)-\(g\) product when PEH is subjected to torsion stress, which means that energy harvesters in \(d_{15}\) mode have potential in offering higher output power [56]. However, the fabrication of the \(d_{15}\) device is not straightforward as electrodes for poling and charge collection should be different. Thus, the application of the PEH in \(d_{15}\) mode is restricted.  
Further study of \(d_{15}\) device was carried out by [PERSON] et al. [57] in 2013. He developed a model based on the Timoshenko beam theory to estimate the electric power output in a cantilever beam with a piezoelectric core subjected to the base excitation. The simulation results showed that the output voltage increased as the load resistance increased in the \(d_{15}\) mode energy harvester model. Besides, increasing the load resistance shifted the resonant peak from short circuit natural frequency into higher values of open circuit natural frequencies. Then, in 2015, he investigated the power output of both \(d_{15}\) and \(d_{31}\) mode energy harvester models [58]. Numerical studies revealed that the \(d_{15}\) mode energy harvester has an enhanced power harvesting efficiency (~50% higher) compared to the \(d_{31}\) mode operation of the piezoelectric phase.  
A common structure of the \(d_{15}\) energy harvester is to place the proof mass at the tip of the cantilever beam asymmetrically [59; 60]. For this structure, the asymmetry of the proof mass can influence the harvester's performance. As the asymmetry increased, the output power increased as much as 30%. In addition, the configuration of asymmetric proof mass may broaden the range of excitation frequencies as well.  
In 2014, [PERSON] et al. [61] designed a torsion energy harvester to achieve energy harvesting in \(d_{15}\) mode. The torsion energy harvester consisted of piezoelectric films and a tube. Four piezoelectric films were attached to the surface of the tube. The simulation results showed that the torsion energy harvester could generate a maximum output voltage of 71.6 V while the \(d_{31}\) mode cantilever energy harvester of the equivalent piezoelectric material volume could only generate 6.3 V. In addition, the cantilever energy harvester produces approximately 70% of the torsion energy harvester's output power. Further study showed that the output voltage lowered down when the piezoelectric material became farther from the base of the beam due to the variation in bending stresses along the length of the beam, whereas all elements on the outer surface of the torsion energy harvester experience the same amount of stress and generate the same output voltage.
# A Low-Profile Dual-Polarized Transmitarray with Enhanced Gain and Beam Steering at Ku Band
## 4 The TA Antenna
The phase of the transmitted electric field of pth UC can be calculated by summing the phase of the incident electric field and the transmission phase shift of the UC [47]:  
\[\psi(x_{\rm P},y_{\rm P})=-k_{0}d_{\rm P}+\psi_{\rm n}(x_{\rm P},y_{\rm P}) \tag{5}\]  
where d\({}_{\rm P}\) is the distance between the feed point having the coordinates \((x_{\rm f},y_{\rm f},z_{\rm f})\) and the corresponding planar TA surface point having the coordinates \((x_{\rm P},y_{\rm P},z_{\rm P})\)[47]:  
\[d_{\rm P}=\sqrt{(x_{\rm P}-x_{\rm f})^{2}+\left(y_{\rm P}-y_{\rm f}\right)^{2} +\left(z_{\rm P}-z_{\rm f}\right)^{2}} \tag{6}\]  
Finally, the required phase shift \(\psi_{\rm n}\) for the generic UC of the planar TA, located at \((x_{\rm P},y_{\rm P},z_{\rm P})\), can be calculated from the Equations (4) and (5) [48]:  
\[\psi_{\rm n}=k_{0}(d_{\rm P}-(x_{\rm P}\sin\theta_{0}\cos\varphi_{0}+y_{\rm P} \sin\theta_{0}\sin\varphi_{0})) \tag{7}\]  
Figure 8: Feed antenna array stack up with dimensions (P\({}_{1}\) = 7.70 mm, P\({}_{2}\) = 7.70 mm, P\({}_{3}\) = 2.75 mm, P\({}_{4}\) = 1.44 mm, P\({}_{5}\) = 2.18 mm, D\({}_{1}\) = 0.62 mm, D\({}_{2}\) = 15.05 mm, T\({}_{1}\) = 0.381 mm, T\({}_{2}\) = 1.575 mm, L = 35 mm, \(W\) = 35 mm); metal portion and substrate 1 and 2 are represented by black and blue colors, respectively. The 3-bit phase compensation can be quantified by the expression (8):  
\[\psi_{n}=\left\{ \begin{array}{c}0^{\circ},\;0^{\circ}\leq\psi_{n}<45^{\circ}\\ 45^{\circ},\;45^{\circ}\leq\psi_{n}<90^{\circ}\\ 90^{\circ},\;90^{\circ}\leq\psi_{n}<135^{\circ}\\ 135^{\circ},\;135^{\circ}\leq\psi_{n}<180^{\circ}\\ 180^{\circ},\;180^{\circ}\leq\psi_{n}<225^{\circ}\\ 225^{\circ},\;225^{\circ}\leq\psi_{n}<270^{\circ}\\ 270^{\circ},\;270^{\circ}\leq\psi_{n}<315^{\circ}\\ 315^{\circ},\;315^{\circ}\leq\psi_{n}<360^{\circ}\end{array} \right. \tag{8}\]  
A home-made software is used to calculate the 3-bit phase distribution of the TA for a focal distance of F = 114.18 mm. The phase compensation required for the broadside pointing direction (\(\theta_{0}\), \(\varphi_{0}\)) = (0\({}^{\circ}\), 0\({}^{\circ}\)) is illustrated in Figure 9b. The distinct colors indicate UCs with different transmission phases; see the colorimetric scale.
## 1 Introduction  
Artificial intelligence (AI) serves as a facilitating technology extensively applied in numerous industrial sectors; however, in naval and maritime industries, its development remains nascent.  
One of the potential domains where AI can be harnessed aboard ships pertains to fault detection. The introduction of machine- and deep learning-based systems for managing system faults on vessels enables the automatic (or nearly automatic) detection of system faults in real-time through the analysis of data gleaned from sensors embedded in the ship's systems. These AI systems offer a multitude of advantages, including the following:  
* Fault Detection: The identification of anomalies or data readings that fall outside the normal range, as captured by sensors distributed across various vessel systems, such as pressure sensors, flow meters, vibration sensors, and temperature sensors, among others.
* False Alarm Identification: Timely decision making is achieved to differentiate genuine system faults from events such as sensor malfunctions or signal loss.
* Precise Fault Localization: By leveraging sensor data comparisons, accurate determination of the fault's location within the system can be achieved.  
* Prescription of Action Steps: After the identification and localization of a detected fault, the system can assist in decision making by providing actionable recommendations or even autonomously executing corrective measures. For instance, in the event of a ruptured section of piping, the system may advise the individual in charge of system control to close specific valves.  
In this work, the utilization of neural networks for the detection and localization of pipe ruptures within the ship's firefighting system is proposed. This contribution centers on addressing a literature gap by applying AI technologies to the specific domain of fault detection within the maritime industry. Unlike other industrial applications that employ dense sensor networks with broad coverage, this approach utilizes the existing sensor network available, which, in the case of a medium size ship, could be limited to 15-25 pressure transmitters. This limitation presents a significant challenge for fault detection and localization, as the reduced number of sensors could affect the ability to monitor and accurately locate ruptures. However, the proposed methodology is tailored to this limitation using neural networks to process the sensor data, allowing for effective fault detection, including the localization of pipe ruptures.  
To provide a comprehensive overview, this paper is organized as follows: The Section 1.1 reviews related work in the field of fault detection in maritime systems. The Section 2 describes the methodology used for data collection and neural network model development. The Section 3 presents the experimental setup and results, including performance evaluation metrics. The Section 4 discusses the implications of the findings and potential applications in maritime safety. Finally, the Section 5 concludes the paper and suggests directions for future research.
# The Numerical Study for the Effect of Stiffness Matching on Wheel-Rail Curve Squeal Noise
## 1 Introduction
Through the lens of wheel-rail stiffness matching, this paper furnishes valuable insights into the suppression and optimization of curve squeal noise. The primary contents of this paper are as follows:  
1. We establish a finite element dynamics model of a wheel-ballastless track system for small-radius curve sections. The finite element model includes a wheel, rail, fastener system, and track plate to simulate the force characteristics of a ballastless track system to the maximum extent. By analyzing the characteristics of wheel-rail sliding friction vibration under the condition of transverse creep force saturation, the stability of the system is judged and the occurrence trend of wheel-rail curve squeal noise is predicted. 2. We explain the process of self-excited vibration of the wheel-rail system based on the modal coupling theory. According to the modal characteristics of the unstable modes in the system, combining the modal coupling theory, the causes of the system instability are analyzed, and the mode shape and frequency of unstable modes of the wheel-rail system are explained. The curve squeal noise is mainly caused by the vibration at the rim, railhead, and rail foot. 3. This study explores the wheel-rail stiffness matching problem from the perspective of material stiffness and constraint stiffness respectively and analyzes the influence of both on the occurrence trend of curve squeal noise. The results show that the selection of suitable wheel-rail stiffness matching parameters can suppress squeal noise.