esa-sceva/satcom-chunk-collection huggingface dataset:
# Sewage Sludge Derived Materials for CO2 Adsorption
## 2 Materials and Methods
Brunauer-Emmett-Teller (BET)-specific surface and pore diameter distribution were determined from the N\({}_{2}\) adsorption-desorption isotherms, at \(-\)196 \({}^{\circ}\)C with an AutosorbiQ-C instrument (Quantachrome Instruments, Boynton Beach, FL, USA). Prior to sorption analysis, the materials were preheated for 5 h. Initially, the temperature increased up to 180 \({}^{\circ}\)C in 1 h and then it was kept constant for 4 h, for degassing. Finally, the material was cooled to room temperature under vacuum. The IR spectra were recorded in the region 4000-400 cm\({}^{-1}\) using Fourier transform infrared spectroscopy (FTIR) on a Cary 630 ATR-FTIR instrument (Agilent Technologies, Inc., Santa Clara, CA, USA). Before the analysis, the samples were grinded in an agate mortar, then dried at 80 \({}^{\circ}\)C under vacuum for 3 h, in order to avoid the appearance of physically adsorbed water. Spectral acquisition was carried out by pressing the sample against the diamond crystal using the attached pressure clamp. A slip clutch on the clamp prevented overtightening, collecting spectra (32 scans for background and samples at 8 cm\({}^{-1}\) resolution and threshold 0.002) between 4000 and 400 cm\({}^{-1}\). The combustion and the pyrolysis methods coupled with the gas chromatography method (Flash EA2000, Thermo Scientific, Waltham, MA, USA) were used to perform the elemental analysis [37; 38]. The metals content was determined by atomic absorption spectrophotometer (NovAA 300, Analytik Jena AG, Jena, Germany). The experimental setup (Figure 2) consisted of a reactor connected to a supply gas cylinder and to a chromatograph with TCD detector for CO\({}_{2}\) quantification (GC Varian CP 3800, Palo Alto, CA, USA), in order to assess the efficiency of the materials. The reactor was made from stainless steel, with the dimensions 160 mm--length, 27.5 mm--radius, 55.00 mm--internal diameter and 0.38 L--volume. Thus, 200 g of adsorbent material was introduced into sandwich support described in a previous study [41] and then placed into the reactor. The tests were performed in two stages by using firstly the gas mixture with higher concentration in CO\({}_{2}\) (74 vol.%) and after that the second gas mixture with lower concentration in CO\({}_{2}\) (0.1 vol.%). The amount of adsorbent used was 200 g, and the test parameters were feed pressure--5 PSI, flow rate--100 mL/min and temperature--23 \({}^{\circ}\)C.  
Figure 2: Flow sheet testing of selective adsorbent porous materials.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 2 Genomics in Cancer Diagnosis
### Contributions and Limitations of Genomics in Cancer Diagnosis  
In summary, genomics has made substantial contributions to cancer diagnosis, enabling precise identification of genetic alterations crucial for clinical decision making. The analysis of somatic and germline variants, supported by technologies like NGS and gene  
Figure 1: Key pathological mechanisms of somatic oncogenes and tumor suppressors in cancer. (**A**) Mutated _KRAS_: Gain-of-function mutation in the _KRAS_ proto-oncogene leads to its constitutive activation, largely independent of upstream receptor tyrosine kinase signaling. The impaired GAP-mediated inactivation results in persistent GTP-bound KRAS, hyperactivating downstream pathways, such as PI3K/AKT/mTOR, RAF/MEK/ERK, and RAL/NF-kB. This leads to altered gene expression, promoting uncontrolled cell proliferation, increased cell survival, and enhanced tumorigenesis. (**B**) Mutated _TP53_: Loss-of-function mutation in the _TP53_ tumor suppressor gene. Despite receiving cellular stress signals (e.g., DNA damage, oncogene activation), the dysfunctional p53 protein fails to effectively activate its target genes for tumor suppression. This failure includes the breakdown of the p53–MDM2 negative feedback loop, as the mutated p53 cannot properly induce MDM2 expression. Consequently, the cell accumulates DNA damage, exhibits uncontrolled proliferation, and has an increased risk of metastasis.  
panels, has allowed for more rapid and personalized diagnoses. However, significant limitations persist. The inherent complexity and high costs of WGS and, to some extent, WES, can restrict their widespread adoption [23]. The interpretation of Variants of Uncertain Significance (VUS) remains a challenge, requiring specialized expertise and further research to ascertain their clinical relevance [13]. Moreover, tumoral heterogeneity can complicate the detection of all relevant variants through a single tissue biopsy, potentially leading to incomplete diagnostic pictures [24]. Addressing these limitations will be crucial for maximizing the full potential of genomics in oncological diagnostic practice and ensuring equitable access.
# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 3 Results
### Creating Ship Trajectory Lines  
Figure 4 shows the trajectory lines of the different ship types derived from the point data and used in the density analysis. All plots are presented on the same scale to include the analysis area of the coastal waters of Korea. On this scale, we can compare and understand the overall international and local routes of different ship types. The lines between each two consecutive points for the same MMSI were constructed based on chronologically sorted records. To prevent the creation of inaccurate or incorrect lines, lines of 30 km or longer and time intervals of 6 h or more between two consecutive points were excluded. This restriction avoided potential errors caused by the irregular reporting intervals of AIS data, in addition to potential errors created when ships stop sending messages during a voyage and resume communication after several days.  
Figure 4: Trajectory line segments constructed from consecutive position points in AIS data: (**a**) cargo ships, (**b**) tankers, (**c**) passenger ships, and (**d**) towing ships.
## 3 Data and Methods
### Flash-Flood Potential Assessment
#### 3.3.4 FFPI Results Validation  
The assessment of the results reliability is a mandatory stage for their use in the subsequent steps performed in the present analysis. One of the most used method for results validation is the ROC curve with its statistical indicator, the area under curve (AUC). A detailed explanation regarding the manner in which the ROC curve is constructed and how AUC values can be calculated has been done before by [PERSON] and [PERSON] (2018) and [PERSON] (2019).
# Prediction of Power Fluctuations of Gaussian Beams After Transmission Through Turbulent Atmosphere
## 4 Prediction Results and Discussion
By comparing Figure 8a and Figure 8b, it can be observed that the prediction accuracy of the TSLSTMRCPN is further improved when the data unrelated to the laser power fluctuation are excluded from the input data for network training and prediction, in which the MAE decreases from 0.0598 milliwatt to 0.0583 milliwatt. Turbulence in outdoor natural environments is far more complex than the artificial turbulence in a turbulence pool, and thus, the laser power fluctu  
Figure 8: Laser power measured and predicted by TSLSTMRCPN using data obtained in turbulence pool experiment (**a**) using all collected meteorological parameters as model input and (**b**) using meteorological parameters of temperature and humidity only as model input. more difficult to predict. To examine the performance of the TSLSTMFCPN in the prediction of laser power fluctuations in natural environments, data collected in the outdoor experiment were used for the neural network model training and laser power prediction. The outdoor meteorological parameters collected included atmospheric temperature, atmospheric pressure, humidity, ambient illuminance, wind speed, and wind direction. These meteorological data and simultaneously collected laser power data were also pre-processed and prepared according to the procedure described in Section 2, and then used to train the TSLSTMFCPN. Once the model training was complete, new data were fed to the model to produce predictions of the laser power for subsequent time steps. The prediction results are shown in Figure 9a along with the measured laser power. Comparing the predicted values with the measured values, it could be concluded that the trained TSLSTMFCPN is capable of making accurate predictions of future laser power fluctuations based on the available laser power data and meteorological data. An MAE value of 0.0121 milliwatt and an MAPE of 1.3% were achieved. It was also observed from the collected outdoor meteorological data that two parameters of atmospheric pressure and ambient illuminance varied little in their values. After the data for these two parameters were excluded from the input data, the training of the model and the prediction of the laser power were performed again, and the results obtained are presented in Figure 9b. Compared with the prediction results shown in Figure 9a, the accuracy of the prediction was improved to a certain extent after this operation, whereby the MAE dropped from 0.0121 milliwatt to 0.0111 milliwatt and the MAPE from 1.3% to 1.2%.
## 2 Materials and Methods
### Loads Priority Model  
The main electrical loads in the Boeing 787 are:  
* **Environmental control system (ECS) and pressurization**. Compressed air produced by gas turbines or bleed air is used for cabin pressure, engine cooling, anti-icing system, and so on. The Boeing 787, uses electric compressors instead of bleed air.
* **Wing anti-icing**. The absence of bleed air makes it possible to use an electric heater in the anti-icing system which is embedded in the wing leading edge.
* **Electrical motor pumps**. Some of the hydraulic pumps in the aircraft have been replaced by electric pumps.  
Figure 3: The overview of the proton exchange membrane fuel cell system (PEMFCS) [49].  
The electrical load management of an aircraft is important during emergency conditions because it must prevent the collapse of the system during operation. In this part of the paper, a priority model is presented. Various factors can affect the priority of having one load on another in an MEA electrical system. These factors include load criticality, expected energy not supplied (EENS) if a load is interrupted and penalty of interruption [51]. An efficient model must be developed to include all of these factors in the optimization formulation. The proposed model calculates a weight for each load in the system. These weights express the priority of loads and can be used in the optimization problem. Also, this weight is dynamic and time-dependent. It means this model can update its weight during different situations. The modeling process is described as follows:
# Total Ionizing Dose Effects on a Delay-Based Physical Unclonable Function Implemented in FPGAs
## 2 Background
### TID on SRAM FPGAs  
TID effects on SRAM-based FPGAs, including non-radiation hardened, have been widely studied in recent years [6; 7; 8]. TID causes a degradation of the transistors as ionizing radiation accumulates on the component. This degradation leads to creating trapped charges that will slowly affect the electrical parameters of the device (threshold voltage \((V_{th})\) and leakage current) [9]. In this context, NMOS and PMOS transistors behave differently. The trapped charges will negatively affect the threshold voltage increasing the leakage current in NMOS transistors. Conversely, in PMOS transistors the threshold voltage will be increased and the leakage reduced. In addition, a deterioration of noise parameters can be observed [10]. All these effects are dependent on many factors such as dose rate, the type of radiation applied, temperature, etc.  
At device level, an increase of the propagation delay of the circuits instantiated in the FPGA is the main aspect to consider. Faults can appear when the timing constraints are violated due to this increment [11].
## 5 Pattern Recognition and Quantitative Diagnosis
### Applications  
The method is applied to the fault diagnosis of a discharge valve leakage fault generated by a reciprocating air compressor of the same type in actual operation in order to further verify the diagnostic performance of the proposed method. Using the same sensors and acquisition parameters to collect data, when the actual RC speed was set to 900 r/min. and the air pipe pressure was 1.0 MPa. The collected data is analyzed by the proposed method to obtain the K and EDRF CIs of the system. Additionally, the extracted K and EDRF were diagnosed while using the designed diagnoser in this paper. Figure 17 shows the diagnostic results.  
The black hollow dot indicates the distribution of the feature points of the actual faulty valve in the diagnoser, as shown in Figure 17a. Black hollow dots are basically distributed in the region III, and more closed to the point sets of 8 mm leakage. Moreover, there are a few points that are distributed to region 2, near the 6 mm leakage point set. Thus, it can be diagnosed that the valve has experienced serious leakage fault, the leakage level is about 7 mm, and the discharge valve needs to be replaced in time.  
Disassemble the compressor cylinder to take out the faulty valve, as shown in Figure 17b. An approximately circular hole of about 5 mm was generated in the middle of the valve and a certain  
Figure 16: Diagnostic device verification; (**a**) 4 mm leakage valve, (**b**) fault diagnosis effect of the valve based on the diagnostic device.  
Figure 17: Diagnosis effect of actual valve leakage fault; (**a**) fault diagnosis effect, (**b**) the disassembled leakage fault valve.  
bending occurred. This bending creates a gap between the valve and the valve body, which can cause gas leakage. Therefore, the above diagnosis results were close to the actual leakage level of the valve, which further proves the quantitative diagnosis ability for the RC valve leakage fault of the method that is proposed in this paper.
# Infrared Spectroscopy as Molecular Probe of the Macroscopic Metal-Liquid Interface
## 2 Materials and Methods  
The present study proposes a more straightforward technique for analyzing the liquid-solid interface. For the purpose of analyzing the molecular interactions at the interface of a metal and an aqueous solution, a modified version of the recently proposed solvent infrared spectroscopy (SIRS) method seems most suitable [18]. SIRS was developed to study the surface chemistry of nanopowders utilizing the influence of the functional groups at the particle surface on the vibrational structure of a solvent. In a typical SIRS experiment, the nanopowder was first pressed onto the internal reflection element of an ATR-IR instrument. In the second step, a solvent, such as water or an alcohol, was added to fill the void space in the fixed bed of particles. The comparison of the solvent spectrum with and without the particles yields information about the molecular interactions at and the chemistry of the surface [18].  
This approach can be modified to study the molecular phenomena at a macroscopic metal-liquid interface. Figure 1 illustrates the proposed modified SIRS technique. Here, the first step is to add a droplet of the fluid onto the internal reflection element. Thereafter, the metal plate is placed on top of the fluid and pressed down by the stamp of the instrument. When the stamp is applying a force on the metal plate, most of the fluid will flow away from the IRE. Eventually, only a very thin film will remain due to the surface roughness of the metal plate. This is beneficial as there will be virtually no bulk fluid left, but the IRE will still be wetted. Consequently, the ATR-IR spectrum will carry information about the molecular interactions at the surface. As a significant advantage, the described technique requires virtually no sample preparation. This is particularly true in comparison to the approaches that are reported in the literature, which were all based on the deposition of a thin film on the IRE.  
As proof of the concept, experiments of three aqueous solutions (pure water, sodium sulfate solution, citric acid solution) at an aluminum surface were carried out on an Agilent Cary 630 instrument equipped with a diamond ATR unit (1 reflection, 2 cm\({}^{-1}\) resolution). Deionized water (18.2 M\(\Omega\) cm) was provided from a Millipore device. Aqueous solutions of 2.1 mol % Na\({}_{2}\)SO\({}_{4}\) and 2.8 mol % citric acid were prepared gravimetrically.
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 2 Implementation of Tracking Component
### Translation Filter  
This filter is used to estimate the instantaneous target's position in the subsequent frames. In the training processing, some fragments are sampled around the original sample (the segment in the bounding box) to construct the training sample dataset. The linear bridge regression is used to find the optimum filter \(w\) which minimizes the squared error over samples \(x_{i}\) and their regression targets \(y_{i}\) stated as Equation (1). Two-dimensional Gaussian Distribution is considered to be the ideal hypothesis for \(y_{i}\) and the peak of Gaussian Distribution is placed in the center of the bounding box which can be presented as the position of the target. In other words, the original sample should be filtered by the optimum filter to formulate a two-dimensional Gaussian Distribution.  
\[\min_{w}\sum_{i}(w^{\mathrm{T}}x_{i}-y_{i})^{2}+\lambda\|w\|^{2} \tag{1}\]  
Figure 1: A visual tracking application for the flight of an unmanned aerial vehicle.  
\(\lambda\) is a regularization parameter which controls overfitting, the same characteristic as in the SVM. The minimizer has a closed-form solution. Given that Discrete Fourier Transform (DFT) is involved in the calculation of correlation for accelerating, the linear bridge regression should be solved in complex field. The generic solution is shown in Equation (2).  
\[W=(X^{H}X+\lambda I)^{-1}X^{H}Y \tag{2}\]  
\(X\) and \(Y\) represent the training dataset and regression dataset, respectively. \(X^{H}\) is the Hermitian transpose, where \(X^{H}=(X^{*})^{T}\) and \(X^{*}\) is the complex-conjugate of \(X\). Circulant matrices are considered to be an excellent implementation in avoiding the extremely high expense for matrices' time-consuming calculation, due to its diagonalizable characteristics by Fourier matrices. In that way, the matrices' Matmul product can be converted into Hadamard product, under the precondition that the training dataset is constructed with cyclically shifted samples around the original sample. The derivation procedure is showed in Appendix A. The derivation is shown in Equation (3):  
\[\hat{W}=\frac{\hat{x}\odot\hat{y}}{\hat{x}^{*}\odot\hat{x}+\lambda} \tag{3}\]  
\(\hat{x}\) represents the DFT of the original sample. In this way, the time complexity is reduced from \(O(n^{3})\) to \(O(nlog(n))\). KCF (kernel correlation filter) propelled Equation (3) forward by introduced the kernel trick which can map the sample space into a high-dimension and non-linear feature space. Kernel trick plays a critical role in SVM and achieves an effective promotion in KCF as well. However, this trick is not adopted in our implementation by contrasting the insufficient improvement of performance with the accompanied expensively time-consuming increase, considering that the FPS of detection should be guaranteed primarily.
## 3 System Description
### Transmission System
As a result, the antenna tuner began to break more frequently, because the amount of backward power in case of mismatch and the voltage peaks were substantially higher. Then, we decided to develop a wattmeter with a directional coupler with a serial connection to the embedded PC. In case of severe mismatch (SWR \(>\) 2), the transmission was immediately aborted at that frequency and the failures were reduced considerably. It is also worth mentioning that we had to face some electromagnetic compatibility issues. First, there were strong mutual interferences between our system and the vertical ionosonde. We had to define an accurate schedule of the tests in order to avoid simultaneous transmissions. Second, our transmitting antenna was radiating our own electronic system along the whole HF band, and both the PC and the FPGA board were halted several times. We had to shield the electronic boards carefully and place the transmitter a minimum of 40 m away from the antenna. At the receiver side, we have also installed three synchronized software radio platforms to allow the simultaneous reception in a monopole, inverted-V and Yagi antenna. The monopole and the inverted-V are vertical and horizontal wideband antennas that allow polarization diversity tests in the whole HF band. We installed the radials for the monopole in the same way as we did in Livingston, and we mounted two lateral masts around the central mast in order to set the inverted-V as horizontal as possible. The directive radiation pattern of the Yagi antenna working around 14 MHz, reduces the amount of noise and interference coming from another directions and allows us to perform tests that need a higher SNR at the receiver. Figure 7: The final version of the transmission system. The watertight box allows the system to resist outdoor conditions. The tent is used for maintenance purposes but it is not necessary at all in normal conditions.
###### Abstract  
This study investigates the use of a custom-built 10 GHz continuous wave micro-Doppler radar system to analyze external vibrations of idling vehicles under various conditions. Scenarios included different gear engagements with one occupant and parked gear with up to four occupants. Motivated by security concerns, such as the threat posed by idling vehicles with multiple occupants, the research explores how micro-Doppler signatures can indicate vehicle readiness to move. Experiments focused on a mid-size SUV, with similar trends seen in other vehicles. Radar data were compared to in situ accelerometer measurements, confirming that the radar system can detect subtle frequency changes, especially during gear shifts. The system's sensitivity enables it to distinguish variations tied to gear state and passenger load. Extracted features like frequency and magnitude show strong potential for use in machine learning models, offering a non-invasive, remote sensing method for reliably identifying vehicle operational states and occupancy levels in security or monitoring contexts. Spectrogram and PSD analyses reveal consistent tonal vibrations around 30 Hz, tied to engine activity, with harmonics at 60 Hz and 90 Hz. Gear shifts produce impulse signatures primarily below 20 Hz, and transient data show distinct peaks at 50, 80, and 100 Hz. Key features at 23 Hz and 45 Hz effectively indicate engine and gear states. Radar and accelerometer data align well, supporting the potential for remote sensing and machine learning-based classification.  
accelerometer; idling vehicle; micro-Doppler radar; microwave radar; spectrogram; vehicle vibration 2020