esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermalization of a Trapped Single Atom with an Atomic Thermal Bath
## 1 Introduction  
One of the current goals in physics is to fully understand the thermodynamics of quantum systems [1]. The understanding of how open quantum systems behave thermodynamically is especially important, as it could be the key to unveil how classical mechanics emerges from the quantum, and will help the development of quantum technologies. The paradigmatic model of open quantum systems is the _spin-boson model_[2, 3], i.e., a system in which a single two-level particle interacts with a thermal bath of harmonic oscillators. In this work, we considered an experimentally implementable extension of such a celebrated model: a single atom trapped in a harmonic potential interacting with a bath of ultracold atoms of a different species.  
Ultracold atoms are highly controllable and versatile systems that can be used to simulate a large variety of quantum phenomena [4, 5]. Optical tweezers are emerging as one of the most promising and powerful tools for quantum simulations with cold atoms [6, 7, 8], and have been recently proposed as a platform to study quantum thermodynamics and implement single atom engines [9]. In the latter system, a single atom interacts with a thermal bath and undergoes various thermodynamic cycles. One or more of the strokes of these engines involves the thermalization of the single atom with a bath of atoms of a different species. With an eye to such quantum engines, we simulated the interaction of a single atom in an optical tweezer with a thermal gas of atoms trapped in a macroscopic trap. The thermalization process studied here could also be used as an alternative method to cool single trapped atoms and bring them to the ground state of their tweezer potential, which can currently be accomplished only with laser cooling [10].  
The article is organized as follows. In Section 2, we describe our system and provide the theory that we have used to simulate our system. In Section 3, we provide the results of a Monte Carlo simulation where we show how the thermalization rates vary and how the trapped atom interacts with a bath of atoms undergoing evaporative cooling. Section 4 is devoted to the conclusions.
## 2 Stacked Power Amplifiers
Figure 1: Schematic of the stacked PA.  
Figure 2: Simulated voltage swings at the drain nodes of M1,2,3,4. Output matching (see Figure 1) consists of a parallel DC feed inductor L\({}_{2}\) and high Q (HQ) metal-insulator-metal (MIM) capacitor C\({}_{6}\). These (along with stack transistor sizing and gate capacitors) are optimized to the R\({}_{\rm opt}\) and to provide good gain. The operation point of the PA is selected in moderate class AB by using external analog controls for M\({}_{2}\), M\({}_{3}\) and M\({}_{4}\). M\({}_{1}\) is biased using a digitally controlled, variable current (current DAC) source followed by a diode-connected transistor. The gate bias of M\({}_{1}\) can be tuned with 3 bit control from 100 mV up to 700 mV. V\({}_{\rm gs}\) = 450 mV is set as a nominal gate bias value for each transistor. In order to prevent breakdown, V\({}_{\rm g2}\), V\({}_{\rm g3}\) and V\({}_{\rm g4}\) are derived from 4.6 V VDD in an external control board and thus turned on at the same time. An additional precaution is taken with R\({}_{1}\) and R\({}_{2}\), which prevent breakdown by setting a 100 mV voltage at V\({}_{\rm g1}\) in case the VDD is turned on while the current DAC is set to 0. The input matching is implemented using a high density (HD) metal insulator metal (MIM) capacitor C\({}_{1}\) and two turn L\({}_{1}\), from which the signal is fed to the transistor core via  
Figure 4: Layout of the PA core of the four-stack PA.  
Figure 3: Schematic of one power cell of the stacked PA.  
the center tap of L1. This provides compact and high Q matching around 26 GHz. As the input impedance of M1 is inherently capacitive, the input matching is designed so that it resonates out the capacitive load and maximizes the power transfer to the PA. Resistive parasitics of the L1 play a significant role in the resonator's Q factor and thus, by feeding the signal out from the center tap of the L1, the resistive parasitics are decreased significantly. The DC is blocked from input and output nodes by using HD MIM capacitors C1 and C8 of 1 pF, which are low loss capacitors at mmWave frequencies. In addition, a large 10 pF HD MIM capacitor is used in the bias feed to provide a sufficient RF ground. An ADS Momentum EM simulator is used to design the input and output matching circuit, while the PA core (see Figure 4) is verified using parasitic extraction.
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 5 Discussion
First, the generation of scalograms using CWT is more computationally expensive and requires significantly more memory than the usage of 1D time domain signals, which is especially problematic when pre-training or fine-tuning on massive datasets. Nevertheless, the proposed model is intended for fine-tuning on relatively small datasets, so it should not be a major concern. Secondly, due to memory and computational resource constraints, the deep 2D CNN may be too computationally expensive for use in embedded systems and intelligent wearable devices, such as smartphones, smart-watches, and fitness bracelets. Nevertheless, cloud computing and distributed computing can significantly mitigate the aforementioned problems. Summarizing the information discussed in this section, it can be claimed that the usage of the proposed pre-trained model, especially with layer freezing, results in a more stable gradient descent, faster training, and improved performance on small datasets. However, the usage of the pre-trained model with datasets of medium and large sizes may result in an accuracy decrease and, therefore, a negative transfer. In this study, a general case of research was conducted. The proposed methods and models can be used, for example, to recognize repetitive movements of production personnel in enterprises. Therefore, it is anticipated that future research will expand the recognition of personnel movements, such as component assembly while seated, collaborative robot assembly operations while standing, and personnel position changes.
## 4 TSN Network-Wide Scheduling--Approaches Overview, Challenges, and Future Directions
### Literature Analysis
#### 4.2.5 Diversity of Shapers/Use Case Specifications  
Although earlier works focus on isochronous traffic, the later works consider other traffic types, too. The diversity in traffic specifications also includes considering multicast streams. Early works usually assume unicast streams, but later on some works [49, 60, 69, 70, 88, 96, 101, 112] considered multicast streams. Other works tried to schedule sporadic traffic while imposing some constraints on the interframe arrival time. However, scheduling sporadic traffic is still challenging due to the criticality of the traffic in terms of latency while being irregular. Related to the diversity of the traffic specification, we noticed the increasing consideration of shapers other than TAS, although TAS is still dominant and most attractive. CQF is gaining momentum, especially in the context of multidomain optimization. We expect it to gain more and more interest since it tolerates bigger jitter than TAS while keeping the synchronous nature. Similarly, combining different shapers/shaping techniques is a very interesting direction of research as it has great potential. However, only a few works exist in this area, such as [95], which studies the combination of TAS with frame-preemption, and [113] which studies the combination of CBS with frame-preemption. Ref. [83] studied combining TAS with CBS and [11] studied combining TAS with CQF. It would be interesting, in this context, to study the effect of frame-preemption [12] on traffic shaping, which could improve the performance for urgent-high-priority frames further. Another interesting direction for future studies is to analyze the relation between the length of the tolerance interval and the slope accumulation rate as well as the rate of frame drops and the rate of frames missing their allocated windows.
# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 3 Results
### NDVI Fusion
Strong correlations were found between the LS5 and LS5_fused (\(r\geq\) 0.91, \(p\) < 0.05) and LS7 and LS7_fused (\(r\geq\) 0.95, \(p\) < 0.05) records. The MODIS NDVI record was also strongly correlated with the LS5_fused (\(r\geq\) 0.99, \(p\) < 0.05) and LS7_fused (\(r\geq\) 0.97, \(p\) < 0.05) results. These results indicated that the LS5_fused and LS7_fused records preserve similar variability as the native LS5, LS7 and MODIS NDVI records. There was no significant difference between LS5_fused and LS7_fused NDVI records from the different crop types for the 2008 to 2011 period of overlapping LS5 and LS7 observations. These results indicated the reasonable accuracy and performance for our agricultural application, allowing production of a continuous (2008 to 2015) 30-m, eight-day NDVI record for MT derived using the linear regression model developed between overlapping 250-m MODIS and 30-m Landsat 5 (2008 to 2011) and Landsat 7 (2012 to 2015) NDVI records. Figure 3: \(Cont.\)
# Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity
## 3 GPR Principles
When such energy meets an object, a hyperbolic response is created,with the object always rationalized at its barracks. The reflection will appear at an apparent depth \(d=\frac{vt}{2}\) below the receiver because the travel time must obey the hyperbolic equation:  
\[\left(\frac{vt}{2}\right)^{2}-h^{2}=z^{2} \tag{3}\]  
where \(h\) is the distance from the midpoint of the receiver and transmitter to the target object, and \(z\) is the distance of the object from the system. The dissimilar behavior of objects within GPR signals is due to varying electromagnetic properties, mainly dielectric constants. Objects with different dielectric constants bid partial return of the signals, resulting in exaggerated responses, while boundaries drive transmission and the disobedience of signals, thus producing different return shapes. Decoding subsurface properties from radar signals works out well-defined opposition reversals, to sum up the considered data. The ground impulse response, as depicted in Figure 4, antenna system impulse response, and transmitter output are convolved to create the received signal. Interpretation implies the method of analyzing the amplitude, frequency, and time of the reflected signals. GPR is one of the most valuable instruments for subsurface surveys due to its imaging, non-invasive methodology, and applicability in many areas. One of the most effective uses of GPR and timewise is its application in research and the evaluation of infrastructure. This migration technique improves the spatial resolution of GPR images. It involves transforming data from the time-space domain to the frequency-wavenumber domain using Fourier transforms. Kirchhoff migration is a common method used to create subsurface images from GPR data. It calculates the reflection amplitudes by summing the contributions from all points on the reflector surface.
# On Data Leakage Prevention Maturity: Adapting the C2M2 Framework
## 2 Materials and Methods
### Input Knowledge
#### 2.2.2 DLP Solutions
The hybrid approach is particularly effective in identifying sensitive content, achieving higher detection accuracy than pure context-based methods. Therefore, content-based methods typically achieve higher accuracy but are susceptible to bypassing through data obfuscation. Context-based approaches, while often not as accurate, provide a broader scope of detection by considering user behavior and data context. Hybrid methods aim to balance these aspects, offering a more robust solution to DLP. As illustrated, the domain of DLP techniques presents a diverse array of approaches. However, it is important to recognize that each method, while effective in certain contexts, also has its limitations.
# Abstract Text Summarization with a Convolutional Seq2 seq Model
## 2 Related Work  
### Automatic Text Summarization  
Lots of previous work in summarization focuses on extractive methods, that is, directly extract key words or sentences from the original document and then reproduce them as summary [12; 13; 14; 15].  
In contrast, humans tend to summarize the source text by using paraphrase in their own words and seldom reproduce the original sentences from the document. Inspired by this intuition, many researches used the DUC-2003 and DUC-2004 competitions [[http://duc.nist.gov/](http://duc.nist.gov/)]([http://duc.nist.gov/](http://duc.nist.gov/)) to standardize such generative summarization tasks. In generative tasks, the datsets are composed of stories of news with diverse topics, and each story contains several reference summaries generated by humans. TOPIARY [16] utilized a combination of compression techniques motivated by linguistic, and an unsupervised topic detection method which appends keywords extracted from the story to the compressed output.Reference [17] used a conventional phrase-table based machine translation model to realize text summarization. Reference [18] leveraged weighted tree-transformation rules to compress the source text. Reference [19] utilized quasi-synchronous grammar methods to generate summaries.  
Deep learning has been developing fast recently, and it can handle many NLP tasks [20]. So researchers have began to consider such framework as an effective, completely data-driven alternative for text summarization. Reference [21] used convolutional modes to encode the original text, and an attentional feed-forward neural network to generate summaries. But it did not use hierarchical CNNs, thus not that effective. Reference [22] is an extension to [23], and it replaced the decoder with an RNN. Reference [24] introduced a large Chinese dataset for short text summarization and it used a RNN based seq2 seq model. [25] utilized a similar seq2 seq model with RNN encoder and decoder, but it contained English corpora, reporting advanced results on DUC and Gigaword datasets. Reference [26] utilized the generative adversarial networks to generate the abstract text summarization, meanwhile equipped with a time-decay attention mechanism. Reference [27] was based on BERT to take advantage of the pre-trained language model in the seq2 seq framework, and designed a two-stage decoder process to consider both sides' context information of every word in a summary. We denoted the above two methods as GAN and BERT respectively, and we will compare our model with them in experiments in detail. Reference [28] proposed a two-stage sentences selection model based on clustering and optimization techniques. Reference [29] proposed a text summarization model based on a LSTM-CNN framework that can construct summaries by exploring semantic phrases. Reference [30] presented an automatic process for text abstraction which was based on fuzzy rules on a variety of extracted features to find the most important information from the source text.
# A Reversed-Phase HPLC Method for Determination of Osteopontin in Infant Formula
## 3 Results and Discussion  
### Development of RP-HPLC Method  
To develop the RP-HPLC method, previous studies on the determination of OPN using HPLC were compared and summarized in Table 1, along with the parameters followed in this study. A proper wavelength was selected and followed (214 nm), where OPN showed the maximum absorbance. The contributing effect of solvent was also checked on the absorbance maxima of OPN. The mobile phase conditions were optimized to achieve the best and quickest separation. In this work, 0.1% TFA was used as mobile phase A and several phase systems were tried for mobile phase B (Section 2.3). We found that 60% ACN in 0.1% TFA performed better than others and the peaks appeared immediately after 4 min in a total of 10 min, which is faster than previous studies reported in the literature (Figure 1). The higher polarity index of ACN (5.8) compared to 2-propanol (4.0) might be the reason of the better performance of ACN [18]. Moreover, the isocratic operation of HPLC was performed using 20% of mobile phase A and 80% of mobile phase B, together with other parameters provided in Table 1 (column 6).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**RP-HPLC Conditions** & **[9]** & **[12]** & **[13]** & **[10,11]** & **This Study** \\ \hline \multirow{2}{*}{**Purpose**} & Human OPN only as a biomarker & Determination of C-terminal of bovine OPN to investigate its & Separation of N- and C-terminal of to evaluate binding of human OPN to integrin & Identification and characterization of bovine measurement of bovine OPN in infant formula \\ \hline \multirow{2}{*}{**Column**} & C18 & C2C18 & C18 & C4 & C18 \\ \hline \multirow{2}{*}{**Mobile phase-A**} & 0.1\% formic acid & 0.1\% TFA & 0.1\% TFA & 10\% ACN in 0.1\% TFA & 0.1\% TFA \\ \hline \multirow{2}{*}{**Mobile phase-B**} & \multirow{2}{*}{0.1\% formic acid in ACN} & 60\% ACN in 0.1\% TFA & 75\% of 2-propanol in 0.1\% TFA & 90\% ACN in 0.1\% TFA & 60\% ACN in 0.1\% TFA \\ \hline \multirow{2}{*}{**Elution**} & Gradient, 7 min & Gradient, 54 min & Gradient, time NR * & Gradient (28 min) followed by isocurta (28 min); total 56 min & Isocratic, 10 min; 20\% of mobile phase A and 80\% \\ \hline \multirow{2}{*}{**Temperature**} & 40 \({}^{\circ}\)C & NR & NR & 40 \({}^{\circ}\)C & 40 \({}^{\circ}\)C \\ \hline \multirow{2}{*}{**Absorbance**} & NR & 214 nm & NR & 214 nm & 214 nm \\ \hline \multirow{2}{*}{**Sample injection**} & 25 \(\mu\)L & NR & NR & 4 \(\mu\)L & 10 \(\mu\)L \\ \hline \multirow{2}{*}{**Flow rate (m/min)**} & \multirow{2}{*}{0.3} & \multirow{2}{*}{0.15} & NR & NR & 0.3 \\ \cline{4-6}  & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of high-performance liquid chromatography (HPLC) parameters with previous studies. TFA, trifluoroacetic acid; ACN, acetonitrile.
# Non-Destructive Infrared Evaluation of Thermo-Physical Parameters in Bamboo Specimens
###### Abstract  
The estimation of heat conduction properties has considerable importance in the characterization of bamboo with respect to its potential use as an alternative construction material. Even though traditional methods such as hot plates have successfully measured thermal parameters, like thermal diffusivity and conductivity in bamboo samples, it is still necessary to transform the cylindrical bamboo specimen into a piece with special geometry and size. This requirement makes this method impractical in applications where several bamboo specimens need to be measured in their original cylindrical shape. This paper presents the estimation of thermo-physical parameters \(k\) and \(\rho c_{p}\) in Guadua angustifolia kunth (Guadua a.k.) bamboo through nonlinear least square optimization and infrared thermography. A sensitivity analysis was carried out to determine how the temperature on the bamboo surface is affected by changes in the convection coefficient \(h\), thermal conductivity \(k\), and volumetric heat capacity \(\rho c_{p}\). In spite of the nonlinearity and high correlation in the parameters of the inverse heat conduction problem (IHCP), the estimation of such parameters is robust and consistent with those reported in the literature.  
inverse heat conduction problem; infrared thermography; thermo-physical properties; heat transfer coefficient; Guadua a.k.
## 3 The Proposed Method
### HI Extraction and RUL Prediction Process
#### 3.2.2 Overall Framework Flow
Thus, \(\bar{\mathbf{v}}_{\mathbf{k}}\) can be expressed as: \[\bar{\mathbf{v}}_{\mathbf{k}}=f\Big{(}\Big{[}\mathbf{v_{M-k}},\mathbf{v_{M+1}},\ldots,\bar{ \mathbf{v}}_{\mathbf{M+2}},\ldots,\bar{\mathbf{v}}_{\mathbf{M+(k-M-1)}}\Big{]}\Big{)}\] (23) Meanwhile, the matrix \(\mathbf{V}\) is updated as follows: \[\mathbf{V}=[\mathbf{v_{1}},\ldots,\mathbf{v_{M+1}},\bar{\mathbf{v}}_{\mathbf{M+2}},\ldots,\bar{ \mathbf{v}}_{\mathbf{k}}]^{T}\] (24) where \(\bar{\mathbf{v}}_{\mathbf{k}}=[h_{k},\ldots,h_{N},\bar{h}_{N+1},\ldots,\bar{h}_{k+N-M-1}]\). When \(\bar{h}_{k+N-M-1}\) is less than the threshold 0, the prediction step is stopped, and the RUL of the bearing is obtained as \((k-M)\times T_{s}\), and \(T_{s}\) is the sampling interval of the vibration sensor. If the prediction result is not less than the threshold, the iterative prediction is continued until the predicted value is less than the threshold and the corresponding RUL is obtained.
# Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals
## 2 CT/PET Joint Sparsity  
Usually, despite quite dissimilar physical features utilised in the PET and CT, the reconstruction procedure involves their projections side-by-side. It means that the scans are being restored in isolated methods, but both can utilise the same image features and motion trajectory. However, the reconstruction procedure may be simplified due to inherent objects' similarities and important common information being shared between them. This feature could be applied in combination with other extracted structures to make motion artefacts smaller. Instead of separated reconstruction steps, the joint sparsity method combines two sparsely sampled data sets allowing these structural similarities to increase the image resolution and minimise the motion artefacts. The proposed algorithm solves the following constrained cost function. In this equation, xCT and xPET promote their data sets' sparsity by exploiting the Compressed Sensing. The joint sparsity is the most influential subject in the use of structural information between PET and CT scans, and it can be expressed in the following way:  
\[\begin{bmatrix}\chi(x_{CT}^{i})\\ \chi(x_{PET}^{i})\end{bmatrix}_{2}=\sqrt{\left(\chi(x_{CT}^{i})\right)^{2}+ \left(\chi(x_{PET}^{i})\right)^{2}} \tag{1}\]  
where \(x_{CT}^{i}\) and \(x_{PET}^{i}\) are 3D image volumes, and \(\chi\) is a sparsifying transform.  
In this way, the regularisation performed at each voxel could be expressed as follows:  
\[\kappa^{i}=\|\chi(\mathrm{input}-C_{CT}^{i})|-|\chi(\mathrm{input}-P_{PET}^{i })\| \tag{2}\]  
The regularisation parameters are employed to avoid using non-coherent CT and PET images' features overlapping. The method proposed in this paper utilises joint sparsity, i.e., in CT's and PET's domains, see Figure 1.  
Figure 2: The proposed CT/PET image enhancement algorithm.
## 2 Materials and Methods
### Description of Data  
For this study, daily rainfall readings gathered by nine rainfall neighbouring gauging stations, including the target station (Ratnapura), were considered. The other eight stations are Balangoda (St1_Bal), Detanagalla (St2_Det), Elston (St3_Els), lumbuluwa (St4_III), Keragala (St5_Ker), Moralioya (St6_Mor), Nivithigala (St7_Niv), and Ulinduwawa (St8_Uli). These eight stations are considered as reference stations.  
The total number of days accounted for in this study is 1826. As mentioned earlier, these reference stations consist of various discontinuous missing patterns within the study period (1 January 2015-31 December 2019). The following heatmap (Figure 3) depicts the missing patterns in the data of eight reference stations and Table 1 summarizes percentages.  
Figure 3 depicts the data availability with blue color and inconsistent missingness with white blocks along the time and space. With respect to the figure, it can be noted that, in most cases, the missingness has occurred as a block (i.e., a sequence of missing values). The percentage of missing values of the last two sub-stations, as indicated in Table 1, are 68.4% and 73.55%, respectively. Since more than half of the data values are missing, these two sub-stations were excluded from the analysis. The stations St1_Bal, St2_Det and St5_Ker had random missing cases (noncontinuous missing values). Those were interpolated using the average values of the neighbouring rainfall values of the  
corresponding station. Finally, there were six reference stations considered for assessing the suitability of the proposed methodology.