esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Energy Storage/Release Density  
To estimate the foam energy storage/release density, the heat release while hydrating was measured with a coupled TG/DSC analysis under pure water vapor atmosphere to simulate the typical operation of a TES system working with a closed cycle. The TG/DSC profile of LiCl-PDMS foam under the temperature drop of 80-35 \({}^{\circ}\)C at 12 mbar of water vapor pressure (10 \({}^{\circ}\)C of evaporation temperature) is shown in Figure 7a. The material began to uptake water at \(\sim\)60 \({}^{\circ}\)C, reaching a plateau at 35 \({}^{\circ}\)C, that is, under 12 mbar of water vapor pressure, it corresponded to an RH of \(\sim\)22%. Under this condition, the total percentage of the normalized mass gain was \(\sim\)170%, corresponding to \(\sim\)4 water molecules, which is in good agreement with the DVS analysis (see Figure 5). Hence, the material was overhydrated. From the integration of the heat flow profile, the LiCl-PDMS foam energy density associated with this water uptake was estimated to be \(\sim\)1854 kJ/kg\({}_{\text{foam}}\) (or \(\sim\)323 kWh/m\({}^{3}\)). From the deconvolution of the heat flow signal, as shown in Figure 7b, it is evident that four thermal events (exothermic) occurred while the material hydrated. The mass uptake related to the first peak (\(\sim\)40%) is very close to the one occurring when LiCl converted into its monohydrated phase (42.39%), while the second one began at a temperature of 45 \({}^{\circ}\)C, that is, under 12 mbar of water vapor pressure, it corresponded to an RH of 12.5%, very close to the value at which a saturated solution of lithium chloride will form an equilibrium (12.4%). Similarly to the DVS analysis, the remaining two peaks could be associated with the water intake over the monohydrated threshold, which can likely be caused by the capillary condensation mechanism in the foam.  
Figure 7: _Cont._
## 4 Discussion
### Limitations and opportunities for sensor web based approach
Within NASA, fast delivery of EO-1 remote sensing products has initiated several projects which use SWE to control and access earth observation sensors to monitor the development of hurricanes and wildfires [41].
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 4 Experimental Evaluation and Analysis
### Phase 2  
To adapt to the evolving nature of ransomware, our model employs Incremental Mutual Information (IMI). This method dynamically recalculates feature relevance with each new data batch, ensuring the detection model remains sensitive to emerging attack patterns and maintains optimal feature selection for early detection.  
To mathematically define IMI's feature relevance update, let \(X\) represent the input feature matrix and \(Y\) represent the target class vector. Given a feature set Let \(X=\{x_{1},x_{2},...,x_{n}\}\) and target variable \(Y\), the mutual information between feature \(x_{i}\) and target \(Y\) is denoted as \(I(x_{i};Y)\).  
The dynamic update process begins with calculating the initial mutual information for each feature relative to the target using  
\[I(x_{i};Y)\text{for}\ i=1,2,...,n \tag{17}\]  
When new data arrives, the mutual information for each feature is recalculated and updated as follows. Let \(X_{\text{new}}\) represent the new data. The updated mutual information is given by  
\[I_{new}(x_{i};Y)=\alpha\cdot I_{prev}(x_{i};Y)+(1-\alpha)\cdot I(x_{i};Y\mid X _{new}) \tag{18}\]where \(I_{prev}(x_{i};Y)\) is the previous mutual information value and \(\alpha\) is a weighting factor \((0\leq\alpha\leq 1)\) that balances the impact of new data versus historical data. If new features \(x_{n}+1,x_{n}+2,...,x_{n}+m\) are added, calculate \(I(x_{n}+\mathrm{j};Y)\) for \(j=1,2,...,m\).  
We periodically evaluate feature relevance by updating mutual information scores and deprioritize and remove features with significantly lower scores based on the desired feature count. The model adjustment feedback loop is guided by performance metrics (e.g., accuracy, precision) that trigger feature set re-evaluation if performance deteriorates below a predefined threshold. The weighting factor (\(\alpha\)) in dynamic feature selection balances the impact of historical and new data, determining the influence of past mutual information (MI) values relative to newly calculated MI. \(\alpha\) is adaptively adjusted through correlation analysis that ensures an optimal balance by assessing the relationship between historical and incoming data. The mathematical formulation is next.
## 1 Introduction
In the work reported here, we test the sensitivity of the OSI1::luciferase reporter in yeast and the Kaep1-Nrf2/ARE reporter in murine hepatocytes, in direct comparison, for four test substances. Allicin is known to activate both Yap1 and Kaep1-Nrf2/ARE, but conversely, phenylethyl isothiamate \begin{table}
\begin{tabular}{c c c} \hline \hline
**Substance** & **Activation of Keap1–Nrf2/ARE** & **Activation of Yap1** \\ \hline [PERSON] & [PERSON] et al. (2002) [20] & [PERSON] et al. (2011) [21] \\ Cadmium & [PERSON] (2009) [22] & [PERSON] et al. (1994) [23] \\ Celastrol & [PERSON] et al. (2008) [7] & [PERSON] et al. (2008) [7] \\ Diamide & [PERSON] et al. (2009) [24] & [PERSON] et al. (2000) [25] \\ epigallocatechin gallate & [PERSON] et al. (2016) [26] & [PERSON] et al. (2007) [27] \\ hydrogen peroxide & [PERSON] et al. (2013) [28] & [PERSON] et al. (2000) [25] \\
5-hydroxymethylfurfural & [PERSON] et al. (2017) [29] & [PERSON] and [PERSON] (2013) [30] \\ Methylglyoxal & [PERSON] et al. (2017) [31] & [PERSON] et al. (2004) [32] \\ Resveratrol & [PERSON] et al. (2010) [33] & [PERSON] et al. (2012) [34] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A selection of substances shown to activate both the Kaep1–Nrf2/ARE and Yap1 oxidative stress response systems in mammals and in yeast, respectively. (PEITC), allyl isothiocyanate (AITC), sulforaphane (SFN) are known to activate Keep1-Nrf2/ARE but it was unknown whether they activated Yap1. A congruence for response by the two reporter systems was shown in all cases. Furthermore, we tested the OSI1::luciferase reporter with monomethyl fumarate (MMF) and dimethyl fumarate (DMF), non-isothiocyanates but \(\alpha\)-\(\beta\)-unsaturated electrophilic oxidants known to activate Keep1-Nrf2/ARE and 3 kavalactones (methyscitin, yangonin, kavain) and a diterpene lactone (andrographolide), weak antioxidants which activate Keep1-Nrf2/ARE by a non-oxidative mechanism. We discuss the advantages offered by the yeast-based method compared to the animal cell method for large scale screening procedures.
## 7 Conclusions  
In this study, we conducted a performance evaluation and analysis of GMM-based features for sparse trajectories without semantic information. The experiments utilized three machine learning models, namely, SVC, RF, and DNN, to perform a detailed examination of the features. We also compared them with the IDNN, an existing feature extraction method for trajectories. The results showed that GMM-based features achieved higher classification accuracy and better generalization performance than IDNN-based features. In particular, the RF algorithm attained high accuracy, whereas the SVC algorithm maintained stable generalization performance. Moreover, a sensitivity analysis of the missing rate \(\theta_{\text{th}}\), which indicates trajectory sparsity, revealed that the IDNN becomes more prone to overfitting as the sparsity increases, whereas the GMM-based approach can sustain accuracy and generalizability performance. These contributions demonstrate that GMM-based features offer clear advantages over existing methods in terms of both accuracy and generalizability when dealing with sparse trajectories lacking semantic information. Consequently, this study shows that sparse trajectory data can be effectively utilized even without semantic information. This study expands the potential for data utilization in various fields, including urban planning, transportation analysis, and public policy.  
The limitations of this study are as follows:  
* Although simple linear interpolation was adopted for trajectory imputation, its impact on classification performance was not verified.
* The proposed method was verified only for a single region. In addition, owing to constraints in the data acquisition period, the influence of behavioral changes from prepandemic norms during the COVID-19 era could not be taken into account.
* The features did not contain the connection relationships of RPs.  
In light of these points, our future research will verify the generality of the proposed model in terms of the area and period of location data and further consider the utilization of sparse spatial-temporal information.  
Conceptualization, Y.K., Y.O. and H.T.; methodology, Y.K., Y.O. and H.T.; software, Y.K.; validation, Y.K.; formal analysis, Y.K., Y.O. and H.T.; investigation, Y.K., Y.O. and H.T.; resources, Y.O.; data curation, Y.K., Y.O. and H.T.; writing--original draft preparation, Y.K.; writing--review and editing, Y.K., Y.O. and H.T.; visualization, Y.K.; supervision, Y.K.; project administration, Y.K.; funding acquisition, Y.K. and Y.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS Grant-in-Aid for Early-Career Scientists (Grant No. 23K13517 to Y.K.).  
Not applicable.  
The data used in this study were obtained from Agoop Corp. and are available for purchase at [[https://agoop.cojp/](https://agoop.cojp/)]([https://agoop.cojp/](https://agoop.cojp/)) (accessed on 16 January 2025) with the permission of Agoop Corp.  
The authors declare no conflicts of interest.
# Highly Linear 2.6 GHz Band InGaP/GaAs HBT Power Amplifier IC Using a Dynamic Predistorter
## 2 Design of the Dynamic Predistorter  
### Circuit Configuration  
The conventional shunt diode predistorter depicted in Figure 2a uses a biased diode connected in parallel to the RF signal path with a series capacitor (\(C_{opt}\)) [9]. \(C_{opt}\) directly affects the RF power coupling to the predistorter. The voltage distribution across the predistorter is divided between the diode and \(C_{opt}\). The nonlinearity of the diode is represented by a nonlinear resistance, \(R_{diode}\), which is adjustable through the reference voltage (\(V_{REF}\)). Thus, the conventional predistorter, with two parameters (\(C_{opt}\) and \(V_{REF}\)), offers limited control over its ability to compensate for the dynamic nonlinear characteristics of PAs.  
The proposed dynamic predistorter, illustrated in Figure 2b, utilizes a suitably biased diode-connected bipolar transistor (Q3). It is connected in parallel to the RF signal path at the inter-stage matching network via a series resistor (\(R_{E}\)) and inductor (\(L_{E}\)). A shunt capacitor (\(C_{PD}\)) is added at the base of Q3 to provide degeneration to Q3. The degeneration capacitance, \(C_{PD}\), works with the diode, \(R_{E}\), and \(L_{E}\) to divide the voltage [10]. \(R_{E}\) and \(L_{E}\) primarily regulate the signal coupling from the RF signal path. The voltage across the proposed predistorter is then split among the diode, \(C_{PD}\), \(R_{E}\), and \(L_{E}\). The proposed predistorter features additional control parameters such as \(C_{PD}\), \(V_{REF}\), \(R_{E}\), and \(L_{E}\), which influence the overall nonlinearities generated by the predistorter and enhance the capability to compensate for dynamic nonlinear characteristics across a wide power range of the PA.  
Figure 2: Schematics of diode predistorters: (**a**) conventional, (**b**) proposed.
## 4 Discussion
### Limitations and Future Directions  
#### 4.4.1 Constrained Sample Size and Data Collection Duration  
The study was conducted with a relatively small sample size of 8 participants, which may limit the applicability of the findings. Additionally, the data collection period for each participant was limited to five minutes. Such a brief duration may not capture the full spectrum of RR variability throughout different sleep stages. However, we added variety to the data by instructing participants to breathe in different patterns (e.g., fast, slow, and normal) to make the data varied enough to reflect real-world sleeping conditions.
# Wear Model of Silicon Nitride Ceramic Balls in Three-Body Coupling Grinding Mode
## 4 Wear Mode of Silicon Nitride Ceramic Balls during Grinding
### Conversion between Two-Body Wear and Three-Body Wear  
The micro-scale wear test (also known as the indention abrasive wear test of ball blanks), in which one ball blank rotates around the sample in the presence of fine abrasive slurry, is currently the standard wear resistance test of coating film processing in the project [61] funded by the European Union.  
Figure 11: (**a**) Typical SEM surface image obtained after two-body wear; (**b**) typical SEM surface image obtained after three-body wear [60].  
Figure 12 shows that the ball blank is sandwiched between coaxial rods and driven by a motor. A flat sample is vertically mounted on the rotating arm and loaded by the static weight suspended on the horizontal shaft against the ball.  
In the micro-scale wear test, the dominant wear mode is influenced by the external load, volume fraction of the abrasive in the slurry, wear materials, materials of the ball blank and sample, and surface conditions of the ball blank [60; 61]. For all tested abrasives, the two-body groove wear mechanism is dominated by a high load and/or low slurry concentration. The mechanism dominated by a low load and/or high slurry concentration is a three-body process, in which abrasive particles are not embedded but are rolling between two surfaces, producing a severely deformed and multidented worn surface, and no obvious surface directionality exists. At a moderate load and/or slurry concentration, some wear marks present mixed characteristics, with a two-body groove in the center and three-body rolling around.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 4 Supporting TSN in WiFi 7
### Network Configuration  
The IEEE 802.1 Qcc TSN sub-standard defines a set of management interfaces and protocols to enable TSN network administration. It allows standard, consistent setting of policies to manage the available network resources (e.g., communication paths, bandwidth, scheduling patterns, and so forth) with the aim of meeting time-sensitive needs of end applications.  
As it can be observed in Figure 2, wireless TSN networks are expected to seamlessly extend the wired TSN domain, with which will share two main management entities in an SDN-inspired centralized scheme:  
* The centralized user configuration (CUC), which receives requirements from users (blue lines) of TSN network services (talkers and/or listeners) and communicates them to the central network controller (CNC).
* The central network controller, which receives consolidated requirements from the CUC. Then, it is responsible for determining routes, scheduling TSN flows through the network, and configuring TSN-enabled bridges accordingly (red lines).  
Under this scheme, APs can be seen as wireless TSN bridges configured by the CNC, whereas STAs act as talkers or listeners with permanent communication with the CUC. To cope with the variability introduced by fading and mobility, TGbe is currently considering multiple wired-wireless TSN integration and configuration models, resulting from the balance between network management complexity and efficiency [34].
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 1 Introduction
### Related Reviews  
The literature covers other surveys that examine programmable data plane applications in telecommunication networks, including those for 4G and 5G. [PERSON] et al. [12] present a comprehensive review of programmable data plane switches and P4 language, encompassing various applications, including telecommunication services. The authors analyze each study, compare their results, and discuss limitations. However, the paper has a limited number of articles related to 5G and its associated technologies and does not include recent studies as it was published in 2021.  
[PERSON] et al. [13] offer an overview of P4 implementations in various fields, such as next-generation mobile networks. Nevertheless, this survey compiles a limited selection of studies and does not extensively analyze the detailed findings reported within them. Although the authors propose future research directions, there are no specific prospects for telecommunication networksHauser et al. [14] provide a tutorial on data plane programming and an extensive survey of P4 and applied research, including cellular networks. The article includes an analysis of the results and potential new research directions. However, it lacks an architectural classification for 4G/5G applications and does not include recent studies in the field.  
[PERSON] et al. [15] present a survey of programmable data planes, recognizing their role as an enabler for diverse in-network computing (INC) applications. The article provides a technical definition of INC and introduces a thorough classification of its uses, including 4G/5G/6G technologies. In this context, the authors analyze various implementations and conduct comparative assessments of their results. However, this study does not include several articles with relevant applications and does not provide information about code availability.
## 2 Materials and Methods
### Thermal Processing Procedures  
Following production, the sample underwent thermal debinding and sintering heat treatments. The sample produced by the 3D printer was subjected to TGA (thermogravinetric analysis) to determine the required temperature for the complete removal of the polymer material. The debinding process was conducted up to a temperature of 400 \({}^{\circ}\)C, as identified from the TGA analysis, and the sample was held at this temperature for 1 h before being allowed to cool. The thermal cycle used for the debinding process is presented in Figure 3. A cylindrical furnace (controlled by a Honeywell DC2500 controller, Honeywell, Charlotte, NC, USA) with a mulitte tube was used for sintering. The debinding cycle is shown in Figure 3a. Two different thermal cycles were used during the debinding process of the samples. In the first cycle, the temperature was increased to the debinding temperature at a slow rate (0.2 \({}^{\circ}\)C/min) and then reduced to room temperature at the same rate. The sample subjected to this process was designated as 316 LLS (low speed) in the study. The sintering cycle applied after the debinding process is shown in Figure 3b. To prevent potential contamination, the sintering process was carried out in an argon gas atmosphere.  
Figure 1: Sample dimensions. (**a**) Desired part dimensions; (**b**) CAD model used.  
Figure 2: Flow chart of the shaping, debinding, and sintering (SDS) process.  
After optimizing the debinding process, tensile samples were produced with four different layer thicknesses (100, 200, 300, and 400 \(\upmu\)m) to investigate the effect of layer thickness on the product properties. For density measurement, an Archimedes density measurement kit was used, connected to a precision scale with an accuracy of 0.1 mg. In the study, Vickers hardness testing (Hv: hardness of Vickers) was conducted with a 200 g weight. Five measurements were taken from different regions of the surface, and the average was calculated. The Vickers hardness measurements were performed using the Veica brand VM HT MOT model microhardness tester. The highest and lowest measured values were expressed with error bars.  
Tensile tests were conducted according to the ASTM E8/E8M standard [22] at a 0.5 mm/min pull rate and room temperature. Each test was repeated with three samples, and the average of the results was used.
# A Comprehensive Survey for Deep-Learning-Based
## 4 Abnormality Detection with Images over Long Range
### Grid Anomaly Detection Based on Satellite Positioning
#### 4.2.2 Tower Pole Monitoring and Settlement Detection  
The high-precision positioning results of GNSS in static measurement are often used in abnormality detection tasks such as tower pole detection in the power grid. The power tube pole is a necessary facility to carry the power supply, whose free operation is of great significance to ensure the safe operation of the power grid [89]. However, traditional manual detection methods are time consuming and labor intensive and do not guarantee good real-time performance. Therefore, [PERSON] et al. [90] designed a high-precision satellite data  
Figure 5: Application of satellite-positioning-and-image-based abnormality detection in power systems.  
acquisition system for the power grid environment. Furthermore, to solve the problems of low monitoring accuracy, high false alarm rate, untimely pre-warning, and high equipment failure rate of the transmission line tower rod oblique posture monitoring system, [PERSON] et al. [91] proposed a transmission line tower rod oblique monitoring system based on reverse network RTK. Moreover, [PERSON] et al. [92] proposed technology for monitoring the tower pole tilt attitude based on the RTK technology of a Beidou reverse network and measuring the tilt attitude information of a transmission line tower by using its height and position information. Then, using this system, the risk assessment is carried out, which can control the tilt attitude and assess the risk of the transmission line tower.