esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Power 8T SRAM Compute-in-Memory Macro for Edge AI Processors
## 5 Conclusions  
This paper presents a 128-byte (128 B) 8-transistor (8T) SRAM-based compute-in-memory (CIM) architecture that performs in-memory multiplication of input and weight values directly within bit-cell, where these values are represented in binary as 0 or 1. This innovative approach reduces data transfer overheads and enhances processing efficiency, making it particularly suited for AI edge devices. Simulation results demonstrate that the proposed design achieves a 66% reduction in power consumption during read operations and a 13.7% reduction during write operations compared to conventional 8T SRAM designs. Additionally, the improved noise margin (over 100% increase in read noise margin and approximately 29% increase in write noise margin) ensures reliable data retention even with environmental variations, such as temperature fluctuations or supply voltage changes. This significant power savings and noise resilience underscore the architecture's potential for extending battery life and maintaining stability in energy-constrained applications. Furthermore, if implemented as cache memory in electronic systems, this SRAM structure could significantly enhance overall system performance by increasing access speed, which is particularly beneficial for applications demanding rapid data retrieval and high throughput.  
Conceptualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; methodology, H.-[PERSON]; validation, H.-[PERSON]; data curation, H.-[PERSON]; writing--original draft preparation, H.-[PERSON]; writing--review and editing, [PERSON]-H.J.;  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Biswas [8]** & **Si [9]** & **Si [10]** & **Gonugondla [11]** & **Proposed 8T** \\ \hline Technology & 65 nm & 65 nm & 55 nm & 65 nm & 90 nm \\ \hline Cell Type & 10T & Split-6T & Twin-8T & 6T & 8T \\ \hline Operating Voltage & 1.2 V (DAC)/0.8 V (Array)/ 1 V (rest) & 1 V & 1 V & 1 V/ & 1 V/ 0.6 (nmos) \\ \hline Memory Capacity & 2 kB & 512 B & 480 B & 16 kB & 128 B \\ \hline Input Precision & 6 & 1 & 1–4 & 8 & 1 \\ \hline Weight Precision & 1 & 1 & 2–5 & 8 & 1 \\ \hline Output Precision & 6 & 1 & 3–7 & 4 & 5 \\ \hline Efficiency (TOPS/W) & 40.3 & 30.49 & 18.37–72.03 & 6.25 & 48.08 \\ \hline Throughput (GOPS) & 8 & 1112.8 & 84.8–269.6 & 8.26 & 240 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison to prior IMC works.  
visualization, H.-J.S.; supervision, S.-H.J.; project administration, S.-H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The EDA Tool was supported by the IC Design Education Center, Republic of Korea.  
The authors declare no conflicts of interest.
## 5 Region-Based Features
### Volume features  
We compute various volume-based features to characterize the region. Diminutive findings are often identified based on the number of voxels in the region. Since folds typically appear as thin cylindrical structures, they may have a small maximum distance based on a boundary-seeded distance transform [40]. Other features are based on the degree of sphericity of the region. Specifically, we compute:  
* Number of voxels in region multiplied by slice thickness
* Maximum value of boundary-seeded distance transform applied to region
* Ratio of actual volume to that of a sphere with the maximal diameter of region
* Number of voxels with shape index \(>T_{si}=0.83\) multiplied by slice thickness (shape cluster size)
# Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors
## 8 Conclusions  
Our recent work on SPAD-based photon counting image sensors is analysed for photon counting performance and deep sub electron equivalent noise characteristics. A noise model is developed to include both CIS RN and the cumulative noise specific to analogue integrator circuits. When combined, the three new methods (VPM, PSW and regressive analysis) of determining RN form a new powerful set of tools for the measurement of most SPC and DSERN image sensor characteristics alongside the existing techniques such as photon transfer curve analysis.  
These single-photon and multi-photon methods of binary image capture have the attractive quality of similar noise and signal characteristics of photographic film. Future development of these binary photon-counting image sensors is an interesting and new avenue of research. The tradeoff between in-pixel cumulative and spatio-temporal oversampling is examined. Analogue SPC pixels have DSERN but exhibit cumulative noise limiting photon number resolution. As a result they are best operated at low photon number in combination with digital oversampling. A very large dynamic range is conceivably possible, combining the multi-photon counting with an oversampled frame store, which would extend the limited dynamic range of the analogue counter. Furthermore, the frame rate penalty of oversampling is addressed by a continuous-time moving average technique.  
The capability of an image sensor to capture the arrival of a single photon, is the fundamental limit to the detection of quantised electromagnetic radiation. Each of the three solid-state SPC image sensor technologies, CMOS SPAD, EMCCD and DSERN CIS have specific advantages that will individually serve a variety of photon counting applications.  
**Acknowledgments:** The following people are gratefully acknowledged for their support in this work: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and all our co-authors in our recent works. Thanks to ST Crolles for silicon fabrication. This work is primarily supported by STMicroelectronics Imaging Division and the research leading to these results has received funding from the European Research Council under the EU's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n.339747.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] modelled and analyzed the data; [PERSON] and [PERSON] conceived and designed the pixel test structure, [PERSON], [PERSON] and [PERSON] conceived and designed the image sensor. [PERSON] wrote the paper; [PERSON], [PERSON], and [PERSON] contributed edits to the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 2 Literature Review  
### Sentiment Analysis  
Most traditional SA research works have utilized supervised machine learning approaches as their clustering module or main classification [17]. These approaches exploited \(n\)-gram features and bag-of-words (BOW) techniques to classify and present user-created texts that bear sentiment [18]. These features are presented to cope with the issues of simple BOW techniques, such as overlooking the order of the word and the syntactic structures [19]. The major drawback of utilizing \(n\)-gram features, especially when \(n\geq 3\), is that the result of the feature space is highly dimensional. To handle this drawback, feature selection techniques have been widely applied in recent studies [20; 21].  
SVM, Naive Bayes (NB) and artificial neural networks (ANN) are among the common methods employed to extract the meanings of users from their text, and have achieved good performance [22; 23; 24]. One of the problems that the supervised methods suffer from is that they are sometimes slow and require a large amount of time during training. To solve these problems, many methods based on unsupervised lexicons have been proposed [18; 25]. These approaches are scalable, fast, and simple. However, they significantly rely on the lexicon, making them less accurate than their supervised counterparts [25; 26]. Field dependency is another issue of lexicon-based approaches, making them less applicable for fields that do not contain specific lexicons.  
Due to the advantages of both lexicon- and supervised-based methods, few researchers have taken these advantages and then combined them in various ways [27; 28]. For instance, for SA, [PERSON] et al. [29] proposed a method that consists of two steps for the entity level of tweets. The first step is a high recall based on the supervised method. The second step is a high precision based on the lexicon method. A hybrid model for concept-based sentiment analysis combines machine learning methods and lexicon-based proposed by [PERSON] et al. [30]. Their method provided a more accurate and justified explanation than purely statistical methods and outperformed lexicon-based methods in detecting the strength of sentiment and polarity.
# Classification of Potato Varieties Drought Stress Tolerance Using Supervised Learning
## 3 Results
### Machine Learning Models for Potato Variety Tolerance to Soil Drought Groups Classification
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Feature** & **Weight** \\ \hline Extra Trees Classifier & Climatic water balance & 0.42 \\ (initial features set of config 1) & Maturity & 0.21 \\  & Stem mass & 0.14 \\  & Assimilation area & 0.12 \\  & Leaves mass & 0.11 \\ Extreme boosting & Climatic water balance & 0.20 \\  & Relative yield decrease & 0.16 \\  & MSTI & 0.14 \\  & DSI & 0.13 \\  & Stem mass & 0.09 \\  & Maturity & 0.08 \\  & Assimilation area & 0.07 \\  & Leaves mass & 0.06 \\  & Yield per plant & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The importance of the model features for two selected resistance models, the initial extra trees classifier and the finally selected extreme boosting classifier. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Config 1** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
**Extra Trees Classifier** & **1** & **0.724** & **0.783** & **0.767** & **0.758** \\ Quadratic Discriminant An. & 1 & 0.635 & 0.687 & 0.674 & 0.651 \\ Random Forest & 1 & 0.692 & 0.734 & 0.721 & 0.721 \\ AdaBoost & 1 & 0.603 & 0.642 & 0.628 & 0.632 \\ Extreme boosting & 1 & 0.609 & 0.639 & 0.628 & 0.631 \\
**Extra Trees Classifier** & **2** & **0.871** & **0.904** & **0.893** & **0.890** \\ Quadratic Discriminant An. & 2 & 0.790 & 0.856 & 0.821 & 0.815 \\ Random Forest & 2 & 0.841 & 0.870 & 0.857 & 0.856 \\ AdaBoost & 2 & 0.645 & 0.819 & 0.786 & 0.783 \\ Extreme boosting & 2 & 0.746 & 0.820 & 0.786 & 0.770 \\
**Extra Trees Classifier** & **3** & **0.859** & **0.875** & **0.865** & **0.866** \\ Quadratic Discriminant An. & 3 & 0.791 & 0.845 & 0.802 & 0.803 \\ Random Forest & 3 & 0.820 & 0.832 & 0.823 & 0.824 \\ AdaBoost & 3 & 0.694 & 0.822 & 0.802 & 0.805 \\ Extreme boosting & 3 & 0.849 & 0.864 & 0.854 & 0.856 \\ Extra Trees Classifier & 4 & 0.845 & 0.847 & 0.830 & 0.820 \\ Quadratic Discriminant An. & 4 & 0.858 & 0.866 & 0.851 & 0.854 \\
**Random Forest** & **4** & **0.869** & **0.851** & **0.851** & **0.849** \\ AdaBoost & 4 & 0.761 & 0.837 & 0.830 & 0.832 \\ Extreme boosting & 4 & 0.828 & 0.812 & 0.809 & 0.799 \\ Extra Trees Classifier & 5 & 0.879 & 0.932 & 0.929 & 0.928 \\ Quadratic Discriminant An. & 5 & 0.900 & 0.916 & 0.893 & 0.890 \\ Random Forest & 5 & 0.905 & 0.940 & 0.929 & 0.926 \\ AdaBoost & 5 & 0.808 & 0.825 & 0.821 & 0.822 \\
**Extreme boosting** & **5** & **0.967** & **0.967** & **0.964** & **0.964** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classifier performance diagnostics for models trained using different parameter configurations (Config column). All metrics were calculated using the measurements test set.
# Evaluation of Five Reanalysis Products over France: Implications for Agro-Climatic Studies
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Reanalysis Datasets  
The following section provides an overview of the five climate RPs used in this study, which are summarized in Table 1.  
ERA5 is the fifth generation of the ECMWF's atmospheric reanalysis dataset [40]. It provides a comprehensive record of the global atmosphere, land surface, and ocean waves  
Figure 1: Location map of the weather stations used in the study.  
since 1950. ERA5-Land is an improved extension of the land component of ERA5 with an enhanced spatial resolution of around 9 km [41].  
SCOPE Climate is a 25-member ensemble of 142-year daily high-resolution reconstructions of precipitation, mean temperature, and evapotranspiration over France. It results from the statistical downscaling of the global extended reanalysis 20 CR [42], by the SCOPE method [43]. FYRE Climate results from the hybridization of the reanalysis FYRE Daily and FYRE Yearly, which results from assimilating historical station observations of temperature and precipitation into SCOPE Climate reconstructions [44].  
RFHR (Reanalysis France High Resolution) is a high resolution (daily and 1 km) advanced climate reanalysis over France. It is built upon the ERA5-Land dataset to provide more accurate climate estimates available from 1979 to 2021. It results from a statistical downscaling followed by a bias correction according to the CDF-t method [45] using the ERA5-Land dataset and observed data available since 2012 on a 1 km grid covering all of France. The observed data are obtained from Meteo-France's radar network PANTHERE [46] for precipitation and Meteo-France's numerical weather forecast model AROME [47] for temperature. They are then reconstructed to achieve a spatial resolution of 0.01\({}^{\circ}\) and an hourly temporal resolution.
## 3 Test Results and Theoretical Analysis
### Analysis of the Permeability Properties during Post-Peak Loading and Unloading
During this process, the permeability of the sample continues to increase, and the increasing rate is increasing. The possible reason is as follows: at the initial stage of unloading confining pressure, the axial stress of the specimen continues to increase under the control of displacement, that is, the deviatoric stress of the specimen increases gradually. However, the unloading confining pressure cannot effectively open the closed cracks during this process, which leads to a slow increase of the permeability of the sample. When the confining pressure is unloaded to a certain value, the axial stress of the specimen begins to decrease with the unloading confining pressure, that is, there is a turning point in the change of deviatoric stress during the post-peak unloading confining pressure. During the process of unloading confining pressure, the closed cracks and pores of the sample gradually open and expand, and generate new cracks, which results in a non-linear increase of the permeability of the sample and an increasing rate of increase. The difficulty of gas flow in the coal body depends on the development degree of the seepage channel such as cracks and pores in the raw  
Figure 9: Deviatoric stress-confining pressure-permeability curves of water content 0.65% coal sample during loading and unloading confining pressure post-peak. (**a**) First loading and unloading (**b**) Second loading and unloading (**c**) Third loading and unloading (**d**) Fourth loading and unloading  
coal sample. The more developed seepage channel is, the more beneficial the gas flow in the coal body will be. Based on the test results in Figure 9, the relationship between permeability and effective stress during each loading and unloading process is shown in Figure 10, and the relationship is established. It can be seen that the permeability and effective stress of the sample are subject to the ExpDec1 function distribution, as shown in Equation (4). \[k=b_{1}\exp(-b_{2}\sigma_{e})+b_{3}, \tag{4}\]  
where, \(b_{1}\), \(b_{2}\), \(b_{3}\) are fitting constants. It can be seen from Figure 10 that the gas permeability decreases exponentially with the increase of the effective stress, and increases exponentially with the decrease of the effective stress.
## 3 Results and Discussion
### Effect on Insulin Recovery over Time  
The Hum R insulin recovery values were normalised over the entire time period based on the values observed from the initial time point. The Hum R recovery results for solutions in contact with PC, R, SS and Ti show negligible loss of Hum R after 31 days at 20 \({}^{\circ}\)C (Figure 2a). However, PU showed a loss of \(\sim\)74% after 31 days at 20 \({}^{\circ}\)C. At 37 \({}^{\circ}\)C, solutions in contact with Ti showed negligible loss of Hum R, with SS showing a loss of \(\sim\)7%, R showing \(\sim\)16%, PC showing \(\sim\)67% and PU showing complete loss of Hum R after 31 days (Figure 2b).  
Figure 3a shows solutions in contact with PC and Ti had a negligible loss of Hum R after 31 days at 20 \({}^{\circ}\)C. However, R showed a loss of \(\sim\)25%, SS showed a loss of \(\sim\)15% and PU showed a loss of \(\sim\)82% after 31 days at 20 \({}^{\circ}\)C. At 37 \({}^{\circ}\)C, solutions in contact with PC and Ti showed negligible loss of  
Figure 2: (a) Hum R insulin recovery over time for each test material at 20 \({}^{\circ}\)C and (b) 37 \({}^{\circ}\)C for method A. PC: polycarbonate; PU: polyurethane; R: resin.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Method A**} \\ \hline  & \multicolumn{2}{c}{**Hum R 500 IU/mL**} & \multicolumn{2}{c}{**Hum R 400 IU/mL**} \\ \hline  & **Concentration (IU/mL)** & **Concentration \%** & **Concentration (IU/mL)** & **Concentration \%** \\ \hline Fresh & 494.25 & 100.00 & 401.16 & 100.00 \\
1 & 491.87 & 99.52 & 400.88 & 99.93 \\
3 & 492.93 & 99.73 & 401.10 & 99.99 \\
7 & 492.64 & 99.67 & 400.15 & 99.75 \\
11 & 491.63 & 99.47 & 400.77 & 99.90 \\
31 & 497.35 & 100.63 & 398.16 & 99.25 \\ \hline  & \multicolumn{2}{c}{**Method B**} \\ \hline Fresh & 500.87 & 100.00 & 400.58 & 100.00 \\
1 & 499.09 & 99.64 & 399.63 & 99.76 \\
3 & 499.62 & 99.75 & 400.61 & 100.01 \\
7 & 502.49 & 100.32 & 399.66 & 99.77 \\
11 & 502.59 & 100.35 & 403.72 & 100.78 \\
31 & 501.81 & 100.19 & 399.18 & 99.65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stability data for Hum R insulin samples stored at 20 \({}^{\circ}\)C for 31 days using methods A and B.  
Hum R, with SS and R showing a loss of \(\sim\)22% and PU showing complete loss of Hum R after 31 days (Figure 3b). The insulin recovery results suggest that at 20 \({}^{\circ}\)C Hum R solution is compatible with PC and Ti for up to 31 days and with R and SS up to 21 days. At 37 \({}^{\circ}\)C Hum R solution is compatible with Ti for up to 31 days, with SS for up to 21 days and PC and R up to 11 days. The results indicate that PU looks like the least compatible of all the materials.
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 3 Watermark Embedding
### Method 5 (M5): Adaptive QIM Approach  
In contrast to the previous method, this approach uses adaptive channel selection for watermark embedding, depending on the transform type, instead of following the zigzag order. This limits the undesired effects observed when applying a zigzag order in the watermark embedding process for the set of transform types described in Section 2. On the other hand, it may not suit so well for DCT transform, since in this case, a zigzag order is preferred.
## 1 Introduction
However, these applications are not suitable for narrow and small regions, due to their low spatial resolution scales, yielding a large number of mixed pixels and resulting in lower accuracy of retrievals [29]. In comparison to these medium resolution images, Landsat 8 surface reflectance (LSSR) images aredelivered on a Polar Stereo (PS) or universal transverse Mercator (UTM) mapped grid with 30 m spatial resolution. Table 1 shows the important features of the LSSR product. Various surveys of remote detection of ocean color were carried out to retrieve water quality parameters, most of which used three basic strategies: (i) implicit, based on the correlation between water quality parameters, using inherent optical properties (IOPs) and semi-analytical models [30; 31; 32]; (ii) using experimental models between these parameters and IOPs [33; 34]; and (iii) experimental models using water quality parameters and satellite data reflection [35; 36; 37; 38]. The third approach was used in this study, which is based on the correlation between field measurements and reflectance values extracted from LSSR products. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Band Designation** & \begin{tabular}{c} **Band** \\ **Name** \\ \end{tabular} & \begin{tabular}{c} **Data** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Units** \\ \end{tabular} & \begin{tabular}{c} **Range** \\ **Range** \\ \end{tabular} & \begin{tabular}{c} **Valid** \\ **Value** \\ \end{tabular} & \
\begin{tabular}{c} **Fill** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **Saturate** \\ **Factor** \\ \end{tabular} \\ \hline ProductID\_sr\_band1 & Band1 & INT16 & Reflectance & \(-\)2000–16,000 & 0-10,000 & \(-\)9999 & 20,000 & 0.0001 \\ ProductID\_sr\_band1 & Band2 & INT16 & Reflectance & \(-\)2000–16,000 & 0-10,000 & \(-\)9999 & 20,000 & 0.0001 \\ ProductID\_sr\_band2 & Band3 & INT16 & Reflectance & \(-\)2000–16,000 & 0-10,000 & \(-\)9999 & 20,000 & 0.0001 \\ ProductID\_sr\_band3 & Band4 & INT16 & Reflectance & \(-\)2000–16,000 & 0-10,000 & \(-\)9999 & 20,000 & 0.0001 \\ ProductID\_sr\_band4 & Band5 & INT16 & Reflectance & \(-\)2000–16,000 & 0-10,000 & \(-\)9999 & 20,000 & 0.0001 \\ ProductID\_sr\_band5 & Band6 & INT16 & Reflectance & \(-\)2000–16,000 & 0-10,000 & \(-\)9999 & 20,000 & 0.0001 \\ ProductID\_sr\_band6 & Band7 & INT16 & Reflectance & \(-\)2000–16,000 & 0-10,000 & \(-\)9999 & 20,000 & 0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Significant features of Landsat 8 surface reflectance (LSSR) data product.
# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 2 EMERGE Onboard System Architecture
### EMERGE Navigation System Implementation
#### 2.1.6 Hardware Description  
The HW used as the On-Board Computer (OBC), shown in Figure 3, is the Raspberry Pi 4 Model B [30] equipped with BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC @ 1.8 GHz, 8 GB SDRAM memory and 32 GB Micro-SD card. The sensors (Inertial and GNSS) are connected via USB interfaces, while the Power Management and UPS system is connected to the standard 40-pin GPIO. The SIRIUS RTK GNSS ROVER multi-constellation receiver [31] (based on u-box ZED-F9P) is connected to the Septentrio Polant-x MF antenna via the SMA interface provided. Both the antenna and the GNSS receiver verify multi-frequency and multi-constellation compatibility. Finally, the components are assembled inside a Sixfab enclosure specifically dedicated to Raspberry projects in outdoor environments with IP65 compliance. An IP65-rated enclosure gives protection against moisture, rain, snow, wind, dust and low-pressure water jets from any direction, as well as condensation and water spray. It is suitable for most outdoor enclosures that will not encounter extreme weather. Figure 4 shows the final Navigation Box, the interfaces and the GNSS antenna.  
Figure 2: MQTT Broker Backend.