esa-sceva/satcom-chunk-collection huggingface dataset:
# Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data
###### Abstract  
In supervised Activities of Daily Living (ADL) recognition systems, annotating collected sensor readings is an essential, yet exhaustive, task. Readings are collected from activity-monitoring sensors in a 24/7 manner. The size of the produced dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. This results in annotation gaps in the input data to the adopting learning system. The performance of the recognition system is negatively affected by these gaps. In this work, we propose and investigate three different paradigms to handle these gaps. In the first paradigm, the gaps are taken out by dropping all unlabeled readings. A single _\"Unknown\"_ or _\"Do-Nothing\"_ label is given to the unlabeled readings within the operation of the second paradigm. The last paradigm handles these gaps by giving every set of them a unique label identifying the encapsulating certain labels. Also, we propose a semantic preprocessing method of annotation gaps by constructing a hybrid combination of some of these paradigms for further performance improvement. The performance of the proposed three paradigms and their hybrid combination is evaluated using an ADL benchmark dataset containing more than \(2.5\times 10^{6}\) sensor readings that had been collected over more than nine months. The evaluation results emphasize the performance contrast under the operation of each paradigm and support a specific gap handling approach for better performance.  
activity recognition; annotated and unannotated data; smart environments; HMM Article
## 5 Discussion
### Main Analysis: Alignment, Improvement, and Generation
#### 5.1.1 Task 1 (Alignment Evaluation)  
In this first task, each agent's subgrade of the original question was compared to a meta-evaluator's ground truth, and the discrepancy was measured via weighted absolute error. The fine-tuned LLM generally excelled in multi-step, theory-oriented contexts, especially for higher-level conceptual tasks. Conversely, VectorRAG tended to outperform others for single-step, application-oriented questions, leveraging retrieval-based augmentation. Although VectorGraphRAG started with a somewhat higher baseline error, it could partially close the gap when providing especially strong explanation-quality subgrades--for example, specificity/relevance or accuracy/correctness. Indeed, the regression coefficients indicated that a 1-point increase in specificity_relevance could reduce alignment error by up to 0.4 (e.g., \(-0.431\) estimate), revealing the tangible impact of deeper, more precise justifications.
# A Reconfigurable SRAM CRP PUF with High Reliability and Randomness
## 6 Conclusions  
In this paper, we proposed a novel reconfigurable SRAM CRP PUF that achieves high reliability and randomness. We propose an operation timing control scheme, incorporating an adjunct NMOS transistor, to eliminate the challenge and word-line input mismatch. Additionally, we introduce symmetric layout techniques to achieve ideal random response bits. The symmetric arrangement of the two inverter arrays maximizes the inherent random output characteristic inherited from process variation, and the pre-charge input signal must be connected symmetrically to each array to avoid a delay mismatch. The proposed PUF is fabricated through a 180 nm CMOS process, and achieves a compact cell area of 1.2 \(\times\) 10\({}^{4}\) F\({}^{2}\)/bit. Across 40 chips, the inter-HD measured at 0.4949, with an intra-HD of 0.0167 in a single chip across 5000 trials. Notably, the worst-case BER stands at only 4.86%. The proposed PUF exhibits good reliability and randomness characteristics, along with a compact silicon area, when compared to other state-of-the-art designs. Future efforts should explore pre-processing and post-processing techniques to enhance the reliability of the method.  
Conceptualization, J.-P.H.; methodology, J.-P.H.; software, V.K.P.; validation, V.K.P.; formal analysis, V.K.P.; investigation, V.K.P.; resources, V.K.P. and J.-P.H.; data curation, V.K.P. and J.-P.H.; writing--original draft preparation, V.K.P.; writing--review and editing, V.K.P., C.T.N., J.-W.N. and J.-P.H.; visualization, V.K.P., C.T.N. and J.-W.N.; supervision, J.-P.H.; project administration, J.-P.H.; funding acquisition, J.-P.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2020R1A6A1A12047945). This research was also supported by Mid-Career Researcher Program through the National Research Foundation of Korea (NRF) funded by the MST (Ministry of Science and ICT) (NRF-2021R1A2C2005258). The data presented in this study are available on request from the corresponding author.  
The chip fabrication and EDA tool were supported by the IC Design Education Center (IDEC), Republic of Korea.  
The authors declare no conflicts of interest.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 3 Machine Learning Techniques  
For a long time, scientists involved in ground-based gamma-ray astronomy have been investigating Machine Learning techniques. [PERSON] et al. [12] started the initial efforts. Later, two operational ground-based observatories, H.E.S.S. [13; 14; 15] and MAGIC [16], showed the efficacy of tree-based multivariate classifiers for gamma hadron discrimination.  
More recently, [PERSON] et al. [17] evaluated and compared five different Machine Learning methods to decide which of these methods is most suitable for gamma/hadron discrimination: the Random Forest method outperforms the Artificial Neural Network method in terms of signal strength and misclassification rate by almost 20%. Moreover, the Random Forest method has an advantage over perceptron-based methods in terms of computational time.  
Next, we outline the models that we examined, providing a succinct summary of each.
# Effects of Chitosan Coatings on Controlling
## 2 Materials and Methods
### Application of the Chitosan Solution and the Inocula to the Meat Samples  
The selection of chitosan coating with or without vacuum-packaging was based on preliminary results of different beef and mutton treatments including chitosan or alginate coatings, combined with the addition of oregano essential oil or olive oil, and stored aerobically or under vacuum-packaging [17]. A stock of chitosan solution was produced at a final concentration of 1% by dissolving 1 g of chitosan of medium molecular weight obtained from crab shells (48165, Sigma-Aldrich. St. Louis, MO, USA) in 100 mL of 1% glacial acetic acid and stirring overnight at room temperature; finally, the solution was sterilized at 121 \({}^{\circ}\)C for 15 min [18]. Meat pieces were aseptically immersed in the chitosan solution for 1 min and then dredged up and left on sterilized racks for 5 min to form a strong chitosan membrane and to drain off the excess solution. Three batches of samples were conducted for each type of meat: The first was the control, which consisted of meat samples without chitosan membranes, stored aerobically. The second was meat samples with chitosan membranes, stored aerobically. The third was meat pieces with chitosan membranes stored in vacuum-packaging. Each batch was inoculated with the working mixtures of _S. aureus_ or _L. monocytogenes_ in order to achieve a \(\sim\)6.70 log\({}_{10}\) CFU/mL concentration. The inoculation of the meat pieces was performed by adding 100 uL of the cocktail directly to the surface of the meat pieces and allowing it to dry for approximately 30 min on a laminar flow cabinet. The microbial concentrations were verified by surface platting in Baird-Parker agar (Biolab S.E.E., Belgrade, Serbia) for _S. aureus_ enumeration or Listeria Agar according to Ottaviani and Agosti (ALOA--Biolab S.E.E., Belgrade, Serbia) for _L. monocytogenes_ enumeration, after aerobic incubation at 37 \({}^{\circ}\)C for 24-48 h. Then, the samples were packaged in LDPE/PA/LDPE bags either aerobically or under vacuum conditions by using a packaging machine (Lava V.400, Lava GmbH & Co., Bad Saulgau, Germany) and stored at 4 \({}^{\circ}\)C for 21 days in a Peltier cooled incubator (Labtech, Sorisole, Italy).
## 4 Conclusions  
This paper investigates the effects of different cement contents and substrate thicknesses on slope runoff, wetting front migration and infiltration on ecological slopes through artificial rainfall simulations with a large slope model. The test results show that the cumulative runoff volume and slope runoff rate are positively correlated with the cement content and substrate thickness during a continuous 4 h, 60 mm/h artificially simulated rainfall; specifically, the cumulative runoff volume for a 5% cement content slope is 185.51% of that for a 0% cement content slope, and the cumulative runoff volume for a 5 cm substrate thickness slope is 145.09% of that for a 0 cm slope.  
The substrate inhibits the advance of the wetted front and the inhibition of the advance of the wetted front increases progressively with increasing cement content and substrate thickness. The transport rate of the wetting front at different locations on the slope shows that the middle of the slope > the top of the slope. The relationship between the wetting front migration rate and rainfall ephemeris can be expressed using the non-linear function  
Figure 16: Infiltration rate with rainfall time curve.  
Figure 15: Cumulative infiltration with rainfall time curve.  
\(V=a\cdot t^{b}\), while the cement content is linearly related to parameters a and b, and the substrate thickness is non-linearly related to parameters \(a\) and \(b\). Using these findings, it is possible to predict the wetting front transport characteristics of ecological slopes with different substrate characteristics in practical projects.  
The substrate significantly reduces water infiltration into the slope, with the cumulative infiltration volume and infiltration rate decreasing with increasing cement content and substrate thickness. Specifically, the cumulative infiltration and stabilized infiltration rates for 5% cement content slopes are 81.58% and 73.45% of those for 0% slopes, while the cumulative infiltration and stabilized infiltration rates for 5 cm substrate thickness slopes are 73.01% and 70.17% of those for 0 cm slopes.  
In practical engineering applications, increasing the cement content and substrate thickness in the substrate is beneficial to increase the infiltration resistance and stability of the slope, but as excessive cement content and substrate thickness can adversely affect the growth of plants, more research is needed to determine the optimal substrate mix ratio for engineering applications. The results of this study can facilitate the application of substrate design in slope ecological restoration projects.  
Validation, S.H. and X.Z.; Writing--original draft, Y.C.; Writing--review & editing, G.T. and H.X. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (No. 52078195), the Joint Funds of the National Natural Science Foundation of China (No. U22A20232). The study not involving humans.  
All data during the study were obtained through experiments and fitting equations in the paper.  
The authors declare no conflict of interest.
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 4 Experiments
### Model Configuration  
In this section, we present our model configuration. Notably, we begin our experimental analysis by comparing the performance of LSTM and Transformer models in both translation directions, and we conduct the analysis with OpenNMT-py. With OpenNMT, the basic configurations of models are presented in Table 2.  
We design two models with LSTM and Transformer as follows:  
* **LSTM based Model**: A deep LSTM model that uses 2 stacks of 4 LSTM units as encoder and decoder with 512 hidden size.
* **Transformer-based Model**: A simplified Transformer with only two blocks in the encoder and two blocks in the decoder (by default it is 6 for encoder and decoder) with 512 hidden-size, 512 feed-forward-size. We set two heads in multi-head attention instead of eight heads in the default setup. Because of the lack of powerful computing resources and to reduce the carbon footprint, we designed a basic Transformer model.  
The simplification is to avoid overfitting of models on the training set. Notably, custom-configured models can be built. Especially after the preliminary experiment with LSTM and Transformer, the better model is then used for back-translation. To train a translation model in the reversed direction (Bn-En), we swap the source and target of vocabularies, as well as those of training and validation data. Notably, when we get the translation file after passing the test dataset to the model, we need to perform the desubwording of the translated output using the same subword tokenization model as stated in the Tokenization/Subwording stage of the pre-processing section. After that, we evaluate the translated output using the BLEU metric. These are the post-processing stages of our study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Vocabulary (token) Size & 50,000 \\ Word Vector Size & 512 \\ Training Epochs & 50,000 \\ Validation Epochs & 1000 \\ Batch Size & 4096 \\ Optimizer & Adam \\ Adam, Beta1 & 0.9 \\ Adam, Beta2 & 0.998 \\ Decay Method & noam \\ Learning Rate & 2.0 \\ Warmup Steps & 800 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training Configuration.  
As stated earlier, we use GlobalVoices, Tanzil, and Tatoeba datasets for both training and validation. We extract the first 500 pairs of sentences from each dataset to construct the validation set, then leave the rest for training. The vocabularies of both languages are generated from the first 200,000 pairs of sentences in the training set. In each vocabulary, the words that appear only once in the training data are neglected. We train one Transformer and one LSTM model on each translation direction for 50,000 epochs and save a checkpoint every 1000 epochs. Then we compute the BLEU score on the validation set at checkpoints.
# Antimicrobials from Medicinal Plants: An Emergent Strategy to Control Oral Biofilms
## 2 Oral Microbiome  
The oral cavity has an actual diverse microbiome, comprising bacteria, protozoa, fungi, archaea, and viruses, with the most abundant group (96%) being composed by bacteria belonging to the phyla Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes, and Fusobacteria [23; 24]. The remaining microorganisms belong to the phyla Euryarchaeotic, Chlamydia, Chlorofexi, SR1, Sinergistetes, Tenericutes, and TM7. Although present in small percentages, species from the Archaea domain, such as _Methanobrevbibacteri onalis_, _Methanobacterium curcumin/congolense_ and _Methanosarcina mazeii_, are also part of the oral microbiome [25]. About fungi, _Candidia_ species are the most found, being present in almost 50% of the healthy world population [26]. Other frequently fungi found in the oral cavity belong to _Aspergillus_, _Saccharomycescates_, _Cryptococcus_, _Fusarium_ genera [27]. When it comes to viruses, the most frequently found include those that cause sores in the oral cavity, chicken pox, herpes simplex, among others [28]. Moreover, _Trichomonas tenax_ and _Entamoeba gingivalis_ are the main protozoa found as members of the oral microbiome, the majority of which being saprophyytes [29].  
Microorganisms that make up the oral microbiome can reside on two types of surfaces, the hard faces of the teeth and the soft tissues of the oral mucosa [30]. Usually, these microorganisms are present in the oral cavity in the form of a biofilm, playing an extremely important role in oral homeostasis, maintenance, and prevention of oral pathologies [31]. However, under certain conditions, changes in the composition and properties of the biofilm can lead to oral illnesses, such as tooth decay and periodontitis. Increased sugar intake, for instance, promotes the proliferation of acidogenic and aciduric bacteria, such as _S. mutans_ and _Lactobacillus acidophilus_, which, in turn, create an acidic environment that stimulates the development of dental caries [32; 33; 34]. Other bacteria that are usually present in the tooth biofilm include _P. gingivalis_, _Tannerella forythia_ and _Treponema denticola_, closely related to periodontal diseases, which may result from a set of inflammatory conditions that affect the supporting tissues of the teeth [35]. Thus, the biofilm formation may represent the start of the development of different oral diseases.
support a wide array of functions, from real-time imaging to personalized treatment planning, thereby streamlining clinical workflows and reducing reliance on time-intensive manual procedures [2; 3].  
While the technical capabilities of AI and other digital technologies are well-documented [5; 6], their successful implementation in clinical practice is contingent upon treatment outcomes, as well as the acceptance and engagement of dental professionals. To date, research has predominantly focused on patient-centered outcomes and the operational efficacy of these tools [7; 8]. In this context, less attention has been given to the perspectives of clinicians themselves regarding how the use of digital innovations influences their professional experience, clinical decision-making, and satisfaction. Furthermore, technological development in dentistry with AI is occurring faster than the creation of international clinical treatment protocols and guidelines [3], which can also be considered an important gap in the scientific literature.  
Self-reported measures such as perceived clinical effectiveness, confidence in decision-making, time efficiency, as well as satisfaction with patient outcomes and professional life are key indicators of a sustainable and high-performing dental practice [9; 10]. Despite their importance, the literature lacks information on how the advent of digital tools intersects with these self-reported experiences [1].  
To address this gap, the present study aimed to offer a comprehensive analysis of the role of AI-powered digital tools in contemporary dental practice and their implications for future technological integration. We hypothesized that the use of digital technologies is significantly associated with improvements in clinicians' self-reported outcomes [1; 4].  
Accordingly, the study's primary objectives are threefold:  
1. to assess, analyze, and correlate dental clinicians' self-reported outcomes related to the impact of digital and AI technologies on their clinical activities;
2. to compare these outcomes between users and non-users of digital imaging tools;
3. to qualitatively evaluate the experiences and satisfaction levels among practitioners who currently utilize AI-based software applications.  
Based on these objectives, the respective null hypotheses were as follows:  
1. dental clinicians' self-reported outcomes are not significantly correlated with the use of digital and AI technologies in their clinical activities;
2. users and non-users of digital imaging tools do not differ in self-reported outcomes;
3. AI-based software applications do not influence clinical experience and satisfaction.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 2 Study Area, Data, and Methodology
### Methods
#### 2.3.1 Rainfall Events  
In this study, rainfall events were defined as continuous or temporally clustered precipitation periods separated by dry intervals of specific durations [12]. Specifically, dry intervals of 48 h (two days) and 96 h (four days) were used to delineate rainfall events during the dry season (June to September) and the wet season (October to May), respectively [40]. These different separation criteria were adopted to account for the distinct meteorological patterns of Mediterranean climate, as summer rainfall events were generally brief, while autumn and winter events tended to be more continuous [7; 41]. For each landslide location, rainfall sequences spanning 365 days prior to the landslide occurrence were extracted and segmented into discrete rainfall events based on predefined criteria. The rainfall event closest to the landslide occurrence date was classified as a landslide-triggering event, while all others were designated as non-triggering events. However, this definition primarily considers direct rainfall impacts and does not account for additional influencing factors such as snowmelt, groundwater fluctuations, or potential inaccuracies in rainfall data, necessitating further refinement of triggering event identification [42]. [PERSON] et al. [43] excluded landslides with ambiguous or weak correlations to rainfall, particularly those associated with daily rainfall below the 25 th percentile, while [PERSON] et al. [44] empirically identified an approximate nine-day lag between rainfall peaks and subsequent landslide movements. Building on these findings, this study applied two exclusion criteria to refine the selection of landslide-triggering rainfall events: (1) events with cumulative rainfall within the lowest 10% of the dataset were excluded to remove low-rainfall occurrences unlikely to induce slope failure; (2) events where rainfall occurred more than 10 days before landslide initiation were omitted to reduce uncertainties associated with long-lag triggering mechanisms. This screening process enhanced the causal relevance of the selected rainfall events, improving the accuracy and reliability of the predictive model.
## 2 Study Area and Data
### Data Preparation  
The data sets were collected from June to September 2014, including field data, remote sensing data, and meteorological data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Heavy Metals**} & **Background Value** & \multicolumn{2}{c}{**A (113°12′ E, 27°47′ N)**} & **B (113°10′ E, 2740′ N)** & **C (113°2′ E, 27°50′ N)** \\  & **(bi) (mg/kg)**1 & **Soil 2** & **Rice 2** & **Soil 2** & **Rice 2** & **Soil 2** & **Rice 2** \\ \hline
**Cd** & 1.43 & 0.84 & 0.82 & 2.26 & 7.23 & 3.28 & 5.90 \\
**Pb** & 82.78 & 78.33 & 10.60 & 91.05 & 15.18 & 120.75 & 36.73 \\
**Hg** & 0.2 & 0.18 & 0.04 & 0.3 & 0.04 & 0.51 & 0.06 \\
**As** & 19.11 & 10.23 & 5.39 & 18.33 & 6.29 & 18.15 & 7.04 \\ \hline
**Pollution Index 3 (Cd)** & 0.58 & \multicolumn{2}{c}{1.58} & \multicolumn{2}{c}{2.29} \\
**Pollution Level** & \multicolumn{2}{c}{Safe} & \multicolumn{2}{c}{Level I} & \multicolumn{2}{c}{Level II} \\ \hline \hline \end{tabular} Note: 1 The background values of the heavy metals were obtained from the Human Institute of Geophysical and Geochemical Exploration, China. 2 The heavy metals content in the sample plots were analyzed by the analysis and test Center, IEDA CAAS. 3 Pollution Index was calculated by the express Mean (Soil/Background values).  
\end{table}
Table 1: Content of heavy metals in the soil and rice in the study areas.  
Figure 1: Location map for the study areas in the city of Zhuzhou, Hunan Province, China. DOY is the abbreviation for the day of year. (**a**) The location of Zhuzhou city in China; (**b**) the location of the study areas in Zhuzhou city; (**c**) diagram for the rice planting pattern; (**d**) the CCD images selected by the key periods in study area B.