esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction of Periodontitis
### Importance of Oral Microbiome in Periodontal Disease  
The oral microbiome represents one of the most intricate and diverse ecosystems, arising from successive colonization by more than 600 bacterial species. The development of the microbiome begins with the attachment of early colonizers, such as _Actinomyces_ species and oral streptococci, to the acquired pellicle and enamel surface. These initial colonizers not only adhere to the tooth surface but also engage in reciprocal interactions that facilitate the subsequent attachment of bridging species such as _Fusobacterium nucleatum_, followed by late colonizers, including the red complex pathogens--_Porphylomonas gingivalis_, _Tannerella forythtia_, and _Treponema denticola_--which are strongly implicated in the pathogenesis of periodontal disease [8].  
As the biofilm transitions from supragingival to subgingival sites, a shift in the local environment occurs, progressing from an aerobic to an anaerobic milieu. This transition promotes the proliferation of primarily Gram-negative obligate anaerobes, while simultaneously restricting the growth of the early colonizers, which are predominantly Gram-positive facultative aerobes. Microorganisms present at the supragingival level are primarily associated with gingivitis and root caries, whereas subgingival species play a pivotal role in the destruction of periodontal tissues, contributing to the development of periodontitis.  
The subgingival biofilm within periodontal pockets comprises over 300 different species. In this context, _P. gingivalis_ is considered to be a late colonizer, often co-aggregating at the top layer with initial and secondary colonizers. While most oral microbes are seen as commensal, some, including _P. gingivalis_, are recognized as opportunistic or keystone pathogens [9].  
Alterations in the oral microenvironment enhance the presence of pathogenic species, often at the expense of those beneficial microorganisms typically associated with oral health. This dysbiosis fosters the upregulation of virulence factors within these pathogenic organisms. Furthermore, this dynamic is influenced by the interaction between microbial communities and the host's inflammatory response. During periodontal infection, an increase in gingival crevicular fluid initiates early modifications in the bacterial composition of the supragingival biofilm, with these changes becoming more pronounced as the microbiome matures. This process is further perpetuated by autogenic microbial succession and regulated interactions among the microbiota, which contribute to the dynamic equilibrium within the oral ecosystem. Such interactions promote the persistence of dysbiotic communities that evade host immune responses, leading to chronic inflammation and tissue destruction. Moreover, these microbial consortia produce virulence factors and metabolic byproducts that exacerbate local and systemic pathological processes, thereby reinforcing the bidirectional relationship between periodontal disease and systemic conditions [10].
## 3 Results and Discussion
### GHG Mitigation of Solar-Powered Wi-Fi Deployment at ENST  
In this section, we estimate the quantity of CO2 emissions resulting from the energy consumption by the Wi-Fi network. We will use CO2 when actually we refer to CO2-eq, representing the total GHG emission. We will compare the resulting carbon footprint with the quantity of CO2 resulting if we replace the energy supply of this network with a renewable energy source. In the proposed case study, the source of energy is solar electricity. The process of evaluating the total impact is provided below. To estimate the amount of CO2 we followed the following four steps:  
* Step 1: Identify the source of electricity for the Wi-Fi network supply.
* Step 2: Estimate the amount of CO2 resulting from the consumption of electricity produced from a fossil fuel source.
* Step 3: Estimate the quantity of CO2 reduction if the energy source is replaced by a renewable source to supply this network.
* Step 4: Scale up and generalization We start by identifying the energy source (the source of electricity production), since the resulting quantity of CO2 varies from one source to another. In 2015, 98.36% of the electricity produced in Algeria was from natural gas. The carbon intensity of natural gas (NG) based electricity was 418 g of CO2 per kWh [22]. In our case, we will consider the scenario with five access points, which works for a duration of 10 h. The five access points consume 600 Wh, so the resulting quantity of CO2 is 250 g per day and 915.42 kg per year when using NG electricity.  
The next step is to estimate the amount of CO2 if the energy source is replaced by a renewable source to supply this network. Algeria is endowed with one of the highest solar radiations in the world, with more than 2500 h of sunshine on average per year over a very large part of its territory. The energy received daily on a horizontal surface of 1 m\({}^{2}\) is around 5 kWh, which corresponds to 1700 kWh/m\({}^{2}\)/year in the north and 2263 kWh/m\({}^{2}\)/year in the south of the country [23]. If we replace the NG electricity supply for the Wi-Fi network with solar energy, CO2 emissions will be reduced by 217 g per day and 795 kg per year.
## 4 Experiment
### Experiments on CVIP100  
The CVIP100 dataset [46] contains 1208 leaf images from 100 species; each class has at least 12 images. Figure 10 illustrates 24 images for 4 species; one can observe that the leaves for many species have very similar shapes, textures and visual appearance, and image rotation is also a variation factor, which makes CVIP100 a challenging plant dataset. In each category, 70% of the images are considered the training set; the rest are regarded as the testing set. The comparison results are reported in Table 8. Our proposed method achieves 99.65% recognition accuracy, which is the average value of 100%, 99.75%, 99.50%, 99.75% and 99.25% for the five repeated experiments. AlexNet+relu5 [58] and VGG16+relu5_2 [54] only utilize the features from one layer, which leads to a lack of sufficient characteristics. As a result, these methods cannot achieve an extremely promising recognition rate. Similar to the results in the previous tables, the neural network-based methods generally obtain higher results than handcrafted methods, which reveals the advantages of convolutional features. We further combine the convolutional features from multiple CNNs and multiple layers; therefore, our approach can provide higher recognition results as expected. Our  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Methods**} & **Accuracy** & **Year** \\ \hline \multirow{6}{*}{Hand} & SC+DP [9] & 96.94 & 2002 TPAMI \\  & IDSC+DP [10] & 94.90 & 2007 TPAMI \\  & MDM [24] & 94.90 & 2012 TIP \\  & MTCD [13] & 95.92 & 2016 ECAI \\  & MFD [14] & 97.96 & 2019 SPIC \\  & MTD+LBP-HF [27] & **100** & 2021 PR \\ \hline \multirow{6}{*}{DNN} & AlexNet+relu5 [58] & **100** & 2012 NIPS \\  & VGG16+relu5\_2 [54] & **100** & 2015 ICLR \\  & Dual-Path CNN [19] & 98.56 & 2017 ICIP \\  & Deep Plant [18] & 94.90 & 2017 PR \\  & HGO-CNN [20] & 96.43 & 2018 TIP \\  & MaskCOV [35] & **100** & 2021 PR \\  & IMTD+relu5\_2 [38] & **100** & 2023 ESA \\  & Ours & **100**(\(\pm\)0.00) & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification accuracy (%) comparison on ICL compound dataset with identical settings.  
Figure 9: Example images from ICL compound dataset, one image for each species.  
method outperforms the best state-of-the-art method IMTD+relu5_2 [38] by a margin of 0.4%.
# Benefit Optimization of Short Food Supply Chains for Organic Products: A Simulation-Based Approach
## 3 Problem Description
### Organic Production of Food: Descriptive Analysis
\begin{table}
\begin{tabular}{c c c} \hline
**10 Countries with the largest Organic Agricultural** & **10 Countries with the largest Number of Organic** \\
**Areas (Millions of Ha)** & **Producers 2017 (Thousands)** \\ \hline Australia & 35.65 & India & 835 \\ Argentina & 3.39 & Uganda & 210 \\ China & 3.02 & Mexico & 210 \\ Spain & 2.08 & Ethiopia & 203 \\ USA & 2.03 & Philippines & 166 \\ Italy & 1.91 & Tanzania & 148 \\ Uruguay & 1.88 & Peru & 87 \\ India & 1.78 & Turkey & 75 \\ France & 1.74 & Italy & 66 \\ Germany & 1.37 & Paraguay & 58 \\ \hline Countries with an organic share of at least 10\% of the & \multicolumn{2}{c}{Distribution of retail sales value by country 2017 (\%)} \\  & agricultural land 2017 & & USA & 43 \\ \hline Liechtenstein & 37.9 & Germany & 11 \\ Samoa & 37.6 & France & 9 \\ Austria & 24 & France & 9 \\ Estonia & 20.5 & China & 8 \\ Sweden & 18.8 & Italy & 3 \\ Sao Tome and Principle & 18 & Canada & 3 \\ Italy & 15.4 & Switzerland & 3 \\ Latvia & 14.8 & Sweden & 3 \\ Switzerland & 14.4 & Other & 17 \\ Uruguay & 13 & \multicolumn{2}{c}{Distribution of retail sales value by region 2017 (\%)} \\  & & & \\ Czech Republic & 12.2 & North America & 47 \\ Finland & 11.4 & Europe & 41 \\ French Guiana & 10 & Asia & 10 \\ Slovakia & 10 & Oceania & 1 \\  & Latin America & 0.9 \\ \hline Ten countries with the largest markets for organic food & \multicolumn{2}{c}{Ten countries with the highest per capita consumption} \\  & 2017 (million Euros) & \multicolumn{2}{c}{2017 (Euros)} \\ \hline USA & 40,011 & Switzerland & 288 \\ Germany & 10,040 & Denmark & 278 \\ France & 7921 & Sweden & 237 \\ China & 7644 & Luxembourg & 203 \\ Italy & 3137 & Austria & 196 \\ Canada & 3002 & Liechtenstein & 171 \\ Switzerland & 2435 & USA & 122 \\ Sweden & 2366 & Germany & 122 \\ United Kingdom & 2307 & France & 118 \\ Spain & 1903 & Canada & 83 \\ \hline \end{tabular}
\end{table} 
Table 3: General data for world organic farming in 2017. In Poland, 3.4% of the agricultural area is organic (49,797,979 ha). The agriculture is produced by 20,257 farms, 795 processors, 161 importers, and 216 exporters. Annual sales amount to 235 million Euro, which is 6 Euros per person (data for 2017; Table 4). In Poland, 108,655 hectares are undergoing transformation, changing from conventional to organic farming. Of the total 494,979 hectares, 383,246 ha are fully organic and 111,733 ha are being converted.
# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
## 3 Method  
### Estimation of NIIRS Score Using the Image Quality Measure (IMQ) Algorithm  
In this study, we applied the MITRE Image Quality Measure Computer Program(r) (IQM-v7.3) to automatically compute NIIRS ratings for the images. To establish the NIIRS values for the nSight-1 and reference Landsat 8 images we used the Image Quality Measure (IQM) algorithm. IQM is an objective, quantitative image quality indicator that uses the spatial frequency power spectrum of digital imagery and the modulation transfer function of the human visual system [26]. The power spectrum carries critical spatial quality information inherent in digital images such as sharpness, contrast and detail rendition [40]. IQM is highly correlated with the NIIRS to the extent that IQM valued can easily be converted to NIIRS values using a regression equation. NIIRS is a standard visual quality scale utilized in image interpretation that has been used widely for the detection, recognition and identification of man-made objects discernible from digital satellite imagery and aerial photography. In this study, we used the MITRE Image Quality Measure Computer Program(r) (IQM-v7.3) to compute the NIIRS values for the images. The mathematical formulae for IQM is shown in Equation (2) below.  
\[\text{IQM}=\frac{1}{\text{M}^{2}}\sum_{\Theta}\sum_{\Theta}\text{S}(\theta_{1 })\text{W}(\rho)\text{A}^{2}(\text{T}_{\rho})\text{P}(\rho,\theta) \tag{2}\]  
where:  
\(\text{M}^{2}=\text{digital image size in pixels}\)  
\(\text{S}(\theta_{1})=\text{directional image scale parameter}\)  
\(\text{W}\left(\rho\right)=\text{modified Wiener noise filter}\)  
\(\text{A}^{2}(\text{T}_{\rho})=\text{MTF of the human visual system (T= constant)}\)  
\(\text{P}(\rho,\theta)=\text{brightness normalized image power spectrum}\)  
\(\left(\rho,\,\theta\right)=\text{spatial frequency in polar coordinates}\)  
The program is capable of computing NIIRS for general aerial images, space digital sensors, space oblique digital sensor, and digitized film imagery.
# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 5 Experimental Evaluation
### Assessing Scalability  
As machine learning models are increasingly applied to large-scale datasets, the scalability of interpretability methods becomes a critical factor in their practical adoption. Methods like SHAP, while robust and widely used, often exhibit computational inefficiencies as datasets' size increases due to their quadratic complexity. This section evaluates the scalability of C-SHAP compared to SHAP by analyzing their execution times across datasets of varying sizes.  
To assess scalability, we selected the Diabetes dataset and created subsets with sizes of 10%, 25%, 50%, 75%, and 100% of the original dataset. For each subset, we applied SHAP and C-SHAP to compute feature attributions for Random Forest models. The execution times were recorded for generating explanations for the entire dataset subset. The scalability of each method was evaluated based on the growth in execution time as the dataset size increased.  
The results, summarized in Table 3 and visualized in Figure 5, demonstrate the computational advantage of C-SHAP over SHAP. As dataset size increased, SHAP exhibited exponential growth in execution time, while C-SHAP achieved linear scalability, significantly reducing execution times compared to SHAP. This highlights C-SHAP's suitability for large-scale applications without compromising interpretability.
# Autonomous Cyber-Physical Systems Enabling Smart Positive Energy Districts
###### Abstract  
The European Union (EU) is striving to achieve its goal of being climate-neutral by 2050. Aligned with the European Green Deal and in search of means to decarbonize its urban environments, the EU advocates for smart positive energy districts (PEDs). PEDs contribute to the United Nations' (UN) sustainable development goals (SDGs) of \"Sustainable Cities and Communities\", \"Affordable and Clean Energy\", and \"Climate Action\". PEDs are urban neighborhoods that generate renewable energy to a higher extent than they consume, mainly through the utilization of innovative technologies and renewable energy sources. In accordance with the EU 2050 aim, the PED concept is attracting growing research interest. PEDs can transform existing energy systems and aid in achieving carbon neutrality and sustainable urban development. PED is a novel concept and its implementation is challenging. This study aims to present the emerging technologies enabling the proliferation of PEDs by identifying the main challenges and potential solutions to effective adoption and implementation of PEDs. This paper examines the importance and utilization of cyber-physical systems (CPSs), digital twins (DTs), artificial intelligence (AI), the Internet of Things (IoT), edge computing, and blockchain technologies, which are all fundamental to the creation of PEDs for enhancing energy efficiency, sustainable energy, and user engagement. These systems combine physical infrastructure with digital technologies to create intelligent and autonomous systems to optimize energy production, distribution, and consumption, thus positively contributing to achieving smart and sustainable development.  
positive energy districts; PED; internet of things; IoT; smart cities; cyber-physical systems; artificial intelligence; AI; blockchain; energy efficiency; smart districts; review 2025  
2
# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 4 Energy Efficient Techniques in the IoT
### Energy Efficient Techniques at Network/Communication Layer
#### 4.3.3 Energy Efficient Communication Techniques
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocols**} & \multirow{2}{*}{**Characteristics**} & \multirow{2}{*}{**Isuses**} & \multirow{2}{*}{**Contributions**} & \multirow{2}{*}{**Techniques**} & \multirow{2}{*}{**Simulators**} & \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{**Limitations**} \\ \cline{5-9} \cline{7-10} \multirow{-2}{*}{PFSC} & Maintain tradeoff between & Energy efficient solution & Farthest away from the sink and & & & -EE & Link quality has not been \\  & energy efficiency and delay & with minimized delay & Slow data collection for node & OMNET++ & -DL & & -NL & \multicolumn{1}{c}{} \\  & & & & & & & & -NL & \\ \hline \multirow{3}{*}{MC-vB} & Low capacity, more delay & Superior energy efficiency & & & & & -EE & Inferior Energy efficiency in \\  & and sensitivity to & for long distance and & for delay sensitive & & & -NDIB & short distance and delay \\  & via defusion & communication & & & & -DR & tolerant communication. \\ \hline \multirow{3}{*}{EC-CENTRIC} & Ill design network, & & & & & & & & Functionalities such as \\  & processing and resource & & & & & & -NL & Energy harvesting, wakeup \\  & management solution & & & & & & -ND & radios and protocol \\ \cline{1-1} \cline{4-10}  & & & & & & & -QoS & adaptation have not been \\ \hline \multirow{3}{*}{FBP} & \multirow{3}{*}{Communication energy cost} & Less computation time & Maximum weight independent & & & & & & & & \\  & and energy cost & & set approach based on clustering & & & -L-Ratio & & Delay, link quality have not \\ \cline{1-1} \cline{4-10}  & & & & & & & & & & & & \\ \cline{1-1} \cline{4-10}  & & & & & & & & & & & & \\ \cline{1-1} \cline{4-10}  & & & & & & & & & & & & \\ \cline{1-1} \cline{4-10}  & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Summary of energy efficient communication techniques. In Table 13, the characteristics of each paper reviewed are categorized into three including protocol/author, approach, and metrics. The protocol/author column comprises abbreviations of techniques and references. The approach column is categorized into five classes, namely, MAC, BMR, DC, CA and CLT. The last column represents metrics including DL, EE, T-ratio, L-ratio, NL, ND, NDIB, DR and QoS. Considering Table 13, the approaches in columns two, three and five demonstrate that more contributions have been made by MAC-, DC- and CLT-based techniques, followed by BMR, and CA techniques. Further, the metrics column shows that EE is the most considered metric for measuring the performance of energy efficient communication techniques, followed by NL, then DL, ND and DR.
## 4 Discussion
### Data Acquisition  
For the conventional workflow, the extent of the defect and the use of different types of materials make this procedure very difficult and challenging for the medical team and uncomfortable and painful for the patient. For an auricular defect, an impression of the contralateral healthy ear is needed for guiding the handcraft of the wax or the negative pattern of the ear prosthesis, with no direct mirroring being achievable. Potential errors occurring with traditional processes also include distortion of the facial soft tissues caused by the pressure of the impression material [27]; obstruction of the airway when the defect is close to it; aspiration of the impression material; difficulties associated with retentive undercut, sometimes requiring additional surgery for impression material removal; or an impaired impression due to a reduced mouth opening after scar contracture or radiotherapy for intraoral or complex defects [18]. Impression taking is also extremely difficult for the patients, especially when they have large defects or claustrophobia [28]. Moreover, for young and uncooperative individuals, a conventional impression taking procedure is usually not tolerated without sedation [48].  
The digital workflow requires the acquisition of the three-dimensional data of the patient, depending on the type of defect. If it is intraoral, further information on neighboring teeth or surrounding bone structures also needs to be registered.  
In most of the published studies, at least two capture methods were used to precisely register the anatomical structures (Table 1). [PERSON] et al. proposed the use of two capture systems--a face capture system (3 dMDface System; 3 dMD) and an intraoral scanner (TRIOS 2.0; 3 Shape)--for restoring an orbital defect. The digital impression was performed in three steps: a scan of the face by using a facial scanner, a scan of the unaffected orbit with an intraoral scanner and matching of the two scans in Geomagic Studio 2014 software, based on the best-fit algorithm provided by a color-coded deviation map [35].  
Medical scanning (CT, MRI and CBCT) was used in many reported cases [22; 24; 26; 29; 31; 36; 39; 41; 43; 46; 50; 67; 71] for defect data acquisition. The choice of image data is extremely important, with low-resolution images resulting in discrepancies from the actual anatomy, and a high resolution requires greater radiation exposure in the case of CT and CBCT scans [13]. However, for intraoral and complex defects, the use of a CBCT scan is mandatory to collect all the necessary information. Besides that, if the defect was generated by the excision of a malignant lesion, a postoperative CBCT is performed anyway for assessing the risk of tumor recurrence [82].  
Surface scanners are the most-used devices for defect data acquisition (Table 1). However, the laser scanners used are unable to penetrate and register deeper defects and detect concavities, as medical scanners do [58]. As such, most of the time, both types of data acquisition means are necessary [17].