esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### SCOPE Model Simulations
We computed Fesc and F\({}_{760\rm leaf}\) in three alternative ways to evaluate their consistency:  
(i) Combination of forward runs of SCOPE and measured F\({}_{760}\) (Fesc\({}_{\rm fw}\)) as shown in Equation (16):  
\[Fesc_{fw}\ \ =\ \ \frac{F_{760}\ \times\ \pi}{F_{760\rm leaf,FW}} \tag{16}\]  
where F\({}_{760\rm leaf,FW}\) and F\({}_{760\rm leaf,fw}\) are fluorescence emitted by all leaves at 760 nm as calculated by the forward SCOPE run (hemispherical and directional, respectively). (ii) An empirical estimate of Fesc (Fesc\({}_{\rm emp}\)) computed according to Equation (17) [33]:  
\[Fesc_{emp}\ \ =\ \ \frac{NIR_{\rm P}}{fAPAR_{RENDVI}}. \tag{17}\]  
NIR\({}_{\rm V}\) was calculated as in Equation (18), where NIR\({}_{\rm T}\) is the reflectance at 858 nm. \[NIR_{V}\ \ =\ NDVI\times NIR_{T} \tag{18}\]  
Then, empirical F\({}_{\rm leaf}\) (F\({}_{760\rm leaf,emp}\)) was calculated as in Equation (19). \[F_{760\rm leaf,emp}\ \ =\ \ \frac{F_{760}}{Fesc_{emp}} \tag{19}\]  
(iii) An estimation of Fesc using data from a SCOPE inversion (Fesc\({}_{\rm inv}\)) (Equation (20)). Fesc\({}_{\rm inv}\) was obtained from inversion of SCOPE against reflectance, F\({}_{760}\), GPP and thermal radiance, as described in [42] and was calculated as in Equation (20). \[Fesc_{inv}\ \ =\ \ \frac{F_{760 INV}/\pi}{F_{760\rm leaf,INV}} \tag{20}\]  
where F\({}_{760\rm INV}\) and F\({}_{760\rm leaf,INV}\) are the top-of canopy sun-induced fluorescence at 760 nm and sun-induced fluorescence emitted by all leaves at 760 nm as obtained from SCOPE inversion. Finally, F\({}_{\rm 760 leaf,inv}\) was calculated as in Equation (21). \[F_{\rm 760 leaf,inv} = \frac{F_{\rm 760}}{F\rm esc_{inv}} \tag{21}\]  
The three alternative Fesc and Fleaf computed (F\({}_{\rm 760 leaf,fw}\), F\({}_{\rm 760 leaf,emp}\), and F\({}_{\rm 760 leaf,inv}\)) were compared against each other (Figure S7). The analysis presented below were conducted with all the different estimates of Fesc to evaluate the effect on the results presented. Hereafter, we report only the results obtained with Fesc\({}_{\rm fw}\) and F\({}_{\rm 760 leaf,fw}\).
# Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
## 3 Materials and Methods
### Data Preparation and Distribution  
For our study, we used a collection of public datasets with varying regions of collection, equipment used, image quality and position of eye structures: ORIGA [19], REFUGE [20], GAMMA [21], Drishti [22] and EyePACS AIROGS [23]. The image quality labels for the datasets were then inferred using the RetinaQualEvaluator model [PERSON] et al. [24], which attributes a label of \"Reject\", \"Usable\" or \"Good\" to each sample.  
Due to the size of AIROGS compared to the remaining datasets (containing 98.4% of the data), in this study, this dataset is only used to complement the other datasets to form the different experiments. After distribution, the samples that remain from the AIROGS dataset are then used to compose an additional dataset to serve as an independent test dataset for the federated setting. As such, a data distributor component was developed to combine one or more base datasets (excluding AIROGS). Afterwards, the distributor uses the AIROGS dataset to complement the base datasets to achieve a target label and quality distribution, noting that data from the complementary dataset cannot be repeated across clients.  
Using this distributor, we formed two data distributions for each client in the following manner:  
1. In the first distribution, which we label \"Dataset Heavily Diverse (Dataset-HD)\", we aim to study the effect of heavily heterogeneous clients on the performance of the federated approach. To achieve such, all clients have different label and quality distributions: (a) Client 1's dataset comprises mostly negative cases (80%) and high-quality images; (b) Client 2's dataset has an even label distribution (50%) and medium-quality images; (c) Client 3's dataset serves as the polar opposite of Client 1, mainly comprising positive cases (80%) and lower-quality images.
2. For the second distribution, which we label \"Dataset Quality Diverse (Dataset-QD)\", we aim to study the effect of different data quality distributions across the clients while following a real-world distribution of glaucomatous cases. For such, we follow  
Figure 1: Architecture diagram for the methodology proposed.  
the glaucoma incidence observed in screening clinics [25], where around 20% of the adults suffer from glaucoma or are suspects. As such, all clients' distributions contain 80% of the cases being negative, while the data quality distribution remains similar to that of the first distribution.  
Client 1's dataset is the same in both distributions and is used as a common comparison point between the two experiments. Table 1 presents the distributions and the datasets forming each client's data.
# Integrated Assessment of Surface Water Quality in Danube River Chilia Branch
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c} \hline \hline
**Quality Class** & **WQI** \\ \cline{2-3}  & **Values** \\ \hline Very good & WQI \textless{} 25 \\ \hline Good & 25 \(\leq\) WQI \textless{} 50 \\ \hline Moderate & 50 \(\leq\) WQI \textless{} 75 \\ \hline Bad & 75 \(\leq\) WQI \textless{} 100 \\ \hline Very bad & 100 \(\leq\) WQI \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Surface water quality classes according to WQI values [3; 5; 18; 37]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sampling Point** & **Index** & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** \\ \hline \multicolumn{1}{c}{Ceatal Chilia} & & 1.44 & 74.04 & 4.04 & 3.78 & 51.78 & 63.718 & 54.225 \\ \hline \multicolumn{1}{c}{Izmail Downstream} & & 1.44 & 79.950 & 1.44 & 1.46 & 61.369 & 61.786 & 70.651 \\ \hline \multicolumn{1}{c}{Periprava} & & 1.44 & 1.47 & 1.47 & 51.079 & 61.383 & 59.638 & 60.133 \\ \hline \multicolumn{1}{c}{Bastroe Upstream} & & 1.43 & 1.43 & 57.134 & 55.730 & 58.715 & 60.704 \\ \hline \multicolumn{1}{c}{Bastroe Downstream} & & 1.41 & 1.43 & 56.242 & 53.740 & 61.128 & 60.133 \\ \hline \multicolumn{1}{c}{Ceatal Chilia} & & 1.40 & 79.77 & 77.33 & 50.06 & 10.030 & 10.030 \\ \hline \multicolumn{1}{c}{Izmail Downstream} & & 39.34 & 37.02 & 39.13 & 89.01 & 39.833 & 10.030 & 89.81 \\ \hline \multicolumn{1}{c}{Periprava} & & 77.25 & 78.48 & 88.18 & 87.831 & 10.030 & 80.030 \\ \hline \multicolumn{1}{c}{Bastroe Upstream} & & 1.40 & 1.43 & 89.12 & 88.91 & 10.030 & 79.99 \\ \hline \multicolumn{1}{c}{Basroe Downstream} & & 39.44 & 89.01 & 83.01 & 80.03 & 80.03 \\ \hline \hline \end{tabular}  
* Classes: I (blue)-excellent/CCME WQI, very good/WQI; II (green)-good/CCME WQI and WQI; III (yellow)-Fair/CCME WQI, moderate/WQI; IV (orange)-marginal/CCME WQI, bad/WQI; V (red)-poor/CCME WQI, very bad/WQI.  
\end{table} 
Table 8: Comparison of Danube River Chilia branch water quality classification * according to CCME WQI and WQI. Figure 10: CCME WQI trends in 2019 on the Chilia branch. Chilia arm (WQI) and 45.16% (CCME WQI). Thus, depending on the values of the two indicators, namely WQI and CCME WQI, over 85% of surface waters have a good quality status, which ensures a living environment propitious to aquatic flora and fauna. We can state that the small differences that appear at moderate ecological status--12.90% (CCME WQI), bad 3.22% (WQI), and very bad 3.22% (WQI) ecological status are insignificant and can be attributed to the different way of integrating concentrations in complex indicators and random exceedances of quality standards. From the point of view of integrated quality indicators, it is difficult to select a specific indicator to make an accurate assessment of surface water quality [5; 17; 37]. In addition, despite the efforts of researchers worldwide, there is still no universal indicator of water quality.
# Local Diversity-Guided Weakly Supervised Fine-Grained Image Classification Method
## 4 Results and Discussion
### Comparisons with State-of-the-Art Methods  
In this section, we first present the ACC-Epochs curve of the proposed LDGNet during the training and testing phases, as shown in Figure 5. Next, we conduct a quantitative analysis by comparing the proposed LDGNet model with several state-of-the-art fine-grained classification methods. The specific details of these representative methods are outlined below:  
1. Part-based R-CNNs [2], KERL [37], and BCNN [6] generally use VGG for feature extraction. Among these, Part-based R-CNN and PoseNorm [38] focus attention on different parts of the object (such as the head, tail, limbs, etc.) through algorithms, and classify based on these local features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **CUB-200-2011 [24]** & **NABirds [35]** & **ISIA Food-200 [36]** &
\begin{tabular}{c} **Ancient** \\ **Pagoda** \\ \end{tabular} \\ \hline Training & 5994 & 23,929 & 118,210 & 6915 \\ Test & 5794 & 24,633 & 59,287 & 6900 \\ Classification & 200 & 555 & 200 & 229 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Four fine-grained dataset parameters.  
* [39], NTS [15], LIO [40], FDL [17], CSC-Net [41], SCAPNet [42], PMG [16], CIN [43], and API-Net [44] utilize ResNet50 and ResNet101 as encoders. Among these, MAMC, SCAPNet, and CIN are attention-based fine-grained classification methods. NTS, LIO, FDL, CSC-Net, and PMG all incorporate feature fusion methods to enhance performance.
* [3], ViT [26], TPSKG [45], FFVT [28], and TransFG [46] all use ViT-B_16 for feature extraction. Among these, FFVT is a Transformer-based method that incorporates feature fusion, aiming to enhance fine-grained classification performance by merging information from different levels. TPSKG primarily employs a multi-stage knowledge fusion approach to improve the results in fine-grained tasks.
* [4] The LDGNet is based on a specially designed ConvNeXt for feature extraction, which offers strong capabilities for local feature extraction and long-range modeling. Furthermore, the LDGNet uses the Multi-Attention Semantic Fusion Module (MASF) to capture key information from feature maps at different levels and perform complementary fusion, resulting in more robust features, which in turn enhances fine-grained classification performance.
# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 2 Problems with Multi-Robot Applied to Agricultural Environments
(**d**) Multiple harvesters harvesting wheat fields in summer. it is also necessary to assign operational tasks to multiple machines, plan operational paths (such as in areas where multiple robots work together), coordinate the formation control of multiple robots, and maintain the information interaction between multiple robots.
# Performance Evaluation of Bluetooth Low Energy: A Systematic Review
## 2 BLE Functioning
### BLE Protocol Stack
#### 2.1.1 Physical Layer  
The BLE technology is designed to operate in the Industrial, Scientific and Medical (ISM) band included in 2.4-2.5 GHz, the same as BR/EDR and Wi-Fi. In particular, the BLE radio band goes from 2.4000 GHz-2.4835 GHz, and it is divided into 40 channels, as can be seen in Figure 3. These channels have center frequencies 2402 + k \(\times\) 2 MHz, where k = 0,..., 39. Three of these channels (37, 38 and 39) are reserved for advertising packets (Section 2.2.1), while the other 37 are used to exchange data packets in connections (Section 2.2.2).  
To avoid interference and fading with other wireless communications in the same radio band, BLE implements an Adaptive Frequency Hopping [53; 54], which is a strategy that defines in a pseudo-random way the communication channel used by the two main characteristics of the link.  
Figure 3: BLE frequency channels. It can be noticed that channels from 0–36 are assigned to data transmission in _connections_ (blue), while the three remaining channels, from 37–39, are used as advertising channels, shown in orange. How channels are positioned in the frequency band is shown in the \(x\)-axis: the first channel, 37, is centered at frequency 2402 MHz, while the last one, the 39 th, is centered at 2480 MHz. Adapted from [48].  
Figure 2: Configuration between Bluetooth versions and device types. On the left is the protocol structure of the BR/EDR, while on the right is the BLE. In the middle is the protocol stack of a device compatible with both Bluetooth versions; this type is called _Smart Ready_ or _Dual Mode_. Adapted from [52].  
It is mandatory that PHY work at 1 Mbps (LE 1M PHY) [53]; in this case, each bit transmitted corresponds to a single symbol (uncoded transmission). Using an error correction coding, more bits can be associated with only one symbol, which implies a bit rate of 500 kbps and 125 kbps, when the coding scheme uses respectively two or eight symbols per bit. An optional radio data rate that PHY supports is 2 Mbps (LE 2M PHY), but in this case, it works with uncoded data only.  
All these parameters lay the foundation for the throughput evaluation, described in Section 3.1.  
BLE PHY also defines the limits for the radio transmit power, which are between the minimum of 0.01 mW (\(-\)20 dBm) and the maximum of 10 mW (\(+\)10 dBm) [53; 55]. In order to optimize the power consumption, along with reducing the interferences and increasing the range of the signal, it is possible to locally change the output power control of the device. The transmit power is the main feature useful to model the maximum range of transmission of BLE (Section 3.5).
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 6 Scheme Analysis
### Performance Analysis
#### 6.2.2 Time Overhead
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **1** & **2** & **3** & **4** & **5** \\ \hline Ratio \(P(\%)\) & 100–20 & 20–15 & 15–10 & 10–5 & 5–0 \\ Number of records & 606 & 138 & 188 & 207 & 340 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Grouping results. Data transmission is a crucial process in electronic medical record sharing, with AES-PRE being the key technology ensuring secure data transmission. While ensuring data security, AES-PRE can improve transmission efficiency. In order to test the performance impact and benefits of using the AES-PRE method in the data sharing phase, we compared it with the method of directly re-encrypting the ciphertext of medical data (DReC) in terms of the time overhead. Figure 4 illustrates the processing time results of our scheme and DReC for different sizes of medical data. As the size of the shared data gradually increases, DReC's time overhead also increases. In contrast, the time overhead of the AES-PRE process varies very little with the size of the data. For the same data size, the incurred time overhead was also smaller than that of DReC, demonstrating that our scheme is more efficient in data sharing.
# AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones
## 6 Conclusions  
In recent years, the defense against low-rate TCP DoS attacks in SDN has garnered significant attention from researchers. However, the application of such defenses to drones has not been a focal point of research, and there are currently no related studies. This paper introduces AccFlow, an incrementally deployable SDN-based protocol, designed to counter both low-rate TCP DoS attacks and general DoS attacks in the context of drones. Consistent with previous studies of defending DDoS attacks where they propose an SDN-assisted DDoS attack defense method [35,36] and the DDoS Attack Detection System using Apache Spark [37], we concentrate on defending low-rate TCP DoS attacks on drones and still demonstrated effectiveness for normal DDoS. We deploy AccFlow on the SDN-centralized controller and use Aggressive Detection and Early Drop to protect the network when attackers try to evade detection by varying their attacking strategies. The main idea of AccFlow is to make the attacking flows accountable. Through substantial amounts of simulations in each setup, we demonstrate that AccFlow, which does not cause any performance degradation to benign flows, can effectively defend against both the low-rate TCP DoS attacks and the general DoS attacks even if attackers are able to vary their strategies.  
Conceptualization, Y.C. and X.Z.; Methodology, Y.C.; Software, X.P.; Validation, L.H.; Investigation, H.L.; Supervision, E.Y.; Funding acquisition, E.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by National Natural Science Foundation of China (62274056), Guangdong Basic and Applied Basic Research Foundation (2022A1515110045 and 2023A1515011241), the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (SKLACSS-202209), Key Research and Development Program of Jiangsu Province (BE2022098), Postdoctoral Science Foundation of Jiangsu Province (2021K605C).  
Not applicable.  
Figure 13: AccFlow provides a strong defense against the general DoS attacks.
# Smart Cities in Russia: Current Situation and Insights for Future Development
## 7 Conclusions  
A smart city is an urban space in which a comfortable and safe environment is established using computer technology. In this paper, the components of a smart city were presented, and the perspectives and challenges for smart cities in Russia were reviewed. The components of a smart city include smart transport, smart communication, smart services, and a smart environment. These components can be subdivided into other components in turn. We proposed the following features for smart cities:  
1. Smart Transport: * Dynamic traffic assignment; * Green energy transport; * Smart public transport.
2. Smart Communication: * Government portals; * City problem trackers; * Emergency notifications.
3. Smart Services: * Smart healthcare; * Smart education; * Smart security.
4. Smart Environment: * Smart buildings; * Smart waste sorting; * Charging stations; * Interactivity.  
The cities were reviewed in accordance with this structure. Smart transport is actively developing in many Russian cities. In most major cities in Russia, one can pay for a trip using a bank card. Furthermore, it is worth noting the trend toward green modes of transport, such as Moscow's electric buses.  
Russian cities are quite developed in the field of smart communication. For example, there is a website operating all over Russia, \"gosuslugi.ru\", which meets a significant number of the goals of smart communication. During the COVID-19 pandemic, it became the main tool for communication between the government and the citizens. There is also an emergency notification system via SMS.  
In other areas, Russian cities are less developed. For example, there is almost no infrastructure for separate garbage collection, nor are there many charging stations for electric vehicles. Besides, it is necessary to solve the issue of the compatibility of heterogeneous systems operating in the city.  
One of the main problems hindering the development of smart cities in Russia is the lack of awareness of citizens about smart city opportunities. For example, the use of green technology will only be successful if civil responsibility and green technologies coexist in harmony.  
To conclude, cities in Russia are actively developing, using information technologies to ensure maximum convenience of citizens. The prospects for smart cities in Russia are mainly related to the further development of the smart environment and the use of green technologies.  
Conceptualization, all authors; methodology, all authors; validation, all authors; investigation, A.Y.; resources, A.Y.; writing--original draft preparation, A.Y. and M.R.B.; writing--review and editing, all authors; visualization,all authors; supervision, M.R.B. and M.M.; project administration, M.R.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable, the study does not report any data.  
We thank Innopolis University for generously funding this endeavor.  
The authors declare no conflict of interest.
## 4 Experiments and Results
### Datasets  
This experimental dataset, OIA-ODIR ([[https://github.com/nkicsl/OIA-ODIR](https://github.com/nkicsl/OIA-ODIR)]([https://github.com/nkicsl/OIA-ODIR](https://github.com/nkicsl/OIA-ODIR))) (accessed on 3 August 2024) [19], contains OCT of both eyes of 5000 patients, totaling 10,000 images. As shown in Figure 6, the dataset comprises normal images (N) and six diseases, such as diabetic retinopathy (D), glaucoma (G), cataract (C), AMD (A), myopia (M), and hypertension (H), as well as other diseases (O). Labels for a single eye were initially created based on the diagnostic results because the dataset only contained labels for binocular images and diagnostic results for a single eye. This led to the retention of 9476 images as the dataset after removing images with shifted images, poor image quality, dusty lenses, abnormal camera exposures, optic discs that were not visible in photos, and controversial images regarding the pathology. The dataset, comprising 7574 images in the training set, 951 images in the validation set, and 941 images in the test set, was randomized into training, validation, and test sets at a ratio of 8:1:1.
## 3 Materials and Methods
### Auto-ARIMA  
ARIMA is the popular term for the [PERSON] prediction method, which was created in 1970. It is essentially an extrapolation strategy that forecasts the underlying variable using existing time series data. It consists of three processes for estimating the ARIMA model, namely, identification, estimation, and diagnosis.  
According to this, the upcoming value of any variable is observed as a linear function of its previous errors and previous values, which are stated mathematically as  
\[Y_{t}=\beta_{0}+\beta_{1}Y_{t-1}+\beta_{2}Y_{t-2}+,...,\beta_{p}Y_{t-p}+ \varepsilon_{t}+\theta_{1}\varepsilon_{t-1}+,...,\theta_{q}\varepsilon_{t-q }+e \tag{3}\]  
where \(Y_{t}\) is the estimated amount of the variable, and in the prior period \(t\), it is denoted as a function of lag variables of itself.  
The amount of arbitrary error in this period is denoted by \(\varepsilon_{t}\). Here, \(\beta_{i}\) and \(\theta_{j}\) are the constants. The moving average and autoregression lags are represented by \(q\) and \(p\), respectively. The left-hand side of Equation (3) from \(\beta_{o}\) to \(\beta_{p}Y_{(t-p)}\) is basically the autoregression (AR) part, while the remaining part of the equation is the moving average (MA) part. This is why the equation as a whole is referred to as the ARMA. The degree of the integral variable, where \(Y_{t}\) the dependent variable attains its stationarity, is represented by \(I(d)\). Since ARIMA models account for the lag of the dependent variable, the random error of estimate, and the order of variables becoming stationary, the models are written as ARIMA \((p,d,q)\). It simply plots the autocorrelation function (ACF) and partial autocorrelation function (PACF) on a correlation matrix to determine the relative locations of \(p\) and \(q\) in a scatter plot [24].