esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.1.3 Impact Calculation and Assessment  
Finally, once the data were gathered, as the third phase of the study, the calculation of the environmental impacts was made.  
The overall impact of the hospital activity is calculated using the ReCiPe method [33]. In addition, the CML-IA baseline V3.05/EU25 methodology is used to assess the environmental impact in the 11 impact categories listed below: AD (abiotic depletion), AD  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Unit**} & \multicolumn{3}{c}{**Quantity**} \\ \cline{3-5}  & & & **2019** & **2020** \\ \hline \multirow{3}{*}{Natural Resources} & Water & m\({}^{3}\) & 29,917,000 & 27,129,000 \\ \cline{2-5}  & Electricity & MWh & 2865.29 & 2785.74 \\ \cline{2-5}  & Natural gas & MWh & 2767.35 & 1885.78 \\ \hline Medical Gases & Gas & m\({}^{3}\) & 60,123.54 & 64,193 \\ \hline Waste production & Waste & kg & 179,991.49 & 204,239.49 \\ \hline Waste disposal & Water & m\({}^{3}\) & 29,038 & 26.919 \\ \hline \multirow{3}{*}{Transport} & Employees & km & 6,648,422.4 & 6,925,440 \\ \cline{2-5}  & Patients & km & 8,274,345.4 & 7,659,151 \\ \cline{2-5}  & Materials & km & 37,101.6 & 39,063 \\ \hline \multirow{3}{*}{Products and materials} & Disinfectants & kg & 2146.6 & 9541.25 \\ \cline{2-5}  & Reagents & kg & 1323.2 & 873.1 \\ \cline{2-5}  & Batteries & kg & 106.56 & 101.36 \\ \hline Paper & kg & 15,033 & 15,200 \\ \cline{2-5}  & Toner & kg & 244.8 & 225.6 \\ \cline{2-5}  & Plastic bags & kg & 5483 & 6369 \\ \hline Masks & kg & 456 & 5792 \\ \hline Goggles & kg & 4.8 & 966 \\ \hline Gloves & kg & 11,275.5 & 25,176 \\ \hline Hats and stockings & kg & 524 & 1305 \\ \hline Aprons & kg & 8 & 82 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Environmental aspects inventory data for 2019 and 2020.  
(fossil depletion), GWP (global warming potential for a 100-year time horizon), ODP (ozone layer depletion), HTP (human toxicity potential), FWAE (fresh water aquatic ecotoxicity), MAETP (marine aquatic ecotoxicity), TE (terrestrial ecotoxicity), POCP (photochemical oxidant creation potential), AP (acidification potential), and EP ( eutrophication potential).  
The modelling of hospital activities has been performed with SimaPro v.9.1\({}^{\text{\textregistered}}\). This software allows modelling a product or process throughout its life cycle and assessing the environmental and social impact associated with it.
# A Review of Image Inpainting Methods Based on Deep Learning
## 3 Traditional Inpainting Techniques  
Inpainting, also known as image restoration, is an ancient art form with a wide range of objectives and applications, from restoring damaged paintings and photographs to removing or replacing selected objects. Below is a brief description of five traditional inpainting techniques:  
1. Simulation of Basic Techniques: The purpose of inpainting is to modify an image in an undetectable manner. [PERSON] et al. [10] introduced a new digital image restoration algorithm that attempts to simulate the basic techniques used by professional restorers.
2. Combining Texture Synthesis and Inpainting Techniques: [PERSON] et al. [11] proposed a new algorithm to remove large objects from digital images. It combines the advantages of texture synthesis algorithms and inpainting techniques, implementing a best-first algorithm to simultaneously propagate texture and structural information, achieving efficient image restoration.
3. Inpainting Based on Mathematical Models: developed general mathematical models for local inpainting of non-textured images. The inpainting techniques involved in this method, when restoring edges, adopt a variational model closely related to the classic total variation (TV) denoising model proposed by [PERSON], [PERSON], and [PERSON].
4. Combining PDE and Texture Synthesis: [PERSON] et al. [12] introduced a new algorithm that combines inpainting based on PDE and texture synthesis approaches, treating each distinct region of the image separately.
5. Image Completion Based on a Large Database: [PERSON] et al. [13] introduced a new image completion algorithm powered by a vast database of photographs collected from the Web. The main insight of this method is that while the space of images is virtually infinite, the space of semantically distinguishable scenes is not that large.
# DLI-Based DP-QPSK Reception Scheme for Short-Range Optical Communication
## 3 The Data Recovery Algorithm
```
1:\(B\), \(S\), \(S\), \(S^{\prime}\), \(D\), \(D^{\prime}\) and \(\theta\) are all row vectors. \(N\) is the length of \(S\), \(S=(s_{i})_{N^{\prime}}\) and \(S^{{}^{\prime}}=(s_{i}^{{}^{\prime}})_{N^{+}1^{-}}\), \(f_{1}(X)\) is the inverse mapping 1 (see Table 1) and \(f_{2}(X)\) is inverse mapping 3 (see Table 2). \(\theta=(\delta_{i})_{8}\) is the initial phase storage matrix. 2:The length of \(B\) is \(2N\). 3:The demodulated bitstream storage matrix \(B\). 4:The raw data \(D\). 5:Initialization, parameter setting: \(i=0\), \(j=0\). 6:\(S=f_{1}(B)\);
7:while\(i<8\)do
8:\(i=i+1\);
9:\(s_{1}^{{}^{\prime}}=\delta_{i}\);
10:while\(j<N\)do
11:\(j=j+1\);
12:\(s_{j+1}^{{}^{\prime}}=s_{j}^{{}^{\prime}}+s_{j}\);
13:end
14:\(D^{{}^{\prime}}=f_{2}(S^{{}^{\prime}})\);
15:Search \(D^{{}^{\prime}}\) for the frame synchronization code;
16:if a fixed code exists in \(D^{{}^{\prime}}\)then
17:\(D=D^{{}^{\prime}}\);
18:end
19:end
20:return\(D\)
```  
**Algorithm 1** Recover the raw data from the demodulated bitstream
# Deep Learning Applications for Dyslexia Prediction
## 4 Research Results
### Data Acquisition
All of them were right-handed, did not suffer from hearing impairments, and had normal vision. [PERSON] and [PERSON] [12] used MRI data, which is a diagnostic medical tool utilized to analyze alterations in the brain anatomy. They collected neuroimaging data on 45 individuals (19 dyslexics and 26 non-dyslexics) aged 15-23 years from Kaggle datasets. fMRI is a type of MRI that produces images of brain soft tissue with the highest resolution, which has been utilized to identify and analyze various regions in brain [36]. In another study [37], an MRI dataset of BOLD functionalimages was collected through different reading tasks: a lexical decision task, semantic categorization task, and lexical orthographic matching. The datasets were collected from 55 Spanish children between 9 and 12 years of age, who were recruited from schools and the University Hospital of Cruces in Spain. [PERSON] et al. [38] gathered 3D images from both DTI and fMRI scans of 52 schoolchildren between nine and 12 years of age at various times. In 2021, an fMRI dataset was collected on 32 Portuguese children, divided into 16 typical readers and 16 dyslexics aged 8-12 years [39]. All children were right-handed and matched for IQ, age, and sex. The final dataset category relates to patterns of eye movement when performing cognitive tasks, as recent studies have used eye movements to differentiate between dyslexic and non-dyslexic individuals. Statistical measures have been used to identify features of children's eye movement through cognitive exercise. The authors in [40,41] used the same dataset containing raw eye movement data on 185 subjects, 88 at low risk of dyslexia and 97 with a high risk of dyslexia. These data were collected in 2016 [42] from a wide population of 2165 school children in second grade. Recently, [PERSON] et al. [43] collected and analyzed a dataset on 30 subjects aged 7-13 years, 15 of whom had dyslexia and 15 of whom were normal participants (11 male and 19 female), as they read a Serbian written text with 13 different color configurations. One study used an EOG in the prediction of dyslexia [44]. EOG is a method that relies on the screening of the electrical potential for eye movements, which is helpful for analyzing various types of eye movements, such as saccades, smooth pursuits, vergence, blinks and gaze fixation. This study collected data on 33 children aged between 8 and 11 years of age (20 with dyslexia and 13 healthy; 17 female and 16 male). None of the dyslexic children had hearing or vision defects. Figure 4 illustrates the usage percentage of different datasets in the articles that employed DL methods to predict dyslexia.
## 3 Research Findings
### Factors and Levels for the MOPSO Algorithm
These parameter settings are critical in achieving an effective balance between exploration and exploitation, ensuring that the MOPSO algorithm can converge to optimal or near-optimal solutions.
## 3 Materials and Methodology
### Machine Learning Models
#### 3.1.1 K-Nearest Neighbor  
The k-nearest neighbor is a non-parametric supervised learning algorithm that classifies objects based on the majority class of their k-nearest neighbors in the feature space. The Euclidean is the most commonly used distance metric among all of these in KNN [34]. KNN is a slow, non-parametric learning algorithm that does not assume the underlying distribution of the data.
# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 3 Materials and Methods
### Data Preprocessing
#### 3.1.2 Data Filtering  
In the usage scenarios of smartwatches such as home life, outdoor sports, and daily work, the built-in IMU of the smartwatch may produce noise due to factors such as environmental magnetic fields, external environmental interference, and hardware device accuracy, thereby affecting the effectiveness of the user behavior recognition algorithm. In multi-sensor user behavior recognition systems, using data generated by multiple wearable IMUs can mitigate the impact of noise to some extent. However, in daily scenarios wheresmartwatches serve as data collection devices and only a single IMU generates data, noise can significantly impact the user behavior recognition algorithm. Therefore, data should be filtered in single-sensor systems.  
There are various types of filtering algorithms, including median filtering, low-pass filtering, and Kalman filtering. Previous studies have shown that the frequency of common human actions generally ranges from 1 to 15 Hz, which belongs to the low-frequency behavior category and can be filtered using a low-pass filter on IMU signals. The Butterworth filter is a classic, easy-to-use filter that can be employed as a low-pass filter to remove high-frequency noise. The characteristic of this filter is that within the passband, its frequency response curve exhibits a maximally flat characteristic, while in the stopband, its frequency response curve shows a gradual decline to zero. Moreover, the rate of amplitude attenuation in the stopband is proportional to the order of the filter. The frequency response curve of this filter has very excellent characteristics, so it has been widely used in the field of signal processing.  
The Butterworth filter can be represented by Equation (1):  
\[|H(\omega)|^{2}=\frac{1}{1+\left(\frac{\omega c}{\omega c_{r}}\right)^{2N}}= \frac{1}{1+\epsilon^{2}\left(\frac{\omega c}{\omega p}\right)^{2N}} \tag{1}\]  
where \(N\)--order of the filter; \(\omega_{r}\)--cut-off frequency; \(\omega_{p}\)--edge frequency of the passband.  
In the scenario designed in this paper, the IMU sampling rate of the smartwatch is set to 100 Hz to achieve high-precision behavior perception. To retain motion information below 15 Hz, the cut-off frequency of the filter is set to 20 Hz with an order of 4.  
After segmenting the data from the PAMAP 2 dataset, three accelerometer data frames from a random sensor are filtered, and the signal waveforms before and after filtering are shown in Figure 1.  
By comparing the morphology of the signal frames before and after filtering, it can be verified that the Butterworth filter designed in this paper has a good smoothing effect on the signal, while not losing low-frequency motion information, making it very suitable for the application scenario of user behavior recognition.
## Energy-Efficient Boarder Node Medium Access Control Protocol for Wireless Sensor Networks
**General Terms:** design; experimentation; performance; algorithms  
**Keywords:** sensor node; hybrid MAC protocols; BN-MAC protocol; mobility; intelligent decision-making (IDM) model; automatic active and sleep (AAS) model; least-distance smart neighboring search (LDSNS); wireless sensor network (WSN)  
**Nomenclature**  
AAS - Automatic Active and Sleep  
ACK - Acknowledgement  
ADC-SMAC - Adaptive Duty Cycle SMAC  
A-MAC - Advertisement-based MAC  
BDIF - Broadcast Destinations Inter Frame  
BN-MAC - Boarder Node Medium Access Control  
BNIS - Boarder Node Indication Signal  
BNVSP - Boarder Node Volunteer Selection Process  
BSIF - Broadcast Source Inter Frame  
BT node - Bluetooth- enabled Node  
Ch-S - Channel Sampling  
CD - Clock Drift  
CDMA - Code Division Multiple Access  
CP - Check PeriodCTS Clear-to-Send CSMA Carrier Sense Multiple Access DAPS Dynamic Adjustment of Packet Size EAP Energy Aware-Routing Protocol EFB Election Flag Bit G-MAC Gateway Medium Access Control HRPs Hierarchical Routing Protocols IDM Intelligence Decision Model IE Indoor Environment IOE Indoor and Outdoor Environment LDSNS Least Distance Smart Neighboring Search LEI Level of Energy Information LPR-MAC Low Power Real Time Medium Access Control MAC Medium Access Control MPD Maximized Probability Detection OE Outdoor Environment ns2 Network Simulator-2 ROC Relative Operating Characteristics RTS Request-to-Send RX Receiver SF Synchronized Frame SP Short Preamble Speck-MAC Speck-MAC SPIN Sensor Protocols for Information via Negotiation SPIN-EC SPIN via Negotiation Energy-Conservation SPIN-BC SPIN via Negotiation Broadcast Channel SPIN-PP SPIN via Negotiation Point-to-Point SPIN-RL SPIN via Negotiation Reliable Link TX Transmitter UE Unknown Environment Z-MAC Zebra Medium Access Control
# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 4 Experiments and Results
### Evaluation Metrics and Loss Function  
To effectively evaluate the final results, the Dice Similarity Coefficient (Dice) [33] score and the Hausdorff distance (HD) [34; 35] were employed in this study. The similarity between the two groups was measured by the Dice score. During image segmentation, it is used to evaluate the similarity between the predicted segmentation results and manual annotations, and is expressed as follows:  
\[Dice=\frac{(2\times\text{predictValue})\cap(2\times\text{manualValue})}{predictValue+ manualValue} \tag{11}\]  
where \(predictValue\) represents the result predicted by the model, while \(manualValue\) represents the value manually annotated by neuroradiologists.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Basic Configuration** & **Value** \\ \hline PyTorch version & 1.11.0 \\ Python & 3.8.10 \\ GPU & NVIDIA RTX A5000 (24 G) \\ Cuda & cu113 \\ Learning rate & 3.00 \(\times\) 10\({}^{-4}\) \\ Optimizer & Ranger \\ Batch size & 1 \\ Loss & Jaccard loss \\ Epoch & 150 \\ Input size & 128 \(\times\) 128 \(\times\) 128 \\ Output size & 128 \(\times\) 128 \(\times\) 128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameter configuration.  
The Hausdorff distance (HD) represents the maximum distance between the predicted and actual boundaries. A lower HD value indicates smaller errors. The HD can be mathematically expressed as follows:  
\[D(P,T)=\max\{sup_{t\in T}inf_{p\in p}d(t,p),sup_{p\in p}inf_{t\in T}d(t,p)\} \tag{12}\]  
where \(t\) and \(p\) represent the actual region boundary and the predicted segmentation region boundary, respectively. The term \(d(\cdot)\) denotes the distance between \(t\) and \(p\). The term \(sup\) represents the supremum (least upper bound), while the \(inf\) term represents the infimum (greatest lower bound).  
Considering the class imbalance problem in brain tumor subregions, the Jaccard loss function was introduced. This loss function is derived from the Jaccard coefficient, also known as Intersection over Union (IoU), which measures the similarity of two sets. The Jaccard loss function can directly calculate the matching degree between the tumor region predicted by the model and the real tumor region in the image, thus playing an important role in improving the performance of brain tumor segmentation tasks. In addition, the Jaccard loss function is more robust to the class imbalance situation when the target area is small and the background area is large, alleviating the large difference between the number of foreground and background pixels, to a certain extent, and improving the segmentation effect. The Jaccard loss function is expressed as follows:  
\[\text{Loss}_{\text{accard}}=1-\frac{\text{predictValue}\cap\text{manualValue}}{ predictValue\cup manualValue} \tag{13}\]
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 4 Experimental Platform
### iLab.t Virtual Wall Platform  
The Virtual Wall at IDLab, an imc research group embedded in Ghent University and the University of Antwerp, is a large-scale generic environment that allows researchers to assess and analyse the performance of network software prototypes by providing computer hardware as well as various software tools. Two facilities are currently available, each one consists of around 100 server blades interconnected by a non-blocking VLAN Ethernet switch. For our experiments, we used one Virtual Wall where each node has the following specifications: 2\(\times\) dual core CPU, 8 GB RAM, 4 \(\times\) 80 GB hard disk, four or six Gigabit Ethernet interfaces and a control interface providing remote login.  
In Virtual Wall, virtualisation using OpenVZ ([[http://openvz.org](http://openvz.org)]([http://openvz.org](http://openvz.org)) accessed on 15 September 2021) Linux containers is adopted to enable multiple virtual nodes to run on one machine. In this way, large-scale experiments with 10-20 times the number of physical machines are possible. Moreover, it is possible to use virtual interfaces to create an arbitrary number of virtual network links, which can be individually shaped, multiplexed over physical links or used to interconnect virtual nodes within one physical machine.  
We used an average of 45 server blades to experimentally validate GCMR. In order to reach a network with more than 200 nodes, each blade was virtualised into five virtual machines. In each node, we installed a Linux distribution, Quagga, SMCRoute and GCMR and used Phyton and bash scripts to configure all nodes and packages according to the experimental setup. During the execution, the information exchanged between the nodes and their status were constantly stored in log files and processed afterwards to validate the experiments and obtain the performance results.  
Figure 7: The GCMR state machine.
# Research on Low-Carbon-Emission Scheduling of Workshop under Uncertainty
## 3 Methods
### Analysis of Test Results  
To verify the superiority of the improved algorithm, MOEA/D, NSGA-II, NSGA-III, and the NSGA-III-ST algorithm are selected to perform simulation experiments on ZDT (ZDT1-ZDT4) and DTLZ (DTLZ1, DTLZ2, and DTLZ7) [37] test functions, which are common function sets. The four algorithms use simulated binary crossover and polynomial variation to achieve iterative population evolution, and the variation probability is \(1/k\), where \(k\) denotes the dimensionality of the corresponding decision variables. According to multiple tests, the rotation factor \(\alpha\), maximum search length \(\beta\), and scaling factor \(\gamma\) are all set as \(1\), and the sample size is set as \(SE=5\). Each algorithm runs 30 times independently, and the average value (Average) and standard deviation (St.dev) of the three indexes IGD, HV, and \(\Delta p\) are taken to reflect the comprehensive performance of the algorithm. The optimal values of all indexes are marked in dark gray, and the suboptimal values are marked in light gray. 1. HV  
It can be seen from Table 5 that among the 7 test functions based on the HV, NSGA-III-ST obtains 4 optimal values and 1 suboptimal value, and the st.devs are smaller than other algorithms on some functions that achieve optimal values, such as ZDT1 and ZDT2; MOEA/D obtains 3 optimal values; NSGA-II obtains 2 suboptimal values; and NSGA-III obtains 5 suboptimal values. That means that NSGA-III-ST has a better comprehensive performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Algorithm** & **Average** & **St.dev** \\ \hline \multirow{4}{*}{ZDT1} & MOEA/D & \(7.1146\times 10^{-1}\) & \(6.57\times 10^{-3}\) \\  & NSGA-II & \(7.1913\times 10^{-1}\) & \(2.34\times 10^{-4}\) \\  & NSGA-III & \(7.2000\times 10^{-1}\) & \(9.33\times 10^{-5}\) \\  & NSGA-III-ST & \(\mathbf{7.2028\times 10^{-1}}\) & \(2.94\times 10^{-5}\) \\ \hline \multirow{4}{*}{ZDT2} & MOEA/D & \(4.1891\times 10^{-1}\) & \(3.06\times 10^{-2}\) \\  & NSGA-II & \(4.4379\times 10^{-1}\) & \(1.89\times 10^{-4}\) \\  & NSGA-III & \(\mathbf{4.4460\times 10^{-1}}\) & \(1.40\times 10^{-4}\) \\  & NSGA-III-ST & \(\mathbf{4.4501\times 10^{-1}}\) & \(2.77\times 10^{-5}\) \\ \hline \multirow{4}{*}{ZDT3} & MOEA/D & \(\mathbf{5.9748\times 10^{-1}}\) & \(3.96\times 10^{-2}\) \\  & NSGA-II & \(5.8232\times 10^{-1}\) & \(2.44\times 10^{-2}\) \\ \cline{1-1}  & NSGA-III & \(5.8212\times 10^{-1}\) & \(2.02\times 10^{-4}\) \\ \cline{1-1}  & NSGA-III-ST & \(5.8924\times 10^{-1}\) & \(2.45\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The test results based on the HV.
# Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring
## 4 LoRaWAN Area-Based Speed Control  
As outlined above, excessive vehicle speed is one of the main factors adversely impacting road traffic safety (RTS). For this reason, activities aimed at effectively enforcing and monitoring vehicle speed limit compliance are crucial and strategic in improving RTS [56; 57]. This problem has also been acknowledged by the European Parliament, which introduced several regulations dedicated to newly marketed vehicles, such as Regulation No. 2019/2144, mandating the inclusion of Intelligent Speed Assistants (ISA) in motor vehicles starting from 6 July 2022 for new vehicle types, and from 7 July 2024 for all new vehicles [58]. However, considering the standard delays in implementing such regulations and the average age of vehicles used and imported into Poland, the full dissemination of this solution is expected to take 20 or more years. Moreover, during the initial period, the ISA is intended to be informative and advisory, rather than strictly enforcing compliance.  
Therefore, traditional speed checks will remain a crucial element of monitoring and improving road traffic safety for the foreseeable future. However, technological development enables continuous advancements and the introduction of new dimensions to speed monitoring, including the application of Artificial Intelligence (AI).