esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Results and Discussion
#### 5.2.3 Energy Consumption  
The nodes consume energy throughout their lifetime, called energy consumption of nodes. The sum of energies consists of their packet sending, packet receiving, sleeping, and idle state power consumption. These values are initialized through energy model. Furthermore, the mathematical model of energy consumption has been explained in the previous section. Equation (47) is used to determine the value of energy consumption.  
\[TE=TD*DPE+2.4*HPS+SNR \tag{47}\]  
\(TE\), \(TD\), \(DPE\), \(HPS\), and \(SNR\) represent the total energy consumption, total data packets, data packet energy, hello packet sending, and \(SNR\). A network is designed against different set of nodes and their energy consumption is analyzed through Section 4.1. Nodes make a path that has a less amount of energy consumption alive and longer in the network; therefore, path1 is chosen. Figure 9 depicts the energy consumption of paths.  
Figure 8: Packet Delivery Ratio of Different Paths in TBRL.  
Figure 9: Energy Consumption of Different Paths in TBRL.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Challenges
#### 8.2.9 Lack of Transparency  
Decisions made by the control and management planes of KDN are based on knowledge generated by knowledge generation models. However, these models are difficult to be interpreted and explained. This lack of transparency can make it difficult to understand why a decision was made, especially in cases where the decisions are unexpected or seem contradictory. Furthermore, it will be challenging to identify the underlying reason for a misclassification caused by a machine learning algorithm due to a performance issue.  
Using explainable machine learning approaches, which make machine learning models easier to analyze and comprehend, is one way to address the transparency problem stated above. Using explainable ML, network administrators will benefit from having a better understanding of how machine learning models operate by detecting the root causes of issues.
## 5 Conclusions  
In this paper, the OGRU-CSO model is developed for intracranial hemorrhage detection or segmentation in the 3D CT scans. The proposed OGRU-CSO model consists of two important phases: feature selection or optimization and classification. After segmenting the diseased portions from the collected 3D CT scans, the feature vectors are extracted using three feature descriptors such as HoG, LBP, and LTP. The extracted features are multi-dimensional, so the CSO optimization algorithm is employed for optimizing the dimension of the extracted feature vectors to improve the computation time and the system complexity. Finally, the optimized discriminative feature vectors are given as the input to the OGRU for the sub-type classification of intracranial hemorrhages. In the experimental section, the proposed OGRU-CSO model achieved 99.36% of classification accuracy on the RSNA 2019 brain CT hemorrhage database, which is effectively related to other classifiers and optimizers. Furthermore, the computational complexity of the proposed OGRU-CSO model is linear by optimizing the feature vectors. As a future extension, a novel hyperparameter optimization algorithm can be included in the OGRU model to further enhance intracranial hemorrhage lesions detection.  
Data curation, J.S.; Formal analysis, J.S.; Investigation, J.S.; Project administration, J.S. and R.A.; Resources, J.S.; Supervision, R.A.; Visualization, J.S.; Writing--original draft, J.S.; Writing--review & editing, R.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available in the [Kaggle] repository, [[[https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection)]([https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection))], (accessed on 1 February 2022).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Database** & **Accuracy (\%)** & **Recall (\%)** & **Specificity (\%)** \\ \hline Synergic deep learning model [10] & & 95.73 & 94.01 & 97.78 \\ ResNeXt-101 [15] & & 97.54 & 60.79 & 99.32 \\ ResNeXt-101 with & RSNA 2019 brain CT & 97.83 & 72.86 & 99 \\ bidirectional LSTM [15] & hemorrhage & 95 & 95.84 & 94.85 \\
2D CNN [16] & & 99.36 & 99.25 & 99.40 \\ OGRU-CSO & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative investigation between the proposed and the existing models.  
**Acknowledgments:** The study was partially supported by the project. This project has received funding from the European Regional Development Fund (project no. 01.2.2-LMT-K-718-03-0091) under a grant agreement with the Research Council of Lithuania (LMTLT).
## 3 Datasets and Methods
### Radiometric Measurements  
On 21 July 2016, a field campaign was conducted in the study area to collect radiometric measurements, associated with the different types of substrate. Hyperspectral measurements were performed in the 400-900 nm spectral range (every 3 nanometres) from two radiometrically-calibrated TriOS-RAMSES sensors. The radiance sensor pointed downward to measure the upward signal of the substrate (\(L_{\text{u}}(\lambda)\), W-m\({}^{-2}\)sr-\({}^{-1}\)nm-\({}^{-1}\)), while the irradiance sensor pointed upward to measure the downward irradiance (\(E_{\text{d}}(\lambda)\), W-m\({}^{-2}\)nm-\({}^{-1}\)). Radiance and irradiance measurements were performed simultaneously, as recommended in Reference [48]. The remote sensing reflectance (\(R_{\text{rs}}(\lambda)\), sr\({}^{-1}\)) was then calculated as the ratio of \(L_{\text{u}}(\lambda)\) to \(E_{\text{d}}(\lambda)\). Note that, for brevity, we do not include the reference to the spectral dependency in the following text. Several features were chosen according to the different aspects of the vegetation of interest found within the study zone. For each feature, a run of 10 radiometric measurements was performed for three different targets in order to compute the average spectrum of \(R_{\text{rs}}\).  
Finally, in order to compare the field data with the Pleiades data, the in situ \(R_{\text{rs}}\) spectra were spectrally transformed using the spectral response function (SRF) of the Pleiades-1A and -1B bands in order to make them compatible with the spectral characteristics of the sensors. The SRF-corrected \(R_{\text{rs}}\) spectra (\(R_{\text{rs}\_SRF}\)) were computed as follows:  
\[R_{\text{rs}\_SRF}(\lambda_{0})=\frac{\int\ R_{\text{rs}}(\lambda)SRF(\lambda) d\lambda}{\int\ SRF(\lambda)d\lambda} \tag{1}\]  
where \(\lambda_{0}\) represents the central wavelength of the different MS bands of Pleiades.
## 4 Discussion
To overcome the limitations of ERA5, recent advances in artificial intelligence, particularly machine learning, have been applied to enhance its accuracy. For example, machine learning techniques have proven to be effective in reducing errors in boundary layer height estimates in ERA5 reanalysis data [11]. Furthermore, this study reveals that ERA5 struggles to accurately reproduce local meteorological phenomena in complex terrain regions. For instance, when northwesterly winds pass over the Yinshan Mountains, the orographic forcing leads to a significant acceleration of wind speeds, which ERA5 fails to capture, resulting in underestimated wind speeds. The presence of complex terrain increases the complexity of the interactions between local circulations and meteorological variables, and ERA5's relatively coarse spatial resolution hinders its ability to faithfully represent these localized phenomena.
# Adsorption of Ammonium, Nitrate, and Phosphate on Hydrochars and Biochars
## 2 Materials and Methods
### Characterisation of BCs and HCs  
The BC, HC, and AC samples were subjected to analysis utilising a CHNS/O analyser, specifically the Elementar Variant LE Cube. The moisture content of the char samples was determined in accordance with the ICS 75.160.10, DD CEN/TS 14774-3:2009 standard [21]. The ash and volatile matter contents were determined using ICS 75.160.10, DD CEN/TS 14775:2009 [22] and ICS 75.160.10, DD CEN/TS 15148:2009 [23], respectively. The pH values of the BCs and HCs were measured by means of a pH metre (PHM 84, Radiometer, Denmark) equipped with glass REF 451 and calomel pHG 201-8 electrodes. The ratio of BC or HC to distilled water was maintained at 1:20 (m/v) during the pH determination process. The study employed a Fourier transform infrared (FTIR) spectrometer, specifically the Cary 630 FTIR spectrometer from Agilent Technologies Inc. (Santa Clara, CA, USA). Spectra were obtained within the 400-4000 cm\({}^{-1}\) range, using a resolution of 4 cm\({}^{-1}\) and 64 scans per sample. The biochar surface area and pore volume were determined by using the Brunauer-Emmett-Teller (BET) method, using a Gemini 2375 V5.01 surface area analyser (Mccromerities Instrument Co., Norcross, GA, USA).  
The zeta potentials of the BCs, HCs, and AC were determined. The experimental procedure involved mixing 0.5 g of each sample with 100 mL of deionised water at a pH of 7. The impact of NH\({}_{4}^{+}\), NO\({}_{3}^{-}\), and PO\({}_{4}^{3-}\) adsorption on the zeta potential was assessed by introducing 0.5 g of each char sample to a solution containing 100 mg\(\cdot\)dm\({}^{-3}\) of NH\({}_{4}^{+}\), NO\({}_{3}^{-}\), and PO\({}_{4}^{3-}\). Prior to this, the pH of the suspensions was adjusted to a range between 4.0 and 9.0 using 0.1 mol\(\cdot\)dm\({}^{-3}\) of HCl or NaOH. The solutions under investigation were subjected to mechanical agitation at a rate of 250 revolutions per minute for a duration of 30 min. The suspensions underwent ultrasonic dispersion for a duration of 1 h at a temperature of 30 \({}^{\circ}\)C, utilising a bath-type sonicator with a frequency of 40 kHz and a voltage of 300 W. Subsequently, the solution underwent filtration using filter paper of the Whatman 42 variety. The zeta potential of each supernatant solution was determined by means of a Malvern Zetasizer Nano instrument (Malvern Instruments, Malvern, UK). The replicability of all analyses was ensured.
# Optimal Dimensional Synthesis of Ackermann Steering Mechanisms for Three-Axle, Six-Wheeled Vehicles
## 2 Position Analysis of Ackermann Steering Mechanism  
### The Ackermann Principle of Vehicle Turning  
When a conventional front-wheel drive, two-axle, four-wheeled vehicle turns, the extensions of the axles of the front and rear wheels must intersect at a single point, the instantaneous center of turning, so that the vehicle can turn smoothly with minimum sideslip and to reduce tire wear, which is known as the Ackermann principle of vehicle steering. The Ackermann steering mechanism is a symmetric four-bar linkage with input and output links of equal length, capable of accurately satisfying the Ackermann principle at some specific angular positions (up to five positions). Figures 1 and 2 illustrate the phases of the Ackermann steering mechanism when the vehicle is traveling straight and turning left, respectively. The steering angles of the front wheels must meet the following relationship for proper functioning.  
\[cot\delta_{out}-cot\delta_{in}=\frac{W}{L} \tag{1}\]  
where \(W\) is the distance between the two front wheels (called wheel track), \(L\) is the distance between the front and rear wheel axles (called wheelbase), \(\delta_{in}\) and \(\delta_{out}\) are the steering angles of the front inner and outer wheels, respectively. The point C in Figure 2 is the vehicle's gravity center, a is the distance between the rear wheel axle and the center of gravity of the vehicle, and \(R_{o}\) is the perpendicular distance from the turning center O to the vehicle's central longitudinal axis. And \(R_{c}\) is the instantaneous turning radius of the gravity center of the vehicle, which can be obtained by the following formula:  
\[R_{c}=\sqrt{a^{2}+L^{2}cot^{2}\delta} \tag{2}\]  
In which  
\[cot\delta=\frac{cot\delta_{in}+cot\delta_{out}}{2} \tag{3}\]  
\[cot\delta_{in}=\frac{R_{o}-\frac{W}{2}}{L},\ cot\delta_{out}=\frac{R_{o}+\frac {W}{2}}{L} \tag{4}\]  
Figure 1: Trapezoidal steering mechanism when the vehicle is traveling straight.
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 3 Request Time Prediction: Beyond Content Popularity
### Characterization of Random Request Time  
Request time prediction relies on the fact, also observed in [4], that a content item is usually requested by a user at most once. We set a content item's generation time to be the time origin. The item can be requested by a user at a random time after its generation, denoted by \(X\), also referred to as the request delay. If it is never requested by the user, we regard the request delay to be \(X=0^{-}\). Otherwise, the user will ask for it at \(X\geq 0\). The accurate request delay \(X\) can hardly be predicted, but its probability density function (\(p.d.f.\)), denoted by \(p(x)\), is predictable. We shall refer to \(p(x)\) as the statistical request delay information (RDI), which characterizes our prediction about the request time [11].  
RDI provides more knowledge than demand probability and popularity, because we can obtain a user's demand probability \(\alpha\) for a content item from its RDI, i.e., \(\alpha=\int_{0}^{\infty}p(x)dx\). Further, if we assign lower indices \(i\) and \(k\) to indicate users and content items respectively, the popularity of item \(k\) can be characterized by \(\frac{N_{i}}{\sum_{k}N_{k}}\), in which \(N_{k}=\sum_{i}\alpha_{ik}\) represents the expected total number of requests for item \(k\).  
RDI is a user-specific prediction. Before the 1990s, computers were expensive and rare, and hence usually shared by a group of users, thereby making individual preference analysis impossible. Today's mobile devices, e.g., smart phones, pads, and laptops, etc. belong to individuals. It is thus possible to learn each individual's preferences by collect ing historical requests from their mobile devices. User-specific prediction emerges as a powerful tool to estimate the empirical distribution of the request delay.
# AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry
## 2 Intelligent Monitoring System for the Intelligent Farm  
In the introduction, we mentioned the need for estrous prediction monitoring on large-scale livestock farms. An intelligent farm system should be operated based on livestock management and should be able to perform the duties of workers instead.  
As shown in Figure 2[9; 10; 11; 12], the solution currently applied in livestock farms for establishing an intelligent breeding environment is to attach sensors, such as temperature and acceleration sensors, to the neck, tail, and ankles of the livestock to classify individual objects through each sensor's unique identifier [13; 14; 15; 16]. In this way, information on the activity of target objects and behavioral characteristics such as estrus is acquired. However, this method requires the attachment of devices for detection that directly and indirectly affect the safety of livestock. As a result, it increases device damage due to livestockactivity and increases management costs and stress on the livestock. This issue leads to the secondary problems of animal welfare, the acquisition of safe livestock resources, and the management of the livestock environment.  
We present the Augmented Recognition Model (ARM) for estrus prediction by classifying individual objects into livestock groups for management and collecting activity data on individual objects to establish a safe breeding environment. In addition, to solve the above-mentioned problems, the monitoring system can be implemented based on AI technology, and we have developed IMS for an estrous prediction that can perform the decision of estrous in advance and livestock monitoring among workers' duties.  
The IMS has the function of distinguishing target objects within a vision-based image of similar objects' activity characteristics and detecting estrus livestock among the distinguished objects.  
In this study, an AI-based recognition algorithm (You Only Look Once: YOLO) was used for the image-based identification of individual objects of similar livestock [17]. However, although the YOLO algorithm can recognize objects with different features of different classes as individual objects, as shown in Figure 3a, in a group of livestock classes having similar features, it cannot recognize similar objects of the same class as unique objects, as shown in Figure 3b.  
Figure 2: Example application of activity data acquisition method applying an invasive method.  
The recognition model using the YOLO algorithm is operated by using image data to recognize the object of observation contained in the image. The monitoring system with the YOLO algorithm consists of a camera (an image acquisition device) in the environment to be observed, and the hardware system can be easily composed. Therefore, ARM developed based on the YOLO algorithm can recognize and classify the target object in the image acquired through the camera without a separate observer and can store activity information about the identified target object.
###### Abstract  
This article presents the development and implementation of a real-time monitoring solution designed for CNC machines, specifically applied to 150 industrial printing machines, leveraging Digital Twin (DT) technology. The system integrates an SQL database with Android and.NET interfaces, ensuring seamless data synchronization across all machines and optimizing production processes. The real-time monitoring enables immediate reflection of operational changes, enhancing predictive maintenance and reducing machine downtime. A notable feature of the system is its 1 s average data synchronization rate per machine, managing 150 resources distributed over a 10,000 mp area. This fast synchronization improves workflow coordination, reducing production time by approximately 10%, and minimizing operator delays caused by material issues, machine malfunctions, or product defects. The integration of advanced analytics further supports real-time decision-making, predictive maintenance, and performance optimization, aligning the solution with the objectives of Industry 4.0 and Industry 5.0 initiatives. This version reflects the specific results of the research, including the 1 s synchronization rate, the 10% reduction in production time, and the scalability of the system for 150 resources.  
CNC machines; digital twin; real-time monitoring; process optimization; human-machine collaboration +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Leadate Treatment Facility  
The landfill site under study is equipped with a dedicated treatment facility for processing raw leachate. The facility has an average daily treatment capacity of 45 m\({}^{3}\) and a maximum capacity of 140 m\({}^{3}\)/day. To ensure the quality of the treated effluent, the facility employs a multi-stage treatment process as outlined below.  
The treatment flow begins with preliminary treatment of the leachate generated from the landfill, followed by biological treatment using a RBC to decompose organic matter. Subsequently, the water undergoes coagulation and sedimentation, sand filtration, activated carbon adsorption, and disinfection, before being discharged. Two RBC units are installed in parallel, allowing one to continue operating in the event of a malfunction or during maintenance of the other (see Figures 2 and 3).  
Although measuring the concentrations of treated water is necessary to ensure environmental safety, there are no regulations in Japan to measure the concentrations of influent leachate. Therefore, specific analyzed leachate concentrations do not exist. The BOD and COD of the leachate at this landfill site are considered low because the landfill contains only non-combustible waste.  
Figure 2: Flow chart of the leachate treatment system at the final disposal site. This schematic diagram outlines the main treatment processes, starting with the pumping of raw leachate, followed by screening, homogenization, biological treatment using a rotating biological contact (RBC) device, flocculation and sedimentation, filtration, adsorption, disinfection and final discharge. This system is designed to integrate mechanical and biological treatment to ensure the removal of organic matter and contaminants prior to discharge.  
In terms of treatment performance in the specifications, the facility achieves a 95% removal rate for both BOD and SSs (suspended solids), based on an influent concentration of 200 mg/L, resulting in effluent concentrations of 10 mg/L for each. In addition, the facility maintains a 90% removal rate for COD, with an effluent concentration of 20 mg/L.  
Even in landfills where non-combustible waste is predominantly disposed of, it is inevitable that pollutants may enter the leachate, either by adhering to the surface of non-combustible materials or being contained within them. The removal of these contaminants is carried out by the biological treatment process--specifically, the RBC. This study aims to extract and evaluate early warning signs of potential RBC failures by analyzing routine equipment inspection data.