esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
#### Contribution  
Cybersecurity is critical while constructing information networks. Because smart grids incorporate several networks, cybersecurity is a top priority in smart grid architecture [15]. Numerous studies, surveys, and review papers have been published that provide anoverview of the prevalent cybersecurity challenges in smart grid applications. Despite the fact that many academics work hard to improve smart grid cybersecurity by employing a variety of strategies and research studies, the breaches against these critical systems are very high and more studies and recent reviews are still necessary [16; 17].  
In this study, we focus on providing a detailed review of cybersecurity and cyber threats against smart energy systems in general and smart grids specifically. Our goals are to provide a comprehensive overview, classify cybersecurity threats, review and analyze historic smart grid attacks, and suggest future study directions to educate new researchers. Figure 1 explains how the method of this survey is conducted, starting from the identification stage of related articles through the screening process to scrutinize and investigate these articles until reaching the final selection stage with the summarized chosen articles.  
The contributions and features given in this study are summarized below:  
* A state-of-the-art detailed background of a smart grid, compared to a traditional grid, as well as its key components and security issues, are elaborated in this work with an up-to-date analysis of related studies.
* Collecting, studying and providing the latest mechanisms for recognizing different types of cyberattacks in SG systems. The most common cyber threats targeting smart grids are discussed thoroughly.
* Worldwide historical cyberattacks against critical smart energy systems and smart grids are thoroughly discussed. This can provide academia and industry with an up-to-date vision on how attackers are targeting smart energy systems, which helps develop strategies to mitigate such threats.
* This study also summarizes the latest emerging future research trend, current challenges, and open research issues. Addressing these issues can be helpful when rehabilitating current smart grids or even to build a new smarter and more secure one.
* An evaluation of the extant cyberattacks based on the CIA triad and network layers are given.  
This manuscript is organized within seven sections. A detailed background of smart grid architecture, its components, and its security issues is presented in Section 2. Section 3 addresses the related works, current research, and previous contributions that aim to mitigate cyber threats against smart grid systems. Then, Section 4 elaborates on the major worldwide cyberattacks in history that breached the security of critical energy systems. The classification and taxonomy of cyberattacks that target smart energy systems are illustrated in Section 5. Finally, detailed future directions as well as the concluding remarks of this research are presented in Sections 6 and 7, respectively.  
Figure 1: Flowchart of article selection criteria process.
# Lossless WDM PON Photonic Integrated Receivers Including SOAs
## 4 Results  
### Transmission Simulation  
The performance of the designed SOA device in the reference DWDM architecture was evaluated through simulation of a \(32\times 25\) Gb/s NRZ transmission with 100 GHz channel spacing and \(2^{11}\)-1 pseudo-random bit sequences (PRBS) data. The input power to the SOA was \(-20\) dBm per channel.  
The receiver scheme with twin parallel SOAs was considered: the incident signal is split into two orthogonal polarization states, TE and TM, and each one of them is amplified by separate SOAs. The transmission performance was evaluated under the worst case scenario, i.e., the incident signal has only one polarized state and all light is coupled to only one SOA while the twin SOA works in the absence of input signal. In this condition, the second SOA only emits ASE noise at the output which is coupled with the amplified signal from the first SOA. The transmission performance for a channel under test (Ch.u.t.) was evaluated through eye diagrams either in the presence or absence of the second SOA to assess the impact of the additional ASE noise. Clear eye opening shows successful transmission of 32 WDM channels (Figure 7) with a limited impact of the ASE due to the second SOA.  
We also considered another potentially critical scenario where power differences of two adjacent input channels is about 10 dB in order to estimate the impact of XGM. Figure 8 shows the eye diagrams for the Ch.u.t. (left) and its 10 dB stronger side channel (right) for the case of input power of \(-16\) dBm and \(-6\) dBm respectively. There is no evidence of XGM and pattern effects are visible.  
Figure 6: Range of NTW_SOA operation versus the GC and tapered reflectivity. Lines separate the boundary with different residual facet reflectivities (RFR). The dot line marks the boundary with \(G_{sp}R=0.017\) which corresponds to the 0.3 dB-ripples limit for 16.5 dB single pass gain.  
Figure 7: Simulated eye diagrams for a channel under test (Ch.u.t.) at SOA1 output (**left**) and after coupling with parallel SOA2 (**right**) for the case of \(32\times 25\) Gb/s transmission.  
To evaluate the model for SOA operation in nonlinear regime (NL), we also simulated transmission of \(32\times 25\) Gb/s with \(-4\) dBm input power per channel. Such a large input power causes the SOA to switch into the NL regime and represents a large cross gain modulation (XGM). The signal quality is affected by patterning due to gain saturation. The nonlinear effect on the data pattern and eye diagram of the Ch.u.t is depicted in Figure 9.
# Comparative Study of AI-Enabled DDoS Detection Technologies in SDN
## 5 Conclusions  
In this paper, we have presented a study implementing machine learning and deep learning-based models in detecting a DDoS attack in an SDN environment. The results obtained in our study show that random forest achieved the highest performance in detecting DDoS attacks because of the centralized nature of the controller. The basic information related to network traffic can be obtained by the controller and can be evaluated by the machine learning-based random forest detection module. We analyzed the accuracy and F1_Score, with a result of over 99% with the random forest classifier model. We analyzed the traffic with the flexibility of the SDN structure, and we used the permutation importance algorithm to extract the 20 features that contain the most valuable information for our CNN classifier model related to the type of attack, in our case DDoS.  
Our results show the required performance, with high accuracy of 99.985%, precision above 99% and an F1_Score of 99.985%. We implemented our model in a Mininet-based SDN environment. In the future, we will also try to implement this approach in a real SDN environment with real network traffic and evaluate the performance of the whole network in terms of other parameters, such as spoofing, latency and throughput. Our present experimental verification involved the identification of 20 data features that had a significant impact on DDoS detection. It was confirmed that the random forest model had excellent DDoS detection performance in SDN.  
We plan to conduct research in the field in the future. This is because more unexpected attacks and various types of DDoS attacks are possible in the field than in the laboratory. Research to overcome and improve this situation is very important. In addition, a new machine learning-based learning model will be developed to prove the superiority of the detection performance.  
[PERSON] designed the entire system for DDoS detection and devised a specific solution based on various previous research. [PERSON] investigated ML and DL learning models, conducted various experimental studies and then performed the initial writing. [PERSON] analyzed the content of the study and designed the function of each component of the entire system. In addition, he read the written content of the paper and performed detailed reviewing and editing. [PERSON] was responsible for funding acquisition and verifying the experimental results. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Wonkwang University in 2021.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Abbreviations  
\begin{tabular}{l l} ANN & artificial neural network \\ ANFIS & adaptive neuro-fuzzy inference system \\ BMS & battery management system \\ CC & constant current \\ CV & constant voltage \\ DG & distributed generation \\ DGA & geometry based approach \\ ESS & energy storage system \\ EV & electric vehicle \\ EKF & extended Kalman filter \\ G2V & grid-to-vehicle \\ GHG & greenhouse gas \\ GMDH & group method of data handling \\ GP & Gaussian process \\ HRES & hybrid renewable energy system \\ ITDNN & input time-delayed neural network \\ KF & Kalman filter \\ LS & least squares \\ NN & neural network \\ NEDC & new European driving cycle \\ MSE & mean squared error \\ PS & power system \\ PF & particle filter \\ QGPFR & quadratic polynomial mean function (GP) \\ RMSE & root mean square error \\ RBC & remaining battery capacity \\ SG & smart grid \\ SOC & state of charge \\ SOH & state of health \\ \end{tabular}
## 3 Proposed Method
### Peso  
The Seq2 Seq model has been widely used in regression tasks. A Seq2 Seq model consists of an encoder and a decoder. The encoder embeds the input sequence as semantic representations, and the decoder maps the representation to the output. \(Enc\) and \(Dec\) denote the encoder and decoder, respectively, while \(X_{inp}\) and \(Y_{oup}\) are the input and output of the Seq2 Seq model, respectively. \(C\) is the semantic representation generated by the encoder.  
\[\begin{split} C=Enc(X_{inp})\\ Y_{oup}=Dec(C)\end{split} \tag{1}\]  
We propose a novel trajectory prediction model, PESO, based on the Seq2 Seq structure with Parallel Encoders and a Ship-Oriented Decoder. PESO is composed of three parts as Figure 2 shows: the Semantic Location Vector, the Parallel Encoders, and the Ship-Oriented Decoder. The Semantic Location Vector is the semantic representation of each ship on the grids, containing spatial information related to the ship's historical track points. The Parallel Encoders are designed to capture more feature information using different encoders to obtain multiple feature representations. The Ship-Oriented Decoder is targeted to utilize the Semantic Location Vector (SLV) of each ship to guide the prediction.  
Figure 2: PESO is composed of a Semantic Location Vector (SLV), Parallel Encoders, and a Ship-Oriented Decoder. The SLV of a particular ship represents the spatial correlation of its trajectories. The Parallel Encoders are designed to capture more features by using two different encoders, which include the Location Encoder and the Sailing Status Encoder. The Ship-Oriented Decoder is designed to utilize the SLV to guide the decoding process.
# Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV)
## Materials and Methods
### Measurement Uncertainty Estimation Approaches and Setups  
The International Vocabulary of Metrology (VIM) [19] defines measurement uncertainty as a \"non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand, based on the information used.\" Measurement uncertainty expresses incomplete knowledge about the measurand, hence a probability distribution over the set of possible values for the measurand can be used to represent the corresponding state of knowledge about it. This probability distribution \"expresses how well one believes one knows the measurand's true value\" and fully characterizes how the degree of this belief varies over that set of possible values. For scalar measurements, the standard deviation (\(\sigma\)) is the attribute of this probability distribution that represents its scatter over the range of possible values. This property of repeated measurements is referred to as precision. When the true value of the measurand is known, the closeness of agreement between a measured quantity value and a true quantity value can be assessed, and it is defined as accuracy.  
As a measure of accuracy, the root mean-squared error (RMSE) was calculated to compare the measured value (\(\hat{y}_{i}\)) with the true value (\(y_{i}\)), as follows:  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{n}} \tag{2}\]  
where n is the number of measurements.  
Besides the RMSE, the mean absolute error (MAE) was also computed, as in Equation (2), because according to [20], this dimensioned statistic is a more natural measure of average error:  
\[\mathrm{MAE}=\frac{1}{n}\sum_{i=1}^{n}|\hat{y}_{i}-\ y_{i}| \tag{3}\]In this framework, three setups were considered, as reported in Table 2, to assess the uncertainty and the accuracy coming from the three sensors of the LasUAV. By the angular static setup (scenario 1, S1) and the angular dynamic setup (scenario 2, S2), the uncertainty and accuracy attributable to the laser scanner and to the laser scanner and INS, respectively, were quantified. By the in-flight setup (scenario 3, S3), the uncertainty attributable to all sensors of the LasUAV was assessed.
## 2 Related Work
### Pixel-Based Classification for HSI  
In general, the performance of supervised learning methods requires much labeled training data to guarantee. However, due to the difficulty of data acquisition and labeling, the publicly available HSI data is very limited. It is a challenge to obtain accurate and robust classification methods by making reasonable use of limited public data.  
Most of the existing HSI classification methods are patch-based methods that use the label of the center pixel to indicate the category of the image patch [14] as shown in Figure 1a. There are two drawbacks to these methods. Firstly, if there is no overlap in image block partitioning, only the pixel label values at the center of the image block are involved in model training, and a large amount of label information in the neighborhood is wasted. Meanwhile, the spatial information of the pixels inside the image block is ignored. These are very unfavorable to the classification results. Secondly, if there is overlap in image block partitioning, serious information leakage may occur, as shown in Figure 2. There is a part of sharing region between adjacent blocks; if these image blocks are divided into training data and test data separately, then the training data information leaks into the test data, resulting in inflated test results.  
The pixel-based classification methods are inspired by semantic segmentation [15], as shown in Figure 1b. The label information of all the pixels in the image block is applied to the training and inference of the model, which effectively improves the utilization of the limited label information. Compared to patch-based classification, pixel-based classification aims at predicting the category of all pixels in the input patches, and we can easily obtain the test result map by the inference process [6]. Additionally, with a suitable data partitioning strategy, pixel-based classification can obtain a more robust and reliable classification performance.
# Applications of Water Jet Cutting Technology in Agricultural Engineering: A Review
## 3 Applications of Water Jet Cutting Technology on Different Agricultural Materials
### Fruits
#### 3.2.1 Post-Harvest Processing  
Post-harvest processing is an important part of fruit processing. Due to the simple structure, some fruits can be easily identified in shape and position, which is conducive to matching the water jet cutting technology with intelligent technologies (i.e., machine vision, image recognition, robot technology, etc.) to improve work efficiency.  
[PERSON] et al. [50] designed a machine for removing strawberry calyx automatically. It was equipped with a color-based machine vision section (Figure 3) and a water jet cutting system with 206.8 MPa jet pressure and a 0.127 mm diameter diamond orifice nozzle. Results indicated that when medium sized (25-38 mm diameter) strawberries were processed by water jets at the traverse speed of 0.3 m/s, calyx-free strawberries could be produced at the highest rate of 2270 kg/h. Meanwhile, the calyx-free fruit, calyx and white shoulder, residual, and return fruit weight percentages were 49.6%, 18.2%, 8.1%, and 24.1%, respectively. The return fruit meant that it would be fed back into the machine for a second processing opportunity. For further advancements, they advised that building a multi-axis calyx removal system accommodates a wider range of shapes and sizes. To minimize the direct contact between product and personnel, [PERSON] et al. [21,24] combined robotics, water jets, and image processing techniques to complete an integrated process of peeling or cutting fruits such as mangos, melons, pineapples, and apples. Under such parameters as the maximum water flow rate of 0.6 L/min, the nozzle diameter of 0.15 mm, and the average pressure of 292-308 MPa, the post-harvest processing of these fruits could be realized.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 9 StarBurst
### Expected Performance  
The scientific performance of the StarBurst instrument was assessed through Monte Carlo simulations using _Geometry and Tracking 4_ (_GEANT4_) ([61]). The peak StarBurst effective area between 50 and 300 keV, averaged over azimuth, is roughly 3000 cm\({}^{2}\), or roughly 500% that of the GBM (Figure 16. This provides an estimated detection efficiency of \(>\)90% for a 64 ms peak flux (photons cm\({}^{-2}\) s\({}^{-1}\)) of 0.9 for SGRBs (\(<\)2 s) and a 1024 ms peak flux of 0.25 for long GRBs (\(>\)2 s). Because StarBurst will have a similar field of view and duty cycle as the GBM, it is estimated that StarBurst will detect 158 SGRBs per year, compared to the 40 and 8.6 SGRBs detected by the GBM and Swift, respectively. StarBurst is estimated to achieve a localization uncertainty within 8\({}^{\circ}\) (1\(\sigma\)) for an SGRB with a 1 photon cm\({}^{-2}\) s\({}^{-1}\) 64 ms peak flux. For SGRBs with a 64 ms peak flux comparable to GRB 170817, or about 3 photons cm\({}^{-2}\) s\({}^{-1}\), StarBurst is expected to achieve a localization uncertainty of \(<\)3\({}^{\circ}\) (1 \(\sigma\)). Employing the technique developed in [PERSON] et al. [62], along with the StarBurst detection efficiency and the projected A+ (LHVKI) B  
Figure 15: The StarBurst instrument (**left**). The integrated StarBurst observatory shown mounted to an ESPA-Grande ring (**right**).  
provides an estimate joint GW-SGRB detection rate of roughly seven joint GW-SGRB detections per year.
# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme
### Preprocessing Data
#### 3.2.3 Geocoding and Distance Calculation  
Following log-scaling, we utilized geocoding to pinpoint the geographic locations related to the housing transactions. Geocoding converts addresses into their corresponding positions on the Earth's surface (i.e., latitude and longitude), enabling the mapping and analysis of geographical data [53; 54; 55]. For the geocoding process, we leveraged the AI-NAVER API from the Naver Cloud Platform to convert the 'ADRES' variable from our base dataset into geographical coordinates.  
With the geocoded locations of the housing transactions, we used the Haversine formula to determine the distance between the houses and the surrounding SOC facilities [56; 57]. The Haversine formula calculates the great-circle distance between two points on the surface of a sphere, for instance the Earth, taking its curvature into account. This makes it more suitable than regular flat-plane distance calculations. The formula is as follows:  
\[a =\sin^{2}\left(\frac{\Delta\phi}{2}\right)+\cos(\phi_{1})\cos( \phi_{2})\sin^{2}\left(\frac{\Delta\lambda}{2}\right)\] \[c =2\cdot atan2(\sqrt{a},\sqrt{1-a})\] \[d =R\cdot c\]  
where \(\phi\) is the latitude, \(\lambda\) is the longitude, and \(R\) is Earth's radius (mean radius = 6371 km). For instance, this formula can be used to find the distance between Seoul (coordinates 37.5665\({}^{\circ}\) N, 126.9780\({}^{\circ}\) E) and Busan (coordinates 35.1796\({}^{\circ}\) N, 129.0756\({}^{\circ}\) E) in South Korea. For our study, we aggregated facilities within a 500 m radius, typically considered a 5-10 min walk, which is within walking distance. As we used geocoded coordinates for our calculations, the Haversine formula was essential for accurate distance calculations.  
Figure 3: Logarithmic scaling of the target variable DLNG_AMOUNT (sales price). The **left** figures represent the data before scaling, while the **right** figures illustrate the data after the application of logarithmic scaling.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 4 System Model  
The system model considered in this paper is shown in Figure 6. The IoT network includes a WLAN access point that covers the area in which IoT nodes are deployed. A beacon-enabled single-hop sensor network is considered, which consists of a network coordinator (sink node) or coordinator communicating with several IoT sensors \(S=1,2,...,N\). WLAN and IoT sensor nodes are static and operate at ISM Band (2.4 GHz). The transmission power of WLAN is usually around 12 dBm to \(-20\) dBm. The WLAN stations are not aware of the IoT sensor nodes [29] as the WLAN carrier sense does not allow the detection of low-powered sensor node signals. This causes collisions and packet losses for the IoT nodes. On the other hand, the transmission power of the IoT sensor nodes is in the order of 0 dBm to \(-3\) dBm [39], and their impact on WLAN transmissions is imperceptible [31]. To ensure efficient IoT communications, WLAN activity should be considered before transmission.  
Figure 6: System model.
# An Innovative and Cost-Advantage CAD Solution for Cubitus Varus Surgical Planning in Children
## Materials and Methods
### Computer Aided Surgical Simulation  
The starting point is the generation of the 3D Digital Model of the bone from the tomographic images (CT). The software used were Invesalius (v. 3.1), MeshLab (v. 2016.12) and Meshmixer (v. 2017). Invesalius is an open-source software for reconstruction of computed tomography and magnetic resonance images. MeshLab and Meshmixer are graphic software used to correct any mesh irregularities [39; 40; 41; 42; 43; 44]. The process from CT to 3D digital model is illustrated in Figure 5.  
The surgery is planned using the 3D generated model. In this work, the parametric software used to perform the simulation is Creo Parametric (v.6), made by PTC, 121 Seaport  
Figure 4: (**a**) Cutting guides printed in medical polylactic acid (PLA) material and (**b**) in polyamide biocompatible material.  
Boulevard, Boston, MA, 02210. Parametric software is the ideal choice if you plan to make quick changes to the project, even during construction, as in the case of simulations.  
Based on the theoretical guidelines provided by the doctors, the engineers simulated the interventions and the results that can be achieved during the operation. If these results do not meet the surgeons' expectations, it is possible to regenerate the model and proceed with the identification of the best solution to be achieved. Ultimately, the preview knowledge of the result of the operation gives rise to a relevant tool that favors the identification of the most suitable surgical strategy in relation to the case analyzed (Figure 6).  
The simulation of corrective osteotomy also has the purpose of identifying the cutting planes to be used for the sizing of the surgical guide. In the case analyzed, it is considered that:  
The plane of the distal osteotomy, approximately parallel to the distal articular surface, is placed approximately 10 mm above the olecranon fossa;  
The proximal osteotomy plane forms an angle of 22\({}^{\circ}\) with the distal osteotomy plane.