esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
Three 3D printers were used to carry out this study: Carbon (L1 3D printer; Carbon Global HQ, London, UK), RapidShape (D100+ 3D printer, RapidShape GmbH, Heimsheim, Germany), and Prodways (LD20 Prodways Tech R&D-Technologies & Headquarters, Montigny-le-Bretonneux, France). Starting from the same prototyped resin model of the superior arch each of these printers was used to make three identical aligners through two different types of materials: Zendura FLX and Scheu CA Pro+. A total of 18 aligners were compared and analyzed. (Figure 1) As for previous studies [18; 19], measurements were taken at each model's central incisor (1.1).  
Each aligner's transmittance was analyzed through a spectrophotometer (T0) (Jasco UV-vis V630 PC, Tokyo, Japan) that has the following specifications: deuterium light source for UV range, and halogen lamp for visible range, double beam, single monochromator. To obtain all the transmitted light, the sensor is positioned 1 cm behind the detection window.  
The spectrophotometer was calibrated before each measurement through the use of the white light spectrum. The tongue area of all the aligners was removed from canine to canine by means of a rotating bur, leaving only the buccal surface to be exposed to  
Figure 1: The 18 aligners before aging.  
the light source. Each aligner was then rinsed in distilled water, dried with an air jet, and positioned inside the spectrophotometer on a 35 mm-high stand with the vestibular surface of the upper central incisor placed vertically, in contact with the detection window. The wavelengths considered included a range from 400 to 700 nm, considering only the wavelengths in the spectrum of visible light were selected, and the transmittance (%T) was automatically recorded by the spectrophotometer.  
Every sample was measured three times consecutively, while slightly changing the inclination of the surface with reference to the light beam. The nine total spectrophotometer reading curves resulting from the analysis of the three aligners of each model were processed via Spectra Manager II software (Jasco, Tokyo, Japan) allowing for the achievement of the numerical values of the average curve and the standard deviation.  
Each one of the 18 aligners was then submerged for 14 days in a glass box (40 cm \(\times\) 20 cm \(\times\) 15 cm), which contained 500 mL of artificial saliva, diluted with 2 L of water and integrated with brown food coloring at a ratio of 1:1 (_v/t_). A heating item was also immersed in the solution, connected to a temperature controller set at 37 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C, to keep the bath at a constant temperature. (Figure 2)  
After 14 days of aging, every aligner was washed with distilled water, dried with an air jet, and analyzed under spectrophotometry (T1) with the same procedures described above. (Figure 3) Finally, a total of 54 transmittance measurements were achieved before and 54 after aging.  
Figure 2: Saliva bath used to age the samples.
## 4 Medal Predict XGBoost Model (MPXG)
### Machine Learning Prediction (United States as an Example)
#### 4.2.3 The Best Machine Learning Model  
Machine learning models each have their unique strengths: random forest (RF) excels at predicting nonlinear relationships in high-dimensional data, leveraging ensemble decision trees to reduce overfitting while enabling feature importance analysis [32]. Back propagation neural network (BPNN) demonstrates superior capability in modeling complex nonlinear patterns and large-scale data structures by automatically learning intrinsic data representations through deep architectures [33]. XGBoost shows outstanding performance in the precise modeling of structured data prediction tasks, achieving computational efficiency and strong generalization through its gradient boosting framework with regularization techniques [34]. SVM performs exceptionally well in classifying high-dimensional small sample data by employing kernel functions to address linearly inseparable problems [35]. This study compares the predictive performance of these four model types to identify the optimal solution for the target dataset.  
We select Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Symmetric Mean Absolute Percentage Error (SMAPE) as evaluation metrics. Among them, SMAPE, which measures relative error in percentage terms and  
Figure 10: Autocorrelation plot after first-order differencing.  
Figure 9: Time series plots after first-order differencing.  
is robust to scale variations, is particularly suitable for comparing prediction accuracy across countries with diverse medal counts. These metrics effectively assess the predictive performance of regression models by quantifying the error between predicted and actual values, as shown in Figure 11.  
Observing Figure 11, the minimal performance discrepancies between training and test sets across all models indicate no overfitting occurred. Notably, XGBoost demonstrates the most balanced and superior performance, achieving the best metrics (MSE = 7.7, RMSE = 2.7, MAE = 2.4, SMAPE = 4.7 in the test set) with all absolute error indicators (MAE) and relative error indicators (SMAPE) significantly below 10. This robust performance confirms XGBoost's reliability for predicting medal counts in the 2028 Olympic Games. FCNN and random forest show moderately good performance, though with slightly higher errors compared to XGBoost. In contrast, SVM exhibits the weakest predictive capability with substantially higher error values. In conclusion, XGBoost is identified as the optimal model for Olympic medal prediction due to its optimal accuracy and stability.
## 5 Conclusions  
In conclusion, a two-step deep learning model applied to chest CT scans was developed and proven to be an effective tool to fully automatically detect a substantial proportion of individuals with \"bovine\" aortic arch (sensitivity 66.7%), an anatomical variant associated with higher risk factors for aortic diseases and stroke, at a low cost of false-positive findings (PPV 87.5%). Its actual performance could be further improved with better fully automatic segmentation procedures thanks to semantic segmentation training on larger datasets.  
Conceptualization, I.C., F.S. ([PERSON]) and M.M.M.-T.; methodology, F.S. ([PERSON]), I.C., F.S. ([PERSON]) and M.M.M.-T.; software, M.I., E.S., C.S. and I.C.; validation, M.I., E.S., C.S. and I.C.; formal analysis, M.I., E.S., C.S., I.C. and F.S. ([PERSON]); investigation, F.S. ([PERSON]), M.I., M.A., E.S., C.B.M., D.C., C.S., I.C., S.P., F.S. ([PERSON]) and M.M.M.-T.; resources, F.S. ([PERSON]), M.A., I.C., F.S. ([PERSON]) and M.M.M.-T.; data curation, M.I., M.A., E.S., C.B.M., D.C. and S.P.; writing--original draft preparation, F.S. ([PERSON]), M.I., E.S., C.S., I.C., F.S. ([PERSON]) and M.M.M.-T.; writing--review and editing, F.S. ([PERSON]), M.I., E.S., C.B.M., D.C., C.S., I.C., F.S. ([PERSON]) and M.M.M.-T.; visualization, M.I., E.S. and C.S.; supervision, F.S. ([PERSON]) and I.C.; project administration, F.S. ([PERSON]) and I.C.; funding acquisition M.M.M.-T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The financial support of the Italian Ministry of Health (PE-2013-02355484 IRCCS Ospedale San Raffaele, [PERSON], Ricerca Corrente IRCCS Policlinico San Donato) is gratefully acknowledged.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the local Ethics Committee of Center 1 (Ethics Committee of IRCCS Ospedale San Raffaele, protocol code \"CardioReftro\", number 122/int/2017; approved on 14 September 2017 and amended on 10 February 2021) and by the local Ethics Committee of Center 2 (Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico Ethics Committees to which CDI refers, protocol ID \"1944\" approved on 8 February 2021).  
**Informed Consent Statement:** Informed consent was waived due to the retrospective nature of the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.  
**Conflicts of Interest:** [PERSON] declares to be CEO of DeepTrace Technologies SRL, a spinoff of Scuola Universitaria Superiore IUSS, Pavia, Italy. [PERSON] and [PERSON] declare to be shareholders of DeepTrace Technologies S.R.L. Prof. [PERSON] has received research grants from and has been a member of the speakers' bureau and advisory group for General Electric, Bayer, and Bracco; he is a member of the scientific advisory board of DeepTrace Technologies S.R.L.
## 1 Introduction
The receiver collects a narrowband signal around a given frequency range and converts it to the complex equivalent baseband signals. For this paper, the authors used three different shapes of 8 \(\times\) 8 antenna arrays (i.e., L-shape, circular-shape, and Y-shape arrays) for the transmitter and receiver. The BER performances for these arrays in MIMO-UWB communication systems are presented. To the best of the authors' knowledge, there is still no published work using asynchronous particle swarm optimization (APSO) and self-adaptive dynamic differential evolution (SADDE) to search for transmission locations to minimize the outage probability of indoor wireless communication channels. In this paper, two different optimization algorithms are proposed and compared to find the optimal transmitter location for the MIMO-UWB communication system [26; 27; 28]. The outage probability of the MIMO-UWB systems is investigated. The MIMO-UWB system and its bit error rate calculation formula are described in Section 2.1. APSO and the SADDE evolution algorithms are described in Section 2.2. The results of applying different shape of antenna arrays and finding an optimal transmitter location by applying APSO and SADDE are shown and discussed in Section 3.
## 3 Results and Discussion
### Application of the Pd-InN Monolayer  
A band gap of a typical surface can be obtained through band structure calculations and it is a workable parameter to evaluate the electrical conductivity of the related material [43]. Therefore, the change in the band gap of the Pd-InN monolayer after gas adsorption indicated the increase or reduction of its resistance in the presence of specific gases. A wide band gap corresponds to a large conductivity and a narrow band gap corresponds to a small conductivity [44; 45], which expounds the basic sensing mechanism of the Pd-InN monolayer as a resistance-type gas sensor. The detailed relationship between band gap and conductivity is calculated as \(\sigma=\text{A}\cdot e^{(-B_{g}/2 kT)}\)[46; 47], where A is the certain constant, \(Bg\) is the bandgap, \(k\) is Boltzmann constant, and \(T\) is the working temperature. Based on these, it is believed that the Pd-InN monolayer has a dramatic electrical response upon NO, given the semimetallic property after gas adsorption, while a small change in conductivity would be obtained in the CO system, due to the slight change in electronic behavior of the Pd-InN monolayer in the existence of the CO molecule. However, considering the strong adsorption performance of the Pd-InN monolayer upon CO, it could be proposed as a CO adsorbent for the removal of such toxic gases in certain environments. We assume that this would be a promising application for the Pd-doped surface as well [48].  
In short, the Pd-InN monolayer could be a promising candidate as a resistance-type sensor for NO detection and as a gas adsorbent for CO removal. We are hopeful that this work can offer the basic physicochemical properties and potential applications of the Pd-InN monolayer, which is beneficial for its further exploration in many fields.
## 4 Numerical Analysis with Results
### Gts Allocation Nodes  
PAN coordinator allocates GTS to the GTS requesting nodes up to its maximum capacity. It is obvious that the increase in \(SO\) results in longer slot duration, that can accommodate more data request. IEEE 802.15.4 standard has only 7 GTS in a superframe duration and can adjust the maximum 7 nodes by allocating GTS. However, superframe duration of both \(ESS_{ADC}\) and \(ESS\) contains 14 CFP slots by reducing each CFP slot to its half size that helps it to accommodate up to 14 GTS requesting nodes.  
Figures 12 and 13 shows the number of successful GTS allocating nodes for random and fixed data requests of each node respectively. It is evident from the results that \(ESS_{ADC}\) and \(ESS\) allow up to 14 GTS requesting nodes to send their data in a superframe duration as compared to the IEEE 802.15.4 standard. If data requests of each GTS requesting node is less than the slot capacity then maximum nodes will be entertained, such as 7 for the standard and 14 each for \(ESS_{ADC}\) and \(ESS\) as shown in Figure 13. However, Figure 12 verifies that \(ESS_{ADC}\) performs better than three different \(SO\) values of \(ESS\) and the standard for adaptive data requests. The results show that \(ESS\) and the standard allows maximum nodes to send their data unless, data requests by each node is within their respective slot limit. This is due to its duty cycle adjustment, that allows \(ESS_{ADC}\) to accommodate 14 nodes in a SD.  
Figure 11: Data transmission by all nodes for varying range of fixed data requesting nodes.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 6 Proposed Solutions
### Route-Aware HO Algorithm  
The route-aware HO algorithm was proposed to make use of path data. The data from flight paths is used to optimize the network, reducing the number of unnecessary HOs and the likelihood of an incorrect HO. The airborne channels' consistency and pre-determined directions are applied to manage flexibility. In addition to the offline-based calculation, an online-based calculation was presented in which HO is triggered as a result of SINR computation. The final option entails setting updates regularly. As a result, it can reduce computation complexity while speeding up the execution of active wireless systems [63].
## 4 System Model Implementation
### Occupancy  
Occupancy is a parameter that determines the importance of user comfort. Occupancy data to know whether a person was present at a particular time or not can be received from occupancy sensors. The effect of occupancy can be seen in the energy consumption because, when a place is not occupied, a proposed controller will work differently as compared to a scenario when that place is occupied. Hence, considering the effect of user occupancy not only results in energy consumption minimization, but it also maintains user comfort. Membership function for occupancy input parameter is shown in Figure 6.  
Figure 5: Membership functions of indoor temperature.  
Figure 6: Input membership functions of occupancy, price, relative humidity, initialized setpoints.
## 3 State of the Art
It is the low delay in the classification performed that allows the use of these methods (protocol recognition, traffic classification) in the control mechanisms performing the classification of the actual network traffic. Often, authors do not provide datasets, so the classification of the protocol or network traffic is performed on a dataset that is not publicly available. Thus, it is not possible to re-evaluate the results, directly relate the results to the obtained results, or compare different approaches for classification purposes. Custom (own) datasets that are no longer available bring significant limitations in the development and comparison of available tools and approaches. Based on the analysis of the current state of the art, the main challenges can be identified as (i) the creation of suitable and publicly available datasets that are oriented towards industrial protocols. These datasets must also contain multiple industry protocols in order to validate the discriminative capabilities of each approach. Furthermore, (ii) focusing on the potential in the area of encrypted traffic (protocols) in OT networks. (iii) Comparing the different processing approaches of the developed dataset and identifying the main research direction.
# 3D Facial Plastic Surgery Simulation: Based on the Structured Light
## 2 Materials and Methods
### 3D Face Data Acquisition: Optical Measurement Strategy  
The acquisition of 3D data is the key to the system. The research shows that a single depth camera can be used to collect depth information from multiple perspectives to realize 3D face reconstruction. In optical 3D measurement technology, structured light coding has the advantages of high accuracy, fast measurement speed, and low cost. Its basic principle is that the projector projects the structured light coding pattern onto the measured object, the camera obtains the deformed image modulated by the surface of the measured object at another angle, then the pose relationship between the transmitter and the camera is obtained by camera calibration, and finally, the target point cloud by triangulation measurement calculation is received [24,25].  
Considering the size, accuracy, and cost of the system, a 3D face scanning system is constructed in our system based on the principle of near-infrared high-frequency binary coded structured light, whose three-dimensional dimension is 101 \(\times\) 26 \(\times\) 13 mm. There are two near-infrared structured light lenses, two infrared sensors (1280 \(\times\) 800), and one VCSEL infrared projector (850 nm) inside the camera. In addition, a color sensor is also needed. The infrared sensor is responsible for the detection of depth, the color sensor (in this paper, a mobile phone camera is used as the color sensor) is responsible for color  
Figure 1: The workflow of proposed facial plastic surgery simulation system.  
extraction and recording details, and the laser projector is responsible for scanning. It can combine high-resolution 3D depth data and image texture information to establish a sub-millimeter-level 3D digital model.  
Through the parameter calibration of the camera module, the spatial parameters of the calibration plane and the projection reference system are obtained, and then the internal and external parameter calibration process of the whole system is realized by projecting the calibration pattern by the projector. Structured light coding adopts high-frequency binary fringe coding, realizes local coding through high-frequency binary fringe displacement, and realizes global phase unwrapping based on Gray code. In addition, high-frequency binary fringe can give full play to the high-frequency binary output characteristics of DLP and has stronger robustness. The 3D facial model has high precision, with submillimeter accuracy, a single scanning time is 3 s, and texture clarity is 6.0 mp. When the system scans, the person is 0.3\(\sim\)0.4 m away from the camera.
## 2 Materials and Methods
### Proposed Model  
According to the routing protocol, the proposed methodology is based on an analysis of performance metrics that directly or indirectly impact the network's energy consumption. Energy metrics give an idea of the network processes activity. We can have a local or global view of node performance along with the network. It also helps in troubleshooting intermittent links or possible attacks and is a fundamental metric for WSNs. Sensors are useful devices in capturing data for local control tasks and delivering it to a point where all this information will be processed. Therefore, routing protocols maintain control of how this information is handled. End-to-end packet delivery delay is an important network performance metric. This is why the packets must be transmitted from one node to another in a reliable, efficient, and with low latency. The comparison of metrics helps to identify optimization goals and improve the network's performance. However, the routing protocol's operation should be analyzed from the point of view of the packet processing  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Protocol**} & **Hierarchical** & **Number of** & **Size of** & **Uniformity** & **Number of** & **Position of** & **Nodes** \\  & **Structure** & **Clusters** & **Clusters** & **of Clusters** & **CH** & **the BS** & **Deployment** \\ \hline LEACH & Two levels & Fixed & Controlled & Uneven & Fixed & Decentralized & Randomly \\ HPAR & Two levels & Fixed & Controlled & Even & Variable & Decentralized & Randomly \\ TEEN & Multilevel & Variable & Controlled & Even & Variable & Decentralized & Randomly \\ PEGASIS & Multilevel & Variable & Controlled & Even & Variable & Decentralized & Randomly \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the studied protocols based on clustering.  
and the speed of response, the recovery against failures, and the routes optimization. This will notably impact local (node level) and global (network level) energy consumption.  
In this work, we analyze twelve performance metrics in WSNs to study their relationship, and the impact they cause on the energy consumption of the network. There are parameters that by themselves cause an increase in the energy consumed by a node. For example, retransmissions represent high energy costs in the node because it involves waiting times, extra packets sent and received by the node, searching and processing in the node's routing tables, and listening to the communications channel. One of this study's significant contributions is to analyze these implications to define an adequate model for detecting possible threats to the network. Table 3 describes the metrics explanation. The metrics depicted in the following table have been selected from the literature, and we will be further showing their importance as parameters related to the energy consumption in WSNs.
## 2 Overview of Cyber Threats in EM
### Communication Media
#### 2.1.3 Amateur Radio  
During the Sichuan earthquake in 2008, trained amateur radio operators set up personal radio stations to establish emergency communications, using authorised frequency bands. The local response coordination depended entirely on this network because all other communications had been lost [15]. Amateur radio is a very common solution during a disaster.  
In the past, the security of amateur radio was not a great concern due to the limited range and high cost of the equipment, but this is gradually changing as software defined radio (SDR) has introduced new security threats into the mobile communications of emergency services. Up to now, there has been practically no security measure in place for preventing a malicious user from launching SDR-based attacks against wireless communications.  
_Open issues in security of Amateur Radio used in EM_: As specified in [27], a SDR attacker requires only a PC, a front end to capture traffic, a high-speed Analogue-to-Digital Converter (ADC) to digitise the radio frequency signal, and a Digital Signal Processor (DSP) for analysing it before converting it into audio. The user then analyses the spectrum looking for repeating patterns to isolate the preamble and payload or the message header. Frequency hopping and advanced modulation techniques can be overcome by SDR. Most significantly, SDR is not restricted by frequency. This means that it can provide the means to compromise GSM, WiMax and other wireless communication technologies without expensive dedicated hardware. It has been estimated in [27] that a fake mobile base station can be created with approximately PS500. This would provide a criminal with all the tools required to capture a signal, modify and re-broadcast it, create fake GSM base stations, and even send altered signals to roadside matrix boards. Currently, SDR attacks are particularly difficult to prevent. It would require the targeted wireless system to have been designed with particular provisions in place for preventing over-the-air attacks.