esa-sceva/satcom-chunk-collection huggingface dataset:
As a consequence, more efficient selection criteria are needed, in order to predict which patients will benefit from DIBH. A promising tool for fast patient treatment recommendation is Artificial intelligence (AI), the computer science devoted bestow upon machines the ability to perform tasks requiring human intelligence [23; 24; 25]. AI has been shown to perform a wide array of tasks that can support clinical decisions, such as patient selection, risk prediction, and disease detection [26; 27; 28; 29]. [PERSON] and co-authors [30] applied a machine learning technique to develop an automated patient selection for DIBH for breast cancer, based on variables such as the volumes of the heart and ipsilateral breast, or the lung and breast volumes. One of the most promising subfields of AI is that of multilayer neural networks, proposed in the 1980s [31] for image recognition [32], usually based on multiple convolutional layers. These structures are usually termed \"deep learning\" neural networks, as coined by [PERSON] [33]. DL networks can describe rich and comprehensive information, thus jointly performing data representation and prediction [34]. For these reasons, they became popular for performing automated detection of cancer [35] and other diseases [36; 37; 38]. They showed to be so versatile and powerful, that they could also perform image reconstruction [39], and rigid, [40] as well as deformable, image registration [41]. Recurrent neural networks (RNN) are a class of deep neural networks that can process a sequence of data in order to perform regression or classification [42]. Popular types of RNN include long short-term memory (LSTM) [43], and gated recurrent units. These types of neural networks have been exploited, for instance, for automated cardiovascular disease diagnosis, by detecting heart arrhythmia from an electrocardiogram [44]. In the field of radiotherapy, LSTM could predict tumour motion, due to respiratory cycle from time sequences of markers' positions [45]. DIBH radiotherapy could be a hotbed for the application of LSTM, as the patient data include respiratory motion data acquired during patient preparation [46]. Neural network-based classification of respiratory signals of patients acquired for DIBH, could allow the identification of patients that are eligible for DIBH. Such an AI-based analysis could provide early treatment recommendations from the DIBH signal acquired when preparing the patient for the treatment, removing the need to plan the treatments of DIBH and FB for dosimetric comparison. The purpose of the present study was to train an artificial intelligence binary classifier to select patients for DIBH RT, from the patient's respiratory waveforms. For this purpose, we used bidirectional LSTM (BLSTM), a deep learning recurrent neural network that specializes in analysis of time series data. The BLSTM network is trained on a retrospective series of patients, and validated using random training/test splitting. This is, to the very best of our knowledge, the first study to apply AI-based classification of respiratory tracks for radiotherapy treatment recommendation.
## 3 Results and Discussion  
### Performance Analysis  
In this section, we provide a performance estimate of our routing algorithm by analyzing its route selection, link stability, transmission delay etc.  
Figure 3: Optimal path selection.
## 4 Simulation Research Results
### Measures of Soft Sensor Quality  
In order to determine the quantitative quality of the soft sensor results, two quality indicators for the final results were used. They include measures of errors generated for BOD and DO signals.  
The first indicator of the quality of monitoring is the root-mean-squared error (RMSE):  
\[RMSE_{j}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}e_{j,i}^{2}}. \tag{10}\]  
The second indicator is the mean percentage error (MPE):  
\[MPE_{j}=\frac{1}{n}\sum_{i=1}^{n}\left(\left|\frac{x_{j,i}-\hat{x}_{j,i}}{x_{j, i}}\right|\right)\cdot 100\% \tag{11}\]  
where:  
\(e_{j,i}=x_{j,i}-\hat{x}_{j,i}\)--estimation error of the \(j\)th signal in the \(i\)th calculations step;  
\(n\)--number of calculations steps.  
The RMSE indicator represents the absolute error, while the MPE indicator is a measure of the relative error percentage.
## 2 Materials and Methods
### RF Amplifier of Signals  
However, the output signal is too small to be analyzed and so, an amplifier circuit has to be designed. In addition, for further enhancement of signal amplitude, the RF amplifier circuit is extended to transform a light signal from four photodiode detectors to four current signals. The technique is then developed to integrate the signals by using the RF summing amplifier circuit, as shown in Figure 3 and Equations (4) and (5) for the CD/DVD set, respectively.  
\[R{Fsignal}_{CD}=A+B+C+D, \tag{4}\]  
\[R{Fsignal}_{DVD}=a+b+c+d. \tag{5}\]  
Since the wavelength of CD, 790 nm, is larger than that of DVD, 650 nm, the photodiode detectors are chosen to automatically switch a laser beam of two different wavelength modes. The specifications of CD and DVD pick-up heads are compared in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **CD Pick-Up Head** & **DVD Pick-Up Head** \\ \hline \hline Laser wavelength (nm) & 790 & 650 \\ \hline Focusing area (Standard operating position) (mm) & \(\geq\) +0.8 & \(\geq\) –0.7 \\ \hline \multirow{2}{*}{Laser diode (LD)} & Threshold current (mA) & 20–40 & 25–45 \\ \cline{2-3}  & Operating current (mA) & 35–70 & 35–90 \\ \cline{2-3}  & Drive voltage (V) & 4.5–5.5 & - \\ \hline \multirow{2}{*}{Photodiode (PD)} & Operating supplied voltage (V) & 4.5–5.5 & - \\ \cline{2-3}  & Reference voltage (V) & 2.1 \(\pm\) 0.1 & - \\ \hline \multirow{2}{*}{Play mode voltage (V)} & \(\geq\)3.5 & \(\leq\)0.7 \\ \cline{2-3}  & Radio Frequency (RF) signal (mVP-p) & 1760 \(\pm\) 440 & 1560 \(\pm\) 390 \\ \hline \multirow{2}{*}{Focus Error Signal (FES)} & \((A+C)-(B+D)\) & \((a+c)-(b+d)\) \\ \hline \multirow{2}{*}{S-curve voltage (mVP-p)} & 530 \(\pm\) 212 & 860 \(\pm\) 301 \\ \hline \multicolumn{2}{c}{Emission power output of objective lens (mW)} & 0.23 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the CD/DVD pick-up head set of the Sanyo SF-HD 65 model.  
Figure 3: Circuit diagram of the developed photodiode detector and signal amplifier.
## 3 Results
### Accuracy Assessment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FIG.** & **YEAR** & \begin{tabular}{c} **NUMBER OF** \\ **SAMPLE POINTS** \\ \end{tabular} & \
\begin{tabular}{c} **CORRECT** \\ **PREDICTED** \\ \end{tabular} &
\begin{tabular}{c} **OVERALL** \\ **ACCURACY** \\ \end{tabular} \\ \hline a & 2000 & 1000 & 889 & 88.90\% \\ b & 2005 & 1000 & 875 & 87.50\% \\ c & 2010 & 1000 & 954 & 95.40\% \\ d & 2013 & 1000 & 914 & 91.40\% \\ e & 2016 & 1000 & 932 & 93.20\% \\ f & 2019 & 1000 & 926 & 92.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy assessment and validation by method number 2. Figure 5: Ground-truth sample points used for validation and accuracy assessment. Even in this case, the evaluation returned relatively good results, with a predictive error ranging between \(\pm 10\%\)-\(15\%\), and it is noteworthy that the upward trend of the built-up area expansion can be easily observed (Figure 6). This is very important when we want to quickly evaluate and map the expansion of built environments. Despite the fact that the accuracy assessment returned good results, in the analysis we identified some limitations of the method. Firstly, in some cases there is a false positive prediction related to arable land uncovered by vegetation. Additionally, many constructions or dumping sites have been misidentified as built areas and in a territory where constructions are continuously present on extended surfaces, this can be an issue.
## 6 Future Research Directions
### Robustness, Scalability, and Flexibility of Learning Frameworks  
Aspects of 6G networks that are highly dynamic include BSs affiliations, wireless routes, topologies of networks, and mobility dynamics. For instance, devices or terminals that enter or leave networks may have different QoS and QoE requirements. The settings of AI learning algorithms must be continuously modified, due to all of the aforementioned uncertainties in dynamic networks. High resilience, scalability, and flexibility in learning frameworks are essential for sustaining an infinite number of interacting entities and providing high-quality services in actual dynamic networks. As a result, the issue of how to develop powerful, scalable, and adaptable learning frameworks to support 6G networks is still open [34; 35].
# Fault-Tolerant Optimal Consensus Control for Heterogeneous Multi-Agent Systems
## 2 Preliminaries
### UAV Dynamics Model  
According to reference [27], a single UAV model be defined as Equation (6):  
\[\begin{split}\ddot{p}^{x}&=g\theta\\ \ddot{p}^{y}&=-g\phi\\ \ddot{p}^{z}&=f_{z}/m-g\\ g\dot{\phi}&=M_{\phi}/I_{x}\\ g\theta&=M_{\phi}/I_{y}\\ \ddot{\psi}&=M_{\psi}/I_{z}\end{split} \tag{6}\]  
where \(p^{x},p^{y},p^{z}\) represents the UAV's positions in the \(X,Y,Z\) axes; \(\phi,\theta,\) and \(\psi\) corresponds to the rotation angle of the UAV along \(X,Y,\) and \(Z\) axes, respectively. \(f_{z}\) denotes the lift force in the \(Z\) axes; \(M_{\phi}/I_{x},M_{\theta}/I_{y},M_{\psi}/I_{z}\) represents the control input for rotating the UAV around the three coordinate axes.  
If there is one leader UAV and \(n-m\) follower UAVs, then Equation (6) above can be transformed into a state equation:  
\[\dot{x}_{A0} =A_{A}x_{A0} \tag{7}\] \[\dot{x}_{Ai} =A_{A}x_{Ai}+B_{A}u_{Ai}\]  
where \(x_{Ai}=(p_{i},v_{i},\Omega_{i},\dot{\Omega_{i}})^{T}_{i},p_{i}=\left(p_{i}^{x },p_{i}^{y},p_{i}^{z}\right)^{T},v_{i}=\left(v_{i}^{x},v_{i}^{y},v_{i}^{z} \right)^{T}\),\(u_{Ai}=\left(u_{i}^{x},u_{i}^{y},u_{i}^{z}\right)^{T}\).  
\[\Omega_{i}=(g\theta_{i},-g\phi_{i},\psi)^{T},\dot{\Omega_{i}}=\left(g\dot{ \theta}_{i},-g\dot{\phi}_{i},\dot{\psi}\right)^{T},i=m,m+1,\ldots,n\]  
\(x_{A0}=(p_{0},v_{0},\Omega_{0},\dot{\Omega_{0}})^{T},p_{0}=\left(p_{0}^{x},p _{0}^{y},p_{0}^{z}\right)^{T},v_{0}=\left(v_{0}^{x},v_{0}^{y},v_{0}^{z}\right) ^{T}\) represents the leader UAV's state.  
\[A_{A}=\begin{bmatrix}0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\\ 0&0&0&0\end{bmatrix}\otimes I_{3},B_{A}=\begin{bmatrix}0\\ 0\\ 0\\ 1\end{bmatrix}\otimes I_{3}.\]  
\(A_{A},B_{A}\), respectively, represent the state-space matrix and input-state matrix of the UAVs. \(\otimes\) denotes the Kronecker product, indicating that the multi-UAV system is a linear system. Considering the failure of actuators in UAV, \(u_{Ai}^{f}=(u_{ix}^{f},u_{iy}^{f},u_{iz}^{f})\) represents the input after the actuator failure. The specific expression \(u_{Ai}^{f}\) is as follows:  
\[u_{Ai}^{f}=(I_{3}-\mathcal{P}_{Ai})u_{Ai} \tag{8}\]where the equation \(\mathcal{P}_{Ai}=diag(\rho_{ix},\rho_{iy},\rho_{iz})\in R^{3}\) is central to our analysis. This paper primarily examines partial actuator failures within a multi-following uncrewed aerial vehicle system, in which \(\rho_{ix},\rho_{iy},\rho_{iz}\) does not uniformly equal one across all instances. The dynamic model of each of the \(n-m\) UAVs in the system can be represented as follows:  
\[\dot{x}_{Ai}=A_{A}x_{Ai}+B_{A}u_{Ai}^{f} \tag{9}\]  
For the \(n-m\) UAVs actuator failure model described by Equation (9), when the multi-UAV system achieves consensus, this implies that all states of the UAVs satisfy the following equation:  
\[\underset{\text{t}\rightarrow\infty}{\lim}\|X_{\text{Ai}}(\text{t})-X_{\text {A0}}(t)\|=0 \tag{10}\]
# PALANTIR: An NFV-Based Security-as-a-Service Approach for Automating Threat Mitigation
## 3 Motivating Cases
### Use Case 2: Resilience of the E-Commerce Platform  
Although the new generation of SIEMs delivers powerful features in terms of storage, visualisation, and performance, as well as the ability to automate the reaction processes, the available countermeasures are limited, static, and deployed without comprehensive consideration of the impact of countermeasures on the infrastructure or even the continuity of the business [42]. Furthermore, cyber threats targeting SMEs vary significantly from threats in the large-scale industrial sector (for which most SIEM solutions have been designed [43]). For instance, phishing and malware present critical issues, especially for the SME sector. Furthermore, traditional SIEMs require the use of additional, external solutionsto automate the remediation. In fact, although over 51% of SMEs use AI for detection, only 18% of companies use AI/ML solutions in remediation [44].  
Finally, since ML methods have to adapt exactly to the respective environment, modern IT systems require a vast initial investment of personnel time as well as a high degree of expertise. However, in SMEs and Microenterprises (ME), IT support is often seen as cost-centric rather than a revenue generator [45]. Thus, SMEs/MEs focus on the continuity of IT services rather than protecting the key information resources and infrastructure. Furthermore, over 55% of SMEs and MEs lack the expert knowledge required to understand the risks and implications of policies such as \"bring your own device\" or the lack of an up-to-date cyber risk strategy poses to their business [46]. With this use case, we integrate the PALANTIR platform into the cloud and on-premises ecosystems of an ME involved in e-commerce and, in particular, an ecosystem with physical offices and a cloud environment. Each physical office consists of several residential-grade IT pieces of equipment (e.g., routers, low-cost switches) and various (personal) devices connected to the internet. Access to virtual space (e.g., PLESK platform) and (web) services (e.g., SQL and no-SQL databases, FTP server, web server) is protected with an Account Restriction policy via User Roles. Within the virtual space, the ME implements and runs its Customer Relationship Management (CRM), e-retail store, mail servers, product and content personalisation, and a Small Business Support System (BSS) framework to improve efficiency and flexibility. The majority of personal customer data are stored and handled in the cloud. The timely back-ups, malware protection, Bot (e.g., SEO spam and injection attacks), DoS, DDoS, and brute force attacks represent the major threats to e-commerce solutions [47].
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 2 Materials and Methods
### Undervoltage Algorithm  
The undervoltage (UV) algorithm is a commonly used method based on local voltage measurement. The process involves monitoring the value of the DC voltage and comparing it against a predetermined threshold (2). If the voltage drops below the threshold to a fault, it indicates a fault condition.  
\[\text{Fault detection}=1\text{ if }\text{V}_{\text{DC}}<\text{UV}_{\text{THR}} \tag{2}\]  
where V\({}_{\text{DC}}\) is the measured voltage and UV\({}_{\text{THR}}\) is the predefined threshold for the algorithm.  
UV protection works well in scenarios where faults cause significant voltage drops, such as short circuits to ground or between poles. However, for faults with higher resistance, where the voltage drops are less pronounced, this method might not detect faults effectively. It is more suitable for situations where the voltage deviations are significant enough to cause instability or damage to equipment.  
In reference [14], a pilot project of a flexible MVDC distribution grid constructed in China demonstrates the use of the UV algorithm as part of the protection scheme. The authors of [15] describe the protection system for an MVDC shipboard and propose an undervoltage threshold of 0.833 p.u. to meet the specific requirements of this application. Reference [16] proposes taking three consecutive samples that exceed the undervoltage threshold to claim that the system is under a fault condition. Despite the fact that taking three samples increases the fault detection time, it considerably enlarges the sensitivity of the protection algorithm, providing higher reliability and robustness, because false detections caused by transient undervolages are avoided.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation
### Security Analysis
#### 4.7.1 Security of the Communication Channel  
The framework achieves authenticated encryption against the quantum network adversary (\(\mathcal{A}_{net}\)) by composing two secure primitives. First, the session key is established using CRYSTALS-Kyber, a Key Encapsulation Mechanism (KEM) selected by NIST for its security against quantum attackers. Kyber's security is based on the assumed hardness of the Module Learning with Errors (M-LWE) problem [4]. The established key is then used with AES-256-GCM, an Authenticated Encryption with Associated Data (AEAD) scheme. The composition of a secure KEM and a secure AEAD scheme provides authenticated encryption, protecting both the confidentiality and integrity of all data in transit against \(\mathcal{A}_{net}\)[21].
## 3 Review of Previous Works
### Throughput Estimation Model
#### 3.1.3 Throughput Reduction Factor  
When multiple hosts are communicating with an AP simultaneously, the _throughput reduction factor_ is introduced in our model to improve the estimation accuracy [50]. It is calculated by:  
\[tp\times srf(m) \tag{4}\]  
where \(m\) is the number of hosts communicating with \(AP\), \(tp\) represents the estimated single link throughput between \(AP_{i}\) and \(host_{i}\), and \(srf(m)\) is the empirically derived contention factor at \(AP_{i}\) among the associated hosts for sending data through the CSMA/CA protocol. \(srf(m)\) is calculated by  
\[srf(m)=\left(\frac{1}{m+\frac{0.1(m-1)}{4}}\right)\times 1-(0.1\times m-1) \tag{5}\]
## 3 User Study
### Devices
#### 3.2.3 Practical Utilization  
Both VR and AR interaction interfaces find practical utilization across a spectrum of industries and applications. In VR, these interfaces have gained prominence in fields such as gaming, architectural design, and medical training, where users can engage with virtual environments, simulate procedures, and manipulate digital models with a high degree of fidelity. In contrast, AR interfaces have found applications in industries like education, maintenance, and navigation, enabling users to access contextual information and perform hands-free tasks within their real-world surroundings. The choice between VR and AR often depends on the specific use case. VR's immersive interaction interface shines in scenarios requiring detailed spatial manipulation and full immersion, while AR's gesture-based interface excels in situations where users need to overlay digital information onto their immediate environment without disrupting their physical surroundings. As both technologies continue to advance, their practical utilization is expected to expand into new domains, transforming how users interact with digital content and information in diverse contexts.  
The Quest 2 provides the Passthrough feature, which uses the device's various cameras and infrared sensors to visualize the user's real-world surroundings [14], as seen in Figure 2. As a result, both of these devices can display virtual objects co-located and visualized in the real world. Both devices provide head pose tracking and localize to a fixed reference frame. To ensure consistency between trials and an accurate ground truth map, both devices had to be localized to a known fixed world origin before beginning the mapping tasks. This was carried out on the AR headset using packages provided by Vuforia [15] to identify an ArUco marker representing the world's origin. On the VR headset, localization was performed using spatial anchors provided by the Oculus SDK. User map data were then saved relative to these known points in the reference frame of the headsets.
## 2 Video Camera Imaging Based Method
### Basic Framework
#### 2.1.5 Vital Sign Extraction  
Finally, vital signs are extracted by using frequency analysis or peak detection. For frequency analysis, a signal that contains a distinct periodicity is converted to the frequency domain using a discrete Fourier transform. The fast Fourier transform (FFT) is generally applied to calculate the corresponding frequency, \(F_{s}\). Discrete cosine transform (DCT), [PERSON]'s method or short-time Fourier transform (STFT) can also be used. When using a peak detection algorithm, the number of peaks, \(N_{s}\), is calculated during the processing period \(T\) (s). Heart rate and respiratory rate per minute can be calculated as follows:  
\[\text{HR or RR }=60\,\times\,F_{s} \tag{2}\]  
\[\text{HR or RR }=60\,\times\,(N_{s}/\text{T}) \tag{3}\]
## 3 Results and Discussion
### Interannual Variability in Vegetation Phenology during Dornant Seasons
This result was supported by GPP fluxes, which are a more direct reflection of vegetation greenness than ET and NEE fluxes that include the non-stomatal fluxes of evaporation and respiration, respectively. For example, cumulative GPP for 24-31 March of 2016 and 2018 in P18 was approximately 27 and 5 g C m\({}^{-2}\), respectively. More details on CO\({}_{2}\) fluxes are provided in a later section. The late senescence of newer vegetation growth (i.e., regrowth) in hay harvested (P20) or intensive grazing (P18) pastures during November of good rainfall years (i.e., higher rainfall during October-November) caused some interannual variabilities in vegetation phenology during the dormant season, as illustrated by higher EVI values. In P20, EVI\({}_{250\rm m}\) values on 1 November were 0.28-0.30 when October-November rainfall sums were higher in 2015 (149 mm), 2017 (111 mm), and 2018 (171 mm), while the EVI\({}_{250\rm m}\) was 0.21 on 1 November in 2016 (30 mm). However, EVI\({}_{250\rm m}\) values were similar (0.20-0.22) after mid-November in all years, most likely due to frost damage. Similarly, the EVI\({}_{250\rm m}\) in P18 was 0.26 on 1 November in 2018 compared to 0.21-0.22 in 2019 and 2020, which was supported by GPP fluxes. Cumulative GPP for 1-10 November in P18 was \(\sim\)20 g C m\({}^{-2}\) in 2018 (October-November rainfall of 171 mm) but \(\sim\)10 g C m\({}^{-2}\) in 2019 and 2020 (October-November rainfall of 115 and 92 mm, respectively). These results illustrated the positive impacts of rainfall on the regrowth of vegetation, even in the later parts of the growing seasons. In comparison, a previous study on old world bluestem reported a small impact of baling (i.e., hay harvest in summer) on seasonal GPP because increased GPP rates, driven by large rainfall events, in the post-baling period (i.e., fall) offset the reduction in GPP caused by baling [26].