esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods
### Atmospheric Chemical Potential (ACP) Correction Approach  
A physical model of thermal anomalies registered before earthquakes and around the time of volcano eruptions would explain that, due to air ionization by radon emanating from the active faults, the newly formed ions become the centers of water vapor nucleation and formation of the large ion clusters [75]. The bond energy of water molecules with ions is larger than that in pure water drops, where it is equal to the latent heat energy per one molecule \(U=0.422\) eV, which is equal to the water molecule chemical potential. The higher the ion production rate and concentration of ions, the higher the bond energy of water molecules with ions (i.e., the chemical potential). The process of evaporation/condensation, which is the phase transition of the first order, always takes place during the chemical potential equality. However, newly formed ions created by ionization have a different chemical potential, which is what we took into account. In the one-component approximation, we introduced the correction to the chemical potential \(\Delta U\), which takes into account this circumstance. In this case, the real chemical potential can be expressed as  
\[U(t)=U_{0}+\Delta Ucos2t \tag{5}\]  
where \(U_{0}\) is the chemical potential for pure water, and \(U(t)\) is the chemical potential where ionization and hydration are taken into account. By \(cos2t\), we take into account the daily changes of the solar radiation. The changes of the chemical potential under action of ionization will affect the relative humidity \(H\), which can be estimated using the parameter \(\Delta U\):  
\[H(t)=\frac{\exp\left(\frac{-U(t)}{kT}\right)}{\exp\left(\frac{-U_{0}}{kT} \right)}=\exp\left(\frac{U_{0}-U(t)}{kT}\right)=\exp\left(-\frac{0.032\ \Delta Ucos^{2}t}{(kT)^{2}}\right) \tag{6}\]  
where \(k\) is the Boltzmann constant, and \(T\) is the air temperature. The changes of the chemical potential can be then derived from the formula above by using the satellite microwave sounders data on vertical profiles of the air temperature and relative humidity.
# Quantum Key Distribution Networks: Challenges and Future Research Issues in Security
## 2 Background to Quantum Key Distribution (QKD) Networks
### QKD Network Type
#### 2.3.1 Active Optical Switch Network  
In the active optical switch network, an active optical switch mechanism is employed to establish a direct optical P2P quantum channel between any two quantum nodes (shown in Figure 3a) [58]. Using the switching mechanism, any two quantum nodes can be employed to establish a direct connection and execute the QKD protocol without any assistance from other nodes; however, this framework has two drawbacks: (1) the communication distance of the QKD network is not extended, that is, the distance is still bounded by the maximum communication distance between any two quantum nodes, and (2) all quantum technologies applied in the network must be consistent, which may restrict the application. However, the implementation challenges of the active optical switch in the physical layer still affect the performance of qubit communication. For example, the use of active optical switch will cause additional amount of photon losses and leads to shorten the maximum distance of quantum channels [59]. Therefore, it is an important issue to develop an active optical switch with minimal loss and noise, and without disturbing the states of qubits [60; 61; 62].
## 2 Background for IoT Architecture
### The Application Layer  
The Application Layer is the topmost layer in IoT architecture, delivering tailored services to end-users through applications in fields, such as healthcare, agriculture, smart cities, and industrial automation. This layer processes data aggregated by the Middleware Layer, ensuring that it meets quality and relevance standards for specific applications. End-users access services through Internet-enabled devices, such as smartphones, computers, and tablets, which facilitate interaction with IoT systems in real time [17].  
Energy efficiency in the Application Layer often focuses on optimising data presentation and reducing computational requirements for end-user applications. By employing data filtering and prioritisation techniques, the Application Layer can minimise the amount of data transferred and processed by end-user devices, which helps conserve energy, particularly in applications that involve large data volumes. Furthermore, efficient design and implementation of user interfaces ensure that application interactions are intuitive and reduce the energy burden on devices accessing the IoT services [18].
# New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs
###### Abstract  
Background: Cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are parameters that are used to assess cardiac size on chest radiographs (CXRs). We aimed to investigate the performance and efficiency of artificial intelligence (AI) in screening for cardiomegaly on CXRs. Methods: The U-net architecture was designed for lung and heart segmentation. The CTR and TCD were then calculated using these labels and a mathematical algorithm. For the training set, we retrospectively included 65 randomly selected patients who underwent CXRs, while for the testing set, we chose 50 patients who underwent cardiac magnetic resonance (CMR) imaging and had available CXRs in the medical documentation. Results: Using U-net for the training set, the Dice coefficient for the lung was 0.984 \(\pm\) 0.003 (min. 0.977), while for the heart it was 0.983 \(\pm\) 0.004 (min. 0.972). For the testing set, the Dice coefficient for the lung was 0.970 \(\pm\) 0.012 (min. 0.926), while for the heart it was 0.950 \(\pm\) 0.021 (min. 0.871). The mean CTR and TCD measurements were slightly greater when calculated from either manual or automated segmentation than when manually read. Receiver operating characteristic analyses showed that both the CTR and TCD measurements calculated from either manual or automated segmentation, or when manually read, were good predictors of cardiomegaly diagnosed in CMR. However, McNemar tests have shown that diagnoses made with TCD, rather than CTR, were more consistent with CMR diagnoses. According to a different definition of cardiomegaly based on CMR imaging, accuracy for CTR measurements ranged from 62.0 to 74.0% for automatic segmentation (for TCD it ranged from 64.0 to 72.0%). Conclusion: The use of AI may optimize the screening process for cardiomegaly on CXRs. Future studies should focus on improving the accuracy of AI algorithms and on assessing the usefulness both of CTR and TCD measurements in screening for cardiomegaly.  
chest X-ray; cardiothoracic ratio; transverse cardiac diameter; deep learning; segmentation
# Improved Matching Algorithm with Anchor Argument for Rotate Target Detection
## 3 The Proposed Method  
The proposed method is built based on the oriented anchor and bounding box shown in Figure 3. First, we designed multiple copies of each oriented anchor with different  
Figure 2: The number of anchors matched to a target under the same IOU threshold. Baseline’s matching methods greatly fluctuate at either 4, 6 or 7, with a variance of 1.396, which will produce great unfairness in matching. Our method has small fluctuation, most of which are 8, and a few are 7 and 10, and the variance is 0.5, which is fairer in matching when the receptive field is appropriate.  
angles and defined the intersection over union (IoU) between the target and the anchor as the largest skew IoU of the target, and these copies retained the advantage of high matching accuracy of multi-anchor frame in the matching stage and determined more accurate RoIs through this method. Second, after determining the accurate receptive field, we only applied one anchor to regress the coordinates of rotated bounding boxes, which reduced the complexity and computational burden of the multi-anchor structure. Third, this structure was straightforward and can improve the performance of existing models with multiple anchors while reducing the computational cost without the demands of abundant computation and complicated structures. In this section, we first introduce the existing object detection algorithm based on the rotating anchor and state the omission in the matching stage due to the angle difference between the target and the anchor. Then, we propose our method: an auxiliary anchors matching method. This can intensively match target with more oriented anchors without greatly increasing the amount of calculation to avoid some matching omissions caused by the large difference between the target and the preset rotating anchor.
# Quality of Experience in 6G Networks: Outlook and Challenges
## 2 Quality of Experience in the Cellular Evolution
### Expectations for QoE in 6G Networks  
As aforementioned, considerable deliberations are already underway for the 6G network while the first phase of nonstandalone 5G is rolled out. In March 2020, for example, the ITU signaled that it commenced research work on the 6G networks and beyond, with the expectation that the earliest visions (or requirements) be specified in 2023. To be sure, an ITU focus group called \"Focus Group on Technologies for Network 2030\" commenced in mid-2018, and it takes a holistic view of telecommunications in 2030 and beyond, i.e., a view that is not limited to cellular environments.  
Some have voiced concerns that it is too early (and too risky) to contemplate 6G [13], on the sound premise that the full potential of the extensive softwarization in 5G networks and other technologies is yet to begin. There is also the fact that the industry's struggle with initial deployment in 2020 might require time in seeking recovery. Despite these concerns, contemplations on 6G are already in an avalanche, and while a specific timeline for development requirements and specification of 6G networks is yet to be formalized, a review of the industrial and research view of 6G networks appear to have substantial commonalities.  
However, before discussing QoE in 6G networks, a review of the enabling technologies and the expected services is needed. This review is provided in the next two sections. Meanwhile, further details on 5G networks can be found in [41, 42].
## 3 Build the Controller Model
### Vehicle Dynamics Model  
For the motion control of intelligent vehicles, it is necessary to analyze the dynamic model of the lateral motion of the vehicle. The dynamic model of the vehicle is considered comprehensively [27], as shown in Figure 7. \(\beta\) is the vehicle center of mass sideslip angle. \(v_{x}\) is the longitudinal velocity of the vehicle at the center of mass, and \(v_{y}\) is the lateral velocity of the vehicle at the center of mass.  
Figure 6: Schematic diagram of separating axis theorem.  
In the case of a small tire slip angle, the lateral force of the tire is proportional to the slip angle.  
The slip angles of the front wheel are as follows:  
\[\alpha_{f}=\delta_{f}-\theta_{vf} \tag{12}\]  
The formula for the slip angle of the rear wheel is as follows:  
\[\alpha_{r}=-\theta_{vr} \tag{13}\]  
The lateral force of a single front wheel of the vehicle is as follows:  
\[F_{yf}=C_{af}\Big{(}\delta_{f}-\theta_{vf}\Big{)} \tag{14}\]  
where \(\delta_{vf}\) is the angle between the tire velocity vector and the longitudinal axis of the vehicle, \(\delta_{f}\) is the steering angle of the front wheel, and \(C_{af}\) is the proportional coefficient, which is called the cornering stiffness of the front wheel.  
Similarly, the lateral force of a single rear wheel of the vehicle is as follows:  
\[F_{yr}=C_{ar}(-\theta_{vr}) \tag{15}\]  
where \(C_{ar}\) is the proportional coefficient, which is called the cornering stiffness of the rear wheel, and \(\theta_{vr}\) is the speed angle of the rear wheel of the vehicle.  
According to Formulas (16) and (17), the front wheel speed angle \(\theta_{vf}\) and rear wheel speed angle \(\theta_{vr}\) of the vehicle are calculated as follows:  
\[tan\Big{(}\theta_{vf}\Big{)}=\Big{(}v_{y}+L_{f}\dot{B}\Big{)}/v_{x} \tag{16}\]  
\[tan(\theta_{vr})=\Big{(}v_{y}-L_{r}\dot{B}\Big{)}/v_{x} \tag{17}\]  
where \(L_{f}\) is the distance between the center of mass and the front axle of the intelligent vehicle, and \(L_{r}\) is the distance between the center of mass and the rear axle of the intelligent vehicle.  
The formula obtained by approximating the small angle is as follows:  
\[\theta_{vf}=\Big{(}v_{y}+L_{f}\dot{B}\Big{)}/v_{x} \tag{18}\]  
\[\theta_{vr}=\Big{(}v_{y}-L_{r}\dot{B}\Big{)}/v_{x} \tag{19}\]  
Figure 7: Kinetic model.  
The yaw dynamics equation obtained by the torque balance around the \(Z\) axis is as follows:  
\[I_{z}\ddot{B}=2L_{f}F_{yf}-2L_{r}F_{yr} \tag{20}\]  
where \(I_{z}\) is the yaw moment of inertia of the vehicle body.  
The two-degree-of-freedom dynamic model of the intelligent vehicle is as follows:  
\[\left\{\begin{array}{l}\dot{\beta}=\frac{2F_{yf}^{2}+2F_{yr}}{2 mF_{x}}-\dot{ B}\\ \ddot{B}=\frac{2L_{f}F_{yf}-2L_{r}F_{yr}}{I_{z}}\end{array}\right. \tag{21}\]
# Hardware Prototyping and Validation of a W-\(\Delta\)DOR Digital Signal Processor
## 3 W-\(\Delta\)DOR Hardware Implementation
### Simulation and Synthesis Results  
The W-\(\Delta\)DOR DSP core was simulated in MATLAB & Simulink, in both floating point (FLP) and fixed point (FXP) arithmetic. A simulation-based FLP to FXP optimization flow was performed to minimize the registers word length maintaining the target specification of 50 dB of Dynamic Range on the generated RRC signal depending on the modulation index value. The W-\(\Delta\)DOR core architecture (Figure 2) was coded in VHDL, synthesized by using ALTERA Quartus II 13 IDE Tool, and was implemented on an ALTERA Stratix II EP2S180 FPGA. The maximum clock frequency was 137.9 MHz (100 MHz required). A summary of the hardware resources usage (post Place & Route) is shown in Table 1.
## 4 Enological Yeasts and Health-Related Compounds  
### Biogenic Amine Reduction Content  
Biogenic amines (BAs) are low-molecular-weight compounds obtained from amino acid precursors, mainly by microbial decarboxylation. Several investigations have reported the presence of different BAs in any type of wine, such as histamine, tyramine, and putrescine [153]. BA levels in wine depend on many factors, including grape variety, climatic conditions, agricultural practices, vinicification techniques, strains used in fermentation and aging, and wine parameters such as pH, alcohol content, and sulfur dioxide content [154]. Red wines showed higher levels of BAs, especially putrescine (PUT) and histamine (HIS), than white wines, with an average concentration of 7.30 and 2.45 mg/L, respectively [155]. It is relevant to highlight that, in sensitive consumers, exposure to high BA concentrations may result in health issues such as headache, anaphylaxis, nausea, hypertension, nervous symptoms, organ failure, and renal intoxication. Furthermore, ingestion of high levels of HIS is associated with the onset of histamine poisoning, characterized by hypertension, flushing, tachycardia, and gastrointestinal symptoms; ingestion of tyramine is associated with hypertensive crisis and cardiovascular symptoms [156; 157]. Lactic acid bacteria (LAB) are considered the main cause of BA production, either during maloalactic fermentation or due to contamination during various stages of winemaking [158]. Among wine yeasts, _B. bruxellensis_ shows the highest amine production, while it appears to be a strain-dependent condition for _S. cerevisiae_[159]. Moreover, it has already been reported that some non-conventional yeasts, such as _Debaryomyces hansenii_, can reduce the BA contents in food even though the species is not suitable for winemaking due to its reduced ethanol tolerance and production of unpleasant aromas [160].  
Therefore, exploring other NS yeasts that do not produce BAs as starters can reduce the concentrations of these compounds in wine. In this way, _L. thermotolerans_ and _Shizosachromyes pombe_ can be combined to simultaneously perform AF and malic acid degradation, thereby eliminating MLF in the traditional winemaking process and reducing the activity of LAB and, subsequently, the risk of BA production in wine [161; 162; 163]. Furthermore, it was similarly observed that the co-inoculum of _T. delbrueckii_ TD20 with _H. vineae_ HV6 significantly reduced tyramine level in Petit Manseng must [164], and it has been reported that the use of the _H. uvarum_ FS35 strain, in sequential inoculum with commercial _S. cerevisiae_, produced a wine with lower level of putrescine and higher amounts of glycerol, lactic acid, acetic acid, phenylethyl alcohol, ethyl acetate and beta-phenylethyl acetate compared with the control fermentation; proving that _H. uvarum_ FS35 strain is a promising strain to reduce BAs in wines through copper amine oxidase 1 (CuAO1) activity that lead to a coordinated response in the oxidative deamination of putrescine to 4-amino-buttanal and subsequent dehydrogenation to 4-amino-butanoate [165].
## 5 Results and Discussion
The training accuracy climbs rapidly, whereas the callback stops the network after 56 epochs because further training will not yield useful results and will only result in overtraining. This is because the validation loss does not decrease. The model cannot accurately distinguish the emotions in the testing phase, and the validation loss always stays between 0.11 and 0.09. Figure 5g shows the accuracy graph for the TESS dataset. There is again an exception to note, namely that the TESS dataset being balanced and great for SER produces excellent results. The accuracy climbs to greater than 95% within the first five epochs and then saturates around the 99% mark, ranging from 99.1% to 99.73% on training and validation. This is shown in the loss graph (Figure 5h) as well, which shows the loss nearing 0.01 as the epochs progress. The TESS dataset exhibits the highest values in all the parameters measured, with an average value of 99% in F1-score, precision, recall, and accuracy. Table 4 shows a comparison of the proposed model with the existing studies conducted for the respective datasets. It can be seen that our proposed method outperforms the existing methods by good margins. Since TESS is a dataset that often displays an accuracy greater than 99%, there are not many comparisons for it. That is why RAVDESS, SAVEE, and EmoDB were given more emphasis during research and comparison. The average accuracy for the RAVDESS dataset lies between 60-75%, whereas our model achieved an accuracy of 87% with GWO optimization. The CNN and LSTM, however, underperformed for both RAVDESS and SAVEE datasets without the GWO optimizer. The GWO-SVM beat the traditional DNN frameworks and existing SVM models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Classifier Used** & **Accuracy** \\ \hline [PERSON] et al. [44] & RAVDESS & Bagged ensemble of SVMs & \(75.69\%\) \\ [PERSON] et al. [42] & RAVDESS & DNNs & 64.52\% \\ [PERSON] and [PERSON] [43] & RAVDESS & SVMs & 60.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with existing studies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Classifier Used** & **Accuracy** \\ \hline This Work (Proposed Method) & SAVEE & GWO-SVM & 75\% \\  & GWO-CNN & 65.47\% \\ This Work (Proposed Method) & EmoDB & GWO-SVM & 85\% \\  & & GWO-CNN & 78\% \\ This Work (Proposed Method) & TESS & GWO-SVM & 99.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 5: _Cont._
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 3 Adaptive Bat Algorithm Optimizes Observation Matrix
### Observation Matrix Optimization Based on Adaptive Bat Algorithm
#### 3.3.2 Define Population Interval  
The foraging behavior of bats is to find relatively better food in an area; the area setting can ensure that bats do not fly out of this area during the foraging process. In this algorithm, the initial position of the bat population is the Gaussian random observation matrix, which obeys the standard normal distribution, and the algorithm defines the domain interval as \([-3,3]\).
## 3 Results
### Experimentation with Inputs  
In our previous study, we concluded that adding wind and tide to the input term can improve the training of the LSTM model. However, at that time, we used the flow velocity of the Finite-Volume Coastal Ocean Model (FVCOM) as the true value [32]. Therefore, as the closest approximation to the true ocean current data, obtained from ADCP measurements, we are curious as to whether incorporating wind and tidal factors can improve the correlation between the predicted values and the ADCP data. Experiments were conducted using four groups. The first group used radar flow velocity, wind, and tide as the input terms simultaneously. The second group used radar flow velocity and tide as the input terms simultaneously. The third group used radar flow velocity and wind as the input terms simultaneously. The fourth group made a direct comparison. According to Figure 5, the first three groups of experiments had a positive effect on radar correction. The correlation coefficients of the corrected radar flow velocities were higher than the initial  
Figure 4: Temporal correlation and RMSE variations of raw radar currents and bottom-mounted ADCP with corrected radar data.  
Figure 3: Comparison of raw radar currents and bottom-mounted ADCP observations. Red represents the monitoring data from the bottom-mounted ADCP, while black represents the raw radar data.  
radar flow velocities, and the root-mean-square errors were lower than the initial radar flow velocities. It is evident that the correlation decreases as the number of days of corrected radar increases. Although the radar effect improves from 15 to 21 days, the correction effect decreases with the increase in correction time.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 4 Simulation Scenario
### Proof of Concept Scenario  
A proof of concept (PoC) scenario is designed (see Figure 4) according to the aforementioned proposed architecture.  
In the control plane, the Open Network Operating System (ONOS) is used as open-source SDN controller. DisPaC is programmed over the northbound interface of ONOS, but it could be migrated to any other controller.  
In the data plane, the core network consists of five OF switches forming a mesh topology. Two of them are the exchange point of WiFi and LTE access networks, and another one is directly connected to the CCE. In the access network, there are six OF-APs with a separation of 600 m between them and a single eNB located in the center between two APs, so the total trajectory is 3 km.  
Figure 4: Proof of concept (PoC) scenario; ONOS—Open Network Operating System layout.
## 5 Approaches of Optimal State Estimation Utilizing Filtering  
In the 1960s, the state estimation was primarily established, and their development evolved to considerable applications such as Kalman filtering and robust filtering [18,106]. It applies all practical fields, namely chemical engineering, aerospace engineering, and robotics. At a given moment, the state estimation is known as the complete reproduction of the internal condition of the system.