esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients
## Materials and Methods
### Assessing Inter-Sanner Variations of HU-Density Calibration Curves  
Accurate and reproducible assessment of Hounsfield unit (HU) [29] distribution within the lungs is of paramount importance. The values of the HU of the CT images depends on the calibration curve internal to each equipment (HU as a function of the attenuation coefficient of the materials). For verification, we investigated the impact of the HU-density calibration curves obtained with the parameters used in the image acquisition phase of the CT equipment utilized. To assess the impact of the calibration curves, the acquisition parameters of COVID-19 patients we analyzed, and the specific protocol calibration curves reconstructed for all CT scanners used. For the study description, tube voltage and X-ray current were collected from the header DICOM (Digital Imaging and COmunications in Medicine) of the CT image in the available database. Tube voltage was set at 120 kV for all CT scans, while the X-ray current was modulated according to patient size. The data were divided for each piece of equipment--the median values of the X-ray current for each device were calculated; regarding GE LightSpeed (used for 88% of patients), the values of the minimum, the maximum, and the first and the third quartile were also calculated, as shown in Table 1.  
These parameters were used to perform acquisitions with the calibrated CT image quality phantom, Catphan 600--The Phantom Laboratory, Battenville, NY, USA, in the entire HU range (\(-\)1000 to +1000). A tube voltage of 120 kV and the median value of X-ray current for each piece of equipment was used for the exposition of the Catphan phantom. Moreover, for the General Electric equipment, the first and third quartiles were also used as exposure parameters. The Catphan consists of an inner layer containing several inserts of different materials corresponding to different attenuation coefficients. Knowing the attenuation coefficients of these materials, and measuring the average HU values inside each insert, it was possible to obtain the characteristic linearity curve of the equipment at the exposure parameters selected in the acquisition phase.  
Five repeated measurements were carried out for each scanner, and each X-ray current value considered. The average HU value was used to calculate the linearity curve for each insert.  
Consequently, for Philips Brilliance and Philips Incisive, a single calibration curve was obtained, relative to the median value of the current used for the patient scans carried out on each. For the GE LightSpeed, five calibration curves were obtained, corresponding to the minimum, maximum, 25 th quartile, 50 th quartile (median), and 75 th quartile values. A region of interest (ROI) was selected within each Catphan insert. Using the ImageJ image processing software, the mean value and standard deviation of the HU values within the ROI were evaluated. The variation in the average HU values within each ROI was then calculated between the various acquisitions taken.
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 2 The Dataset and the Studied Buildings
### The Studied Buildings
It appears that the Eiffel tower is subjected to very complex deformation depending on weather conditions, mainly caused by three factors:  
* The wind that makes the buildings oscillate in the perpendicular direction to the wind flow. [PERSON] sized structure for the lateral oscillations due to wind up to 70 cm. However, oscillation amplitude records did not exceed 18 cm amplitude reached during the violent storm of December 1999. Hence, wind is not the most important deformation factor for this structure. * The air temperature, that makes the iron and the concrete expend. The whole structures have then a vertical deformation. Depending on the temperature, the tower can elongate or shrink several centimeters. Given that the thermal expansion coefficient is approximately 10\(\times\)10\({}^{-6}\) m/(mK) for iron and concrete, buildings with a height of 100 m will have a 1 cm thermal expansion by 10 \({}^{\circ}\)C temperature difference. * The sunlight, that makes the side of the structures exposed to the sun expends more that the side of the structure that is in the shadow. The whole structures are thus bended in the direction opposite to the sun. The top of the Eiffel Tower can be shifted by 20 cm on a sunny day. This is thus the prevalent deformation effect for the studied buildings.
# A Comprehensive Survey on Knowledge-Defined Networking
## 6 Data Plane  
### Introduction to Data Plane  
The bottom layer of the KDN architecture is the data/infrastructure plane, consisting of forwarding elements that are interconnected using transmission media. This layer's architecture and performance in KDN are very similar to those of SDN. However, there are a few differences in the data plane of KDN compared to SDN. First, the data plane is managed (configured and monitored) by the management plane and controlled (flow rule installation containing forwarding rules, installing access control rules, prioritizing network traffic based on QoS, implementing security policies, etc.) by the control plane. Furthermore, in KDN, more data are collected by management and control planes compared to SDN. Extra data collected in KDN are used for knowledge generation, as knowledge is used in making network control decisions by the controller, network monitoring and configuration decisions by the management plane, and to dynamically update application plane policies. Due to the extra communication burden utilized for sending data to management and control planes, forwarding elements in KDN require more resources, such as bandwidth and processing power, compared to switches in SDN.  
Let us discuss the components of the data plane in detail, as given in the following subsections.
## 4 Numerical and Experimental Tests
### Near-hover Flight Tests  
The control software system runs on a high performance DSP-based hardware platform (TI TMS320F28335) with a control period of 10 ms. The platform also provides a high precision timer, eight 16-bit pulse width modulation (PWM) channels for controlling the actuators of the MAV and several serial ports for data transmission and communication with external devices. The light-weightsensor unit, with an update rate of 200 Hz, includes a MEMS-based Inertial Measurement Unit (IMU), a differential GPS and an Extended Kalman Filter, which can provide a smooth position, velocity and attitude solution by fusing the measurements from the IMU and GPS. As shown in Figure 4, the near-hover flight tests are subsequently conducted to compare the performance of the proposed strategy with that of the conventional controller. For a fair comparison, all flight tests begin with hover, followed by simple way-point navigation, and the flight test data of the proposed strategy are recorded for comparison after the identification parameters reach a near-steady state. The results for a period of time from these flight tests are shown in Figures 5 and 6.  
The performance of the proposed strategy is first evaluated at low speeds where a square pattern is flown. The noisy tracking error, shown in Figure 5c, is within an acceptable range. The ground track view of trajectory, shown in Figure 5g, illustrates that the proposed strategy can almost eliminate the moving oscillations during the whole test process even though there exists the significant coupling between the pitch and roll axes shown in Figure 5e. We can therefore conclude that the proposed strategy can achieve a guaranteed flight performance.  
Figure 4: Flight test.  
Figure 5: _Cont._  
However, the flight test results of the conventional controller implemented through the widely used Pixhawk autopilot module, shown in Figure 6, appear to deteriorate significantly, mainly because the conventional controller cannot accommodate the unknown system parameters or eliminate the coupling among axes.  
The results also show that the variations of the attitude angles and the dynamic displacements increase significantly, and the moving oscillations are quite obvious when the MAV is commanded to perform the same flight mission. The comparison of the above two flight test results have further verified the proposed strategy performs better than the conventional controller near hover.
## 4 Discussion
In addition, the angles of incidence and emergence by TDS-1 are observed to jump along the track. This kind of jump causes the difference between the two to jump about \(\pm 2^{\circ}\) as shown in Figure 5a. The interval of these jumps is about 300 samples, and since the sampling time interval of SGR-ReSI is 1 s, the jumps' time interval is about 5 min. Furthermore, the positive and negative of the angle jumps are opposite before and after the angle reaching the minimum. This is presumably because the low-noise amplifier of the TDS-1 satellite receiver's nadir load was switched from the nadir pointing antenna to the built-in blackbody to calibrate the noise reference every 5 min [36], but this calibration inevitably affects its own specular reflection point positioning accuracy. Although these jumps also affect the reflection geometry determined by the GFRSCM, the jumps of the angle difference are on the order of \(10^{-4\circ}\), which can be ignored. The method proposed in this study will be an important support for the research team to improve the accuracy of underwater gravity matching navigation based on the high-accuracy and high-spatial-resolution GNSS-R altimetry constellation principle. At present, we have carried out sea surface altimetry experiments based on shipborne GNSS-R receiving instruments in different regions and weather conditions in the East China Sea and South China Sea. The method proposed in this study will be applied to the collected data to correct the position of the specular reflection point, and to study the different influences of the specular reflection points positioning accuracy improvement on the altimetry accuracy in regions with different geoid undulations and sea surface conditions. On the other hand, this study introduces the correction of the main factor which determines the sea surface elevation--the earth's gravity field--and corrects the reflection reference surface to geoid. On this basis, the subsequent work will introduce the ocean tidal model to further correct the reflection reference  
Figure 6: The angle of incidence and emergence and their difference on single track. surface, in order to further improve the positioning accuracy of the specular reflection point, and to finally establish a global and full-sea-state reflection reference surface model. Additionally, the TDS-1 satellite data only contains GPS reflection data. With the completion and full operation of the four major GNSSs and the publications of their precise orbit information in the future, we will introduce the data of more GNSSs. This will help us to study the difference in positioning accuracy of specular reflection points between different GNSSs due to their different parameters such as orbit and signal, etc., and the corresponding different effects on sea surface altimetry accuracy.
# Generalized Paxos Made Byzantine
## 2 Background and Overview  
### Paxos and Its Variants  
#### 2.1.1 Classic Paxos  
The Paxos protocol family solves consensus by finding an equilibrium in face of the well-known FLP impossibility result [17]. It does this by always guaranteeing safety in an asynchronous system, but at the same time making the observation that most of the time systems have periods during which they can be considered synchronous, since long delays are often sporadic and temporary. Therefore, Paxos only foregoes progress during the temporary periods of asynchrony, or if more than \(f\) faults occur for a system of \(n=2f+1\) replicas [7]. The classic form of Paxos employs a set of proposers, acceptors and learners, runs in a sequence of ballots, and employs two phases (numbered 1 and 2), with a similar message pattern: proposer to acceptors, acceptors to proposer (and, in phase 2, also acceptors to learners). To ensure progress during synchronous periods, proposals are serialized by a distinguished proposer, which is called the leader.  
Paxos is most commonly deployed as Multi (Decree)-Paxos, which provides an optimization of the basic message pattern by omitting the first phase of messages from all but the first ballot for each leader [8]. This means that a leader only needs to send a _phase 1a_ message once and subsequent proposals may be sent directly in _phase 2a_ messages. This reduces the message pattern in the common case from five message delays to just three (from proposal to learning). Since there are no implications on the quorum size or guarantees provided by Paxos, the reduced latency comes at no additional cost.
# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 4 Experiment Results and Discussions
### Runway Localization Analysis  
In this part, we primarily elaborate on two aspects. Firstly, we compare and analyze the runway detection performance before and after dehazing. Subsequently, we quantify the errors of the localization equations for the left and right runway lines.
## 3 Material and Methods
### Data Preprocessing  
Data preprocessing is crucial, involving the refinement and transformation of raw data into a suitable format for analysis and model training. This study details the preprocessing stages for data obtained from the EPVIGILA system.  
\begin{table}
\begin{tabular}{c c c c} \hline
**All Patients** & **Suspected** & **Confirmed** & **Total** \\ \hline Age & 37 & 36 & 39 \\ \hline Gender male (\%) & 52.1\% & 52.7\% & 51.2\% \\ \hline Gender female (\%) & 47.9\% & 47.3\% & 48.8\% \\ \hline \end{tabular}
\end{table}
Table 1: Age and gender demographics among EPVIGILA patients.  
Figure 3: Comorbidity distribution of EPVIGILA patients.  
Figure 2: The distribution of symptoms among EPVIGILA patients.  
First, in the _data cleaning_ stage, 1939 records containing sensitive patient information, such as symptoms, illnesses, and geographic location, were considered. We focused on symptoms and comorbidities as the primary predictors for the classification models. During this phase, missing data were appropriately addressed, and records without symptoms or comorbidities were also managed.  
Next, we moved on to the _binary classification_ stage. The dataset includes multiple levels of COVID-19 suspicion, from confirmed cases to discarded cases. For this study, data were filtered to include only two classes: confirmed cases and discarded cases, allowing for binary processing. This step is essential, as it enables the models to learn to distinguish between positive (confirmed) and negative (discarded) cases.  
Next, in the _feature encoding_ phase, ML algorithms use numerical inputs; therefore, categorical features, such as symptoms and comorbidities, were transformed into numerical representations. To achieve this, the one-hot encoding method was applied, which converts each pattern into a binary vector. Each category was represented as a binary vector with 1 in the position corresponding to the category and 0 in the others. For example, if \"Fever\" and \"Cough\" are symptoms, and \"Obesity\" and \"Diabetes\" are comorbidities, the encoding process transforms the data as shown in Table 2.  
After applying one-hot encoding for categorical variables, we defined the binary target variable as COVID-19 positive versus negative cases. This classification allowed the model to distinguish effectively between confirmed cases and non-cases, improving the focus on clinically relevant outcomes. These preprocessing steps, including encoding and binary targeting, enhance the model's interpretability and support replicability in future studies.  
Finally, in the _data splitting_ phase, to evaluate the performance of the ML models, the dataset is divided into two parts: a training set and a test set. The training set is used to train the model, while the test set is used to evaluate its performance on unseen data. Data were split into 60% for training, 20% for validation, and 20% for testing. This split was conducted randomly, ensuring that the distribution of confirmed and discarded cases remained balanced in both sets.
# An Initial Dot Encoding Scheme with Significantly Improved Robustness and Numbers
## 2 Coded Mark Design and Detection  
### Coded Mark Design  
The shape of the mark points is varied, and the most common ones are circles, crosses and dot arrays. When designing coded points, the following requirements should provide three aspects [22]:  
* Coded size is appropriate
* Coded graphics are unique.
* Good stability, less affected by rotation.  
As shown above in Figure 1, the coded mark points are composed of three layers: the innermost layer is the positioning layer which consists of a pure black circle, and the function is to locate the coordinates of mark point; the secondary layer is the starting layer, consisting of a small solid ring that determines the starting point of the coded layer; the outermost layer is the coded layer, consisting of several solid or hollow circles, which are used for the encoding of mark points.
## 3 Results and Discussion
Although existing typical deep learning-based schemes do not require extra spectra compared to traditional pilot schemes, they are slightly inferior to the direct reception scheme proposed in this paper. Moreover, their signal processing process is more complex, making them less suitable for application in end-to-end VLC systems. To further verify the accuracy of the proposed algorithm structure for end-to-end DCO-OFDM VLC, we calculate the cumulative distribution function (CDF) of the data error predicted by three different algorithm structures, allowing us to observe the error distribution across all data points as shown in Figure 9. All three algorithm structures achieve good error performance, with eighty percent of the data points having an error within 0.05, indicating that all three channel models can effectively fit the experimental channel of the DCO-OFDM VLC system. The details in the figure show that the Transformer outperforms BiLSTM and BiGRU in error performance for most data points, owing to its multi-head attention mechanism that accurately captures various channel effects. Figure 7: Different deep learning-based receiver schemes for DCO-OFDM: (**a**) Existing typical receiver scheme. (**b**) Proposed end-to-end channel model for direct reception scheme. Figure 8: BER for different receiver schemes. (**a**) BER comparison between the LS algorithm and the typical DL algorithm. (**b**) BER comparison between the LS algorithm and the proposed DL algorithm. (**c**) BER comparison between the MMSE algorithm and the typical DL algorithm. (**d**) BER comparison between the MMSE algorithm and the proposed DL algorithm. (**e**) BER comparison between the typical DL algorithm and the proposed DL algorithm. Figure 9: CDF of different algorithm structures.
## 4 Research Challenges and Directions
### Model Training, Knowledge Transfer, and Heterogeneous Environment  
Existing multi-agent actor-critic frameworks have adopted centralized training and decentralized or distributed execution. However, considering the limited communication range of GUs and UAVs, distributed or federated training still requires further studies. An intelligent agent can use an attention mechanism to obtain the key features in multi-agent scenarios via a cross-attention mechanism in the form of query, key, and value by taking only partial observation-action as input. The hierarchical space air-ground integrated networks introduce multiple links and decision-makers as intelligent agents at each layer [59]. Hence, a heterogeneous collaborative-competitive environment appears, which requires special neural networks for each agent according to its resource capacity to make the algorithm more robust and practical. Here, the potential for exploring transfer learning and knowledge distillation approaches from higher-capacity devices to lower-capacity devices is significant to enhance the overall system's adaptability and efficiency [60; 61]. The environment should be a high-fidelity environment for training, which can be achieved by employing digital twin-assisted training [12] and introducing noise not only in action space but also in state space to introduce uncertainties.
## 2 Materials and Methods
### Yeasts and LAB Isolation  
To obtain microbial isolates, 10 g of each sample was collected and mixed with 90 mL of physiological solution (9 g/L of NaCl) in a Stomacher bag and homogenized for 2 min. From the homogenate samples, serial dilutions from 10\({}^{-1}\) to 10\({}^{7}\) were made and appropriate dilutions were plated in triplicate on different agar media. Specifically, Yeast Peptone Dextrose Agar (YPDA; 10 g/L yeasts extract, 10 g/L peptone, 20 g/L dextrose, and 20 g/L agar) supplemented with 0.1 g/L of chloramphenicol (Sigma-Aldrich, Milan, Italy) was used for yeasts isolation. Plates were incubated at 27 \({}^{\circ}\)C for 2 days. LAB isolation was made on de Man Rogosa Sharpe (MRS agar; Oxoid, Milan, Italy) and Sourdough Bacteria Agar (SDB; 6 g/L trypticase, 3 g/L yeast extract, 20 g/L maltose, 3 mL of 10% Tween 80, 1.5 % of fresh yeast extract, 15 g/L agar), both supplemented with 0.1 g/L cycloheximide (Sigma-Aldrich). Plates were incubated in jars with the Anaerobic system (Oxoid) at 30 \({}^{\circ}\)C for 3 days. After the colonies were counted on the plates of each medium, between 3 and 10 colonies were randomly selected at the highest dilutions and purified by streaking on the correspondent isolation medium. Basic phenotypic tests, such as Gram staining and catalases, were done for bacteria. Cell morphology of the isolates was observed using a Zeiss Axiolab microscope (Carl Zeiss Ltd., Cambridge, UK). After the molecular characterizations, yeasts and bacteria strains were preserved in the Unimore Microbial Culture Collection (UMCC) in accordance with standard procedures [24]. A seed lot of the strains was stored at \(-80\)\({}^{\circ}\)C, in cryovials containing an aliquot of each culture, grown in liquid medium, and 25% (_v/v_) glycerol ([PERSON], Milan, Italy).  
Figure 1: Artisanal Panettone production using two different sourdoughs (MA and MB). The samples highlighted were collected concurrently at the same stages for their investigation.