esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
## 4 Discussion  
Correct diagnostics requires deeper understanding of the radiological features visible in chest X-rays. Unfortunately, deep neural networks are known for providing no explanation as to how the final decision is made. To make the decision support process more useful, the deep network should provide also explanations beyond plain decisions [65]. Such explanations can take the form of semantic segmentation with explanations in natural language assigned to each identified segment of a chest X-ray photo. Correct implementation of such tasks may require the use of additional eHealth data from the patients, and high-quality annotated datasets.  
Another limitation is introduced by the scarcity of image data representing all types of pneumonia pathologies, which prevents for achieving a higher accuracy or using a deeper network with more parameters. Successful deep learning models such as AlexNet, GoogLeNet and ResNet, have been trained on more than a million images, which are hardly available in the medical domain. Training deep neural networks with limited data available also may lead to over-fitting and prevent from good generalization.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Epoch** & **Recall (\%)** & **Precision (\%)** & **AUC (\%)** & **Test Accuracy (\%)** \\ \hline AlexNet & 200 & 98.97 & 90.21 & 97.83 & 92.86 \\ DenseNet121 & 100 & 99.23 & 91.18 & 98.78 & 92.62 \\ InceptionV3 & 100 & 98.46 & 90.30 & 97.33 & 92.01 \\ GoogLeNet & 50 & 99.48 & 90.44 & 98.29 & 93.12 \\ ResNet18 & 200 & 99.48 & 91.58 & 99.36 & 94.23 \\ Ensemble & – & 99.62 & 93.28 & 99.34 & 96.39 \\ model & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative results for each model on the test set.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Specificity** & **Recall/Sensitivity (\%)** & **Precision (\%)** & **Area Under Curve (\%)** & **Test Accuracy (\%)** \\ \hline Chester [64] & – & – & – & 98.4 & – \\ [PERSON] et al. [62] & 90.1 & 93.2 & – & – & 92.8 \\ This paper & – & 99.62 & 93.28 & 99.34 & 96.39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative results for each model on test set.  
The accuracy results reached in this paper could be still improved by adding more sophisticated deep networks to the ensemble and training the networks with a larger dataset.  
Future research directions will include the exploration of image data augmentation techniques [66] to improve accuracy even more, while avoiding overfitting.
## 3 Result and Analysis
### Transverse Flow Characteristics  
This paper only shows the transverse flow characteristics of the representative sections of \(Y=20\) cm, 80 cm, 140 cm, 190 cm (see Figure 5).  
* Velocity distribution: In the FAA, the velocity \(v\) near the side wall was high, which showed that there were vortexes along the \(Y\) direction at the bottom of this area. The vortexes could wash away the sediment that had been deposited at the bottom of the tank and made it suspend in the flow, reducing the settling efficiency. Because the baffle blocked the upper flow movement, the velocity in the SSA was very slow, with a range of \(-\)0.002-0.002 m/s. It was only within \(Z=0\)-50 cm that the velocity was higher. In the ISA, the section of \(Y\) = 20 cm was far away from the overflow weir, so the suction effect was not obvious, and the velocity \(v\) was less than 0.002 m/s. However, with the decrease of the distance from the overflow weir, the velocity \(v\) increased. The direction of the velocity \(u\)-\(w\) was downward at the front part of the ISA, indicating a downward movement of the flow in the inclined tubes at the front part of the ISA, while it was upward at the latter part of the area, affected by the overflow suction. (see Figure 5a).
* Transverse dimensionless turbulence intensity \(I_{Y}\) distribution: The \(I_{Y}\) gradually increased from the right wall of the tank to the left (the overflow side) (see Figure 5b). The block effect of the baffle on the middle and upper part of the flow led to the \(I_{Y}\) in the middle and the upper part being higher than that in the lower part. At the same time, the \(I_{Y}\) in the ISA was also significantly higher than that in other areas, indicating that the velocity \(v\) of the flow fluctuated greatly and directly related to the suction of the overflow weir.
* Transverse dimensionless Reynold shear stress \(R_{XY}\) distribution: \(R_{XY}\) represents the Reynolds shear stress in the direction \(Y\) and normal to \(X\). The \(R_{XY}\) distribution of each section was similar, ranging from 0 to 4%. The maximum value of \(R_{XY}\) mainly appeared in the FAA and the front  
Figure 4: Distribution of longitudinal flow characteristics: (**a**) velocity (the contour plot represents the longitudinal velocity \(u\), and the vector represents velocity \(v\)-\(w\)); (**b**) dimensionless turbulence intensity \(I_{X}\); and (**c**) dimensionless Reynolds shear stress \(R_{XZ}\) (The unit of velocity is m/s, and the dimensionless turbulence intensity and dimensionless Reynolds shear stress is dimensionless).  
part of the SSA (behind the flow adjustment board) (see Figure 5c). The distribution of \(R_{XY}\) in the whole flow field of the tank was low, and its influence on the sedimentation was not obvious.
## 3 Instrument Control
### Back-End Configuration
#### 3.2.1 Power Measurements
The measured power value for each point of the spectrum is \[P_{\text{RMS}}(f_{p})=\frac{1}{N_{\text{P}}}\sum_{m=0}^{N_{\text{P}}-1}\left|s \left[m+n_{\text{SWP}}\,N_{\text{P}}\right]\right|^{2}, \tag{4}\]  
where \(n_{\text{SWP}}\in[0,1,\cdots,N_{\text{SWP}}-1]\) is the index of each sweep point, \(f_{p}=f_{c}-\frac{B_{r}}{2}+n_{\text{SWP}}\frac{B_{r}}{N_{\text{SWP}}-1}\) is the frequency of each point of the spectrum, \(s[m]\) is the linear digitized video voltage at the output of the Analog-to-Digital Converter (ADC) at the SA, and \(N_{\text{P}}\) is the number of values of the ADC per sweep point defined as  
\[N_{\text{P}}=F_{s}\frac{\text{SWP}}{N_{\text{SWP}}}=F_{s}\,T_{\text{SWP}}, \tag{5}\]  
where \(F_{s}\) is the sampling frequency of the ADC (32 MHz), and \(T_{\text{SWP}}\) is the sweep time per sweep point. The _number of averages_\(N_{\text{AVG}}\) is the number of sweeps or averaged spectra per measurement. The number of averages increases proportionally the integration time. If the spectrogram option is active, \(N_{\text{AVG}}\) determines the number of consecutive spectra. Besides, if \(N_{\text{AVG}}\) is set to either 0 or 1, only one sweep is performed. The averaged spectrum can be expressed as  
\[\overline{P_{\text{RMS}}}(f_{p})=\frac{1}{N_{\text{AVG}}}\sum_{i=0}^{N_{\text {AVG}}}P_{\text{RMS}_{i}}(f_{p}), \tag{6}\]  
whereas if the value of all spectrum points are averaged, the measured radiometric power over the acquisition band \(B_{r}\) can be defined as  
\[P_{m}=\frac{1}{N_{\text{SWP}}}\sum\overline{P_{\text{RMS}}}(f_{p}). \tag{7}\]  
Taking into account the parameters mentioned above and the resolution of a TPR ([1], pp. 359-366), the radiometric resolution of MERITXELL multiband radiometer per sweep point using the RMS detector, and several averaging points is  
\[\Delta P_{\text{RMS}}(f_{p})=\frac{\overline{P_{\text{RMS}}}(f_{p})}{\sqrt{ \text{RBW}\,T_{\text{SWP}}\,N_{\text{AVG}}}}=\frac{\overline{P_{\text{RMS}}}( f_{p})}{\sqrt{\text{RBW}\,\frac{\text{SWP}}{N_{\text{SWP}}\,N_{\text{AVG}}}}}. \tag{8}\]  
Eventually, the resolution may be increased when averaging all the points of the spectrum. However, since there may be an overlapping of the RBW from consecutive sweep points (\(N_{\text{SWP}}\geq N_{\text{IND}}\)), the overall radiometric resolution after averaging all sweep points is  
\[\Delta P_{m}=\frac{P_{m}}{\sqrt{N_{\text{IND}}\,N_{\text{AVG}}}}=\frac{P_{m}} {\sqrt{\frac{B_{r}}{\text{RBW}}\,N_{\text{AVG}}}}. \tag{9}\]
## 1 Introduction
rectifier, but there are other, more complex configurations that are worth mentioning. For instance, the authors of [13] present a full-wave rectenna, formed by two concentric squared patches attached to a full-wave rectifier. The results show an efficiency in the rectifier of less than 34% for an input power of 1 mW. Another passive (not externally biased) configuration arises from multiplier circuits. They are capable of rectifying, but also elevating, the output voltage from the input RF signal. This is of great interest for our application, since the input voltage is on the order of hundreds of mV. In these terms, the authors of [14] introduce a 2.45 GHz rectenna based on a slotted patch antenna and a single-stage Cockcroft-Walton multiplier. In this document, we present a radiofrequency energy harvesting system based on a ultrawideband Archimedean spiral antenna and a half-wave Cockcroft-Walton (HWCW) multiplier. The main aim of this work is to present a deep study of a complete RF harvesting system, paying attention to each individual block and to the entire system, as well as those elements, parameters and parasitic effects that are of importance in its design. The document is consequently organized as follows: Section 1 introduces the topic and reviews recent advances on the matter. Section 2 gives an overview of the system and the different stages that comprise it. Section 3 focuses on the design and manufacture of the Archimedean spiral antenna. Section 4 presents the matching and the conditioning circuits. Section 5 is reserved to test the operation of the complete harvesting system. Finally, conclusions are drawn in Section 6.
## 3 Polyphenols Effects on VOCs Release
### Effects on Oxygen Heterocycles (Furans/Lactones)  
Furans and lactones are VOCs normally related to wine aging. Furans in wines are generated by the thermal degradation of sugars due to acid-catalyzed reactions, or even through Maillard reaction. Lactones are essentially formed by yeasts during alcoholic fermentation, although significant odorant lactones are usually accumulated during wine aging. They can impart powerful nuances to wines, especially in oxidative conditions [79].  
Oxygenated heterocycles reported in Table 1 have different functionalities such as ketonic, aldehyde, or alcoholic and range from very polar compounds such as sotolon (4,5-dimethyl-3-hydroxy-2,5-dihydrofuran-2-one) with log\(P\)= \(-\)0.29, to the more hydrophobic cis-whiskey lactone with log\(P\) = 2.63. Data suggest that at increasing tannins concentrations, VOCs with log\(P\) values higher than 1 show a decrease in volatility. More specifically, the retention effect of the real wine matrices was higher for the oak-barrel aged one compared to the young-red one for \(\gamma\)-nonalactone (log\(P\) = 1.94), trans-whiskey lactone (log\(P\) = 1.97), and cis-whiskey lactone (log\(P\) = 2.63). On the contrary, VOCs with lower log\(P\) values (i.e., 5-methyl furfural with log\(P\) = 0.67), independently from the matrix type, have shown a \"salting-out\" effect [62]. Furthermore, GC-O data have shown that the most hydrophilic and polar VOCs, sotolon (log\(P\)= \(-\)0.29), furanued (log\(P\)= \(-\)0.08), and ethyl furaned (log\(P\)= 0.43), even if not instrumentally detected by GC-MS, were characterized by higher GC-O scores in the presence of a red wine non-volatile extract compared to a white one [67]. These results could be linked to the very low detection thresholds characterizing these furans, all in the order of \(\mu\)g/L, with sotolon having the lowest (1-6 \(\mu\)g/L).  
A sensory implication of these observations could be that the perception of these molecules involved in oxidative notes of wines could be favored in the presence of PPhs. On the contrary, coconut/woody-spicy/sweety odors due to lactones could be less perceivable at increasing concentrations of PPhs. However, there are no scientific data supporting this hypothesis, since no works have been conducted on the hypothetical sensory effects of polyphenols on these VOCs, which could be an interesting aspect to consider for future research.
###### Abstract  
In recent years, the adoption of industrial wireless sensor and actuator networks (IWSANs) has greatly increased. However, the time-critical performance of IWSANs is considerably affected by external sources of interference. In particular, when an IEEE 802.11 network is coexisting in the same environment, a significant drop in communication reliability is observed. This, in turn, represents one of the main challenges for a wide-scale adoption of IWSAN. Interference classification through spectrum sensing is a possible step towards interference mitigation, but the long sampling window required by many of the approaches in the literature undermines their run-time applicability in time-slotted channel hopping (TSCH)-based IWSAN. Aiming at minimizing both the sensing time and the memory footprint of the collected samples, a centralized interference classifier based on support vector machines (SVMs) is introduced in this article. The proposed mechanism, tested with sample traces collected in industrial scenarios, enables the classification of interference from IEEE 802.11 networks and microwave ovens, while ensuring high classification accuracy with a sensing duration below 300 ms. In addition, the obtained results show that the fast classification together with a contained sampling frequency ensure the suitability of the method for TSCH-based IWSAN.  
industrial wireless sensor and actuator networks; support vector machine; interference classification; spectrum-sensing; wireless LAN; microwave oven +
Footnote †: journal: Sensors. Actuator Netw. **2017**, \(6\), 9; doi:10.3390/jsan6020009
## 1 Introduction
The proposed concept does not follow the two aforementioned solutions; by contrast, nodes can leverage the information of each other nodes. Thus, the neighbor unknown to some nodes can be connected/communicated with assistance from other nodes, such that fewer active slots are possible, and reduced reachability can save node energy. Some studies have regarded rendezvous maintenance as a rediscovery problem, despite the system being scheduled for active slots. To address these two concerns, [PERSON] _et al._ proposed an extended quorum system for indirect neighborhood information to bridge the multiple pairwise communications, which can avoid the otherwise required full-mesh pairwise connection [11]. They proposed a quorum graph to prove the reachability of the neighborhood information flowing among nodes, and characterized the reduced quorum algorithm to eliminate redundant paths of active slots. However, some drawbacks remain in their methods, including a lack of mobile flexibility and power insufficiency:  
* The method proposed by [PERSON] _et al._, cannot be applied to mobile dynamic environments; that is, the schedule of a system must be recomputed repeatedly when new devices move around the environment. * Because redundant reachability leads to energy waste, the network lifetime must be referred to as an additional consideration; however, this concept was not included in the [PERSON] _et al._, method. Thus, a novel method must start from a minimal-delay scheduling solution and subsequently performs energy reduction by applying the power and frequency scaling technique [12]. Especially in the mobile sensor network environment, the limitation of the embedded devices is on their pair reachability and the power maintenances. These are the temporal and spatial considerations in the moving device environment. The reachability ability concerns the spatial usages, and the power usage controls the temporal issues. Hence, to address the quorum reachability problem and to improve the two aforementioned drawbacks in the method proposed by [PERSON] _et al._, we designed a heuristic weighted quorum system (WQS) method. This method not only reduces the redundant pairwise reachability between discovered and undiscovered neighbors but also supports the node rendezvous maintenance with a decreased number of active slots to save system energy and eliminate redundant slots. The remainder of the paper is organized as follows: Section 2 presents the theoretical concepts for quorum graphs and an extended quorum system. Section 3 formalizes the quorum reachability minimization problem, followed by an evaluation of the proposed WQS algorithm in Sections 4 and 5 discusses related works and concludes the paper.
## 4 Simulation and Analysis
### Application Discussion  
In this paper, we propose two improved VLL design methods: an HD-VLL based on the loop filter and an HD-VLL-NCO based on NCO. Through a series of simulation experiments, we verified that these two improved VLLs exhibit significant advantages in terms of dynamic adaptability compared to traditional VLL loops. In practical applications, when hardware implementation requires support for multi-channel time division multiplexing, HD-VLL-NCO provides a feasible solution. By setting the bit width and parameters appropriately, the HD-VLL-NCO can support time division multiplexing for loop filter implementation while maintaining signal tracking accuracy, thereby reducing the complexity and cost of hardware implementation. Depending on the specific application requirements, we can reasonably select either of these two loops, as they can both significantly enhance the tracking performance of DS/FH hybrid spread spectrum signals in high dynamic environments.
## 5 Annotation-Free Transcription Alignment  
The proposed method for handwritten transcription alignment without human annotation is based on two components, a character detection system (see Section 4) and a transcription alignment algorithm (introduced below).  
Figure 2(a) provides an overview of the method. First, the Nom font is used to generate simple synthetic pages without a particular parchment background. They are used to train an initial _synthetic_ character detection system, which is then applied to the handwriting dataset (see Section 3). Afterwards, the detection result is aligned with the existing transcription using unsupervised clustering techniques. Finally, without human correction, the resulting annotations are used to train an _adapted_ character detection system, i.e., continuing to train the synthetic system with the self-annotated handwriting data. This last self-training step aims to perform a transfer from printed character detection to handwritten character detection using the alignment results.  
In the following, we provide more details on the synthetic document generation, introduce the alignment algorithm, and discuss the performance measures used for assessing the alignment quality.  
Figure 2: The model architecture of YOLO, where the backbone extracts features from an image, the neck enhances feature maps by combining different scales, and the head predicts the bounding boxes.  
Figure 3: Annotation-free transcription alignment.
## 2 Analysis of Ranging Consistency for ISL Payload  
### Ranging Principle of ISL Payload  
The dual-one-way measurement of an ISL consists of two satellites alternately performing a pseudo-code phase ranging process and the mutual transfer of the measurement results. It contains two one-way pseudo-range measurements.  
As shown in Figure 1,  
\[T_{B}=\Delta t+t_{A}+\tau(t_{1},t_{2})+r_{B}=t_{2}-t_{0}, \tag{1}\]  
where \(T_{B}\) is the measurement pseudo-range, \(\Delta t\) is the system clock difference between satellite \(A\) and satellite \(B\), \(t_{A}\) is the ISL payload transmitting circuit delay of satellite \(A\), \(r_{B}\) is the ISL payload receiving circuit delay of satellite \(B\), \(\tau(t_{1},t_{2})\) is the spatial delay, \(t_{1}\) is the transmitting point time of satellite \(A\), and \(t_{0}\) and \(t_{2}\) are the reception point time and the measurement point time of satellite \(B\), respectively. In the current study, we focus on the variation in the ranging delay, namely, \(t_{A}\) and \(r_{A}\) and \(t_{B}\) and \(r_{B}\), caused by the sensitivity of the ISL payload transmission and reception circuits to the ambient temperature in space. Next, we describe the composition of the ISL payload transmission and reception circuit.
# Power Control and Channel Allocation Algorithm for Energy Harvesting D2D Communications
## 4 Simulation Results and Discussion
The complexity of the proposed power control algorithm is lower than [13] and improves the total capacity of D2D users, while the complexity of the proposed power control \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Cell radius \(R/m\) & 500 \\ Maximum transmission power of cellular user \(P_{\text{max}}^{*}\)/dBm & 24 \\ Maximum transmission power of D2D user \(P_{\text{max}}^{d}\)/dBm & 24 \\ Transmission power of BS \(P_{BS}\)/dBm & 46 \\ Noise power \(N_{0}\)/dBm & \(-174\) \\ Number of cellular users \(M\) & 5 \\ Path-loss constant \(\kappa\) & 0.01 \\ Path-loss exponent \(\alpha\) & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 3: Total capacity of D2D users with different distances between the D2D pair. algorithm is higher than ETA method, but the total capacity of D2D users increases by 19%. Therefore, the system performance of the proposed power control algorithm is better than the algorithm in [13] and the ETA method. Figure 5 illustrates the relationship between the total capacity of D2D users and the number of cellular users. In the simulation, it is assumed that there are 10 D2D users in the cell, and the distance between the D2D pair is 10 m. It can be discovered that as the number of cellular users increases, the total capacity of D2D users increases. This is because the number of cellular users increases meaning that the number of reusable channels increases, which leads to the number of D2D users in some clusters decreasing so that the mutual interference between D2D users in a same cluster decreases, and the total capacity of the D2D user is increased.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 2 Description of the Study Areas  
The study aims to test the use of satellite data for flood mapping. The first thing that we have considered is the main flood characteristics: The extent of the flooded areas, the length of the river segment examined and the temporal evolution of the flood. This information drives the choice of the available satellite data and data processing to be used.  
The three selected cases studies, located in the Ebro River Valley and Arahal in Spain and the Po River basin in Italy (Figure 1), are representative of different situations concerning remote sensing images, ancillary data, geomorphology and land use.  
The cases of the Ebro valley and Po basin floods are representative of rivers with a wide floodplain in which the flood peak requires from many hours to several days to transit the river length, and the inundated area can reach several kilometers in width. This type of flood is more prone to be mapped by satellites because of its spatial and temporal scale. In addition, the availability of ancillary or field data is higher for the main river. The case of Arahal is representative of flash-flood processes common in semi-arid and hilly environments that have rapid temporal evolution and limited flood area extent. In this case, mapping with satellite data is less straightforward.