esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The work presented in this paper further investigates the application of deep learning methods over a collected database with more than 94,000 OCT images of murine (rat) colon polyps to study the discrimination capacity of this imaging technique for its future adoption as a real-time optical biopsy method. The aim of this proposal is to contribute to setting the bases for the automatic analysis of images with latest state-of-the-art techniques that could lead to the development of new computer-aided diagnosis (CADx) applications. Once image analysis methods demonstrate this capacity, colon polyp diagnosis with OCT can be progressively mastered by clinicians and the adoption of the technology naturally accomplished. With this aim, this work implements a classification (benign vs. malignant) approach based on an Xception deep learning model that is trained and tested over a large dataset of OCT images from murine (rat) samples that have been collected for this purpose. We propose a pre-processing method for data augmentation and to validate the application of deep learning methods for colon polyp classification as benign or malignant. In addition, to further investigate the diagnosis capacity of the proposed approach, evaluation is performed twice, once over individual B-scan images and then also over C-scan volumes for comparison. Finally, a strategy to maximize results when evaluating individual B-scans is applied. In comparison with previous studies [46], this work proposes a general diagnosis strategy based on classification instead of pattern recognition, which avoids time consuming manual annotation of the database providing automatic identification of the characteristics representing polyps tissue type. The classification strategy model can generalize better upon new polyp categories than the segmentation strategy, the performance of which is biased by the available annotations of the database. A classification strategy can help in the identification of subtle characteristics present on noisy OCT images that are not easily distinguished by the naked eye, and with proper visualization of them, can help clinicians to better understand the OCT imaging technique. In the future, the combination of both approaches could be considered for maximizing automatic diagnosis results.
## 2 Structure and Operation Principle of the Proposed Converter
### Operation of the Proposed Converter in DCM  
Mode 1: [0 to t0]. In this mode, both MOSFETs, Sw1 and Sw2, are turned on at the same time. The diodes D1, D2, D5, and D6 are in an on state while D3, D4, and D7 remain off. L1 starts charging energy from the input source, and L2 charges energy from the input source through D1; both inductors have the same current. C1 releases energy through D2 and Sw1. Capacitors C2 and C3 store power and act as a dual source, which is used to charge L3 through Sw2. At the same time, L4 charges from C2 and C3 through D5; both inductors have the same current. C4 releases energy through D6, and C5 supplies energy to the load. The proposed converter for this mode is shown in Figure 2b. The voltage and current equations for the different components during Mode 1 are as follows, where (lin) is the input current.  
\[\left.\begin{array}{l}V_{L_{1}}=Vg\\ V_{L_{2}}=Vg\\ V_{L_{3}}=Vc_{2}+Vc_{3}\\ V_{L_{4}}=Vc_{4}\\ Vc_{4}=Vc_{2}+Vc_{3}\\ Vc_{5}=V_{o}\end{array}\right\} \tag{1}\]  
\[\left.\begin{array}{l}lin=iL_{1}+iL_{2}+Ic_{1}\\ I_{SW1}=lin+Ic_{1}\\ I_{D1}=lin-iL_{1}\\ I_{D2}=Ic_{1}+iL_{2}\end{array}\right\} \tag{2}\]  
\[\left.\begin{array}{l}Ic_{4}=Ic_{2}\\ I_{SW2}=Ic_{3}=Ic_{2}\\ I_{D6}=Ic_{4},\\ I_{D5}=Ic_{2}\end{array}\right\} \tag{3}\]Figure 1: (a) The proposed converter integrated with PV Panels with applications. (b) The proposed DC-DC converter.  
Figure 2: **(a)** The proposed DC–DC converter; **(b)** Mode 1 DCM, Mode 1 CCM; **(c)** Mode 2 DCM, Mode 2 CCM; **(d)** Mode 3 DCM.  
Mode 3: [t1-t2]: Both MOSFETs stay turned off, and diodes D1, D2, D5, and D6 also remain off. The energy stored in inductors L1 and L2 continues to discharge and charge capacitors C2 and C3. The energy in inductors L3 and L4 decreases to zero at time (D2). Moreover, capacitor C5 continues to provide power to the load. The configuration of the converter in this mode is shown in Figure 2d. The voltage and current equations remain unchanged from the previous mode. Figure 3a illustrates that inductor currents L1 and L2 operate in CCM, while L3 and L4 operate in DCM when the proposed converter operates in DCM mode. The current equation remains the same as in the previous mode.
# Transient Responses of Repeated Transverse Impacts on Beams
## 5 Results and Discussion
### Transient Impact Behaviors  
#### 5.3.1 Contact Force and Energy Transformation  
As shown in Table 3, 91 repeated impacts at the center of the beam have been conducted. By combining the eigenfunction expansion method and spring-mass method with Thornton local contact model, the theoretical contact force and contact force-indentation relations of the 1 st (i.e., 50 mm release height) and the 91 st (360 mm release height) impacts are shown in Figures 10 and 11, respectively. The impact impulse \(P\), contact work \(W\), kinetic energy loss of the sphere \(\Delta E\), energy absorption of the beam \(Ua\) for the 1 st and the 91 st impacts are listed in Table 5.  
Comparing the results shown in Figures 10 and 11 and Table 5, the differences between the eigenfunction expansion method and spring-mass method are significant. Spring-mass method predicts the larger contact force, indentation, permanent indentation, impulse and contact work, but the smaller impact duration and energy absorption of the beam. For spring-mass method, due to the neglecting of wave propagation energy, less energy is transferred to structural vibrations and more energy is transferred to local contact zone, which results in higher contact force and shorter impact duration.  
Figure 10: Contact force responses by eigenfunction expansion method and spring-mass method (with Thornton contact model): (**a**) the 1 st impact, \(V_{01}=0.990\) m/s; (**b**) the 91 st impact, \(V_{091}=2.656\) m/s.  
Figure 9: Stress responses of the 91 st impact (\(H=360\) mm): (**a**) Experimental results (Solid line) and eigenfunction method results (Scatter symbols) with KE model; (**b**) Experimental results (Solid line) and eigenfunction method results (Scatter symbols) with KK model.  
impact shown in Figure 10a, the predicted contact force and impact duration of spring-mass method are 13.30% higher and 4.12% shorter relative to eigenfunction method, respectively. For the results of 91 st impact shown in Figure 10b, the predicted contact force and impact duration of spring-mass method model are 53.30% higher and 8.99% shorter relative to eigenfunction method, respectively. Therefore, higher modes of beam vibration should be taken into account to achieve a better estimation of the contact force and impact duration.
### Related Work  
Loosely coupled visual-inertial odometry methods are usually constructed based on filter frameworks such as the EKF [14; 15]. High-confidence visual keyframe measurements are then periodically utilized to correct the high-frequency integration results from IMUs. This basic structure effectively balances the updating rate and accuracy.  
[PERSON] and [PERSON] [16] proposed a data fusion algorithm for the self-calibration of a monocular visual-inertial system with proven observability. With a similar filtering structure, [PERSON] and [PERSON] [17] incorporated the world coordinate system drift in visual measurements, as well as the spatial relationships between the IMU and the monocular camera, into the EKF framework. This approach enables failure detection and scale-drift estimation by fusing of measurement data on the position and attitude layers.  
[PERSON] and [PERSON] [18] constructed a filter-based framework to recover the relative configuration of two drones performing IMUs and monocular visual measurements. To address error amplification during the error propagation process in EKFs due to possible inaccurate state modeling, [PERSON] et al. [19] proposed the visual-inertial invariant EKF algorithm, which is based on Lie algebra rules. Furthermore, researchers designed visual-inertial odometry that embraces the characteristics of novel filers such as the MEKF [20] and the equivariant filter [21; 22] to broaden the application of filter-based techniques in motion estimation.  
The aforementioned loosely coupled strategies have demonstrated remarkable performance when mainly focused on the fusion of positional layers. However, since the high-frequency update and the intrinsic drift of IMUs inevitably result in fast error accumulation, the effect of the final correction on the position and attitude layers will be suboptimal when the visual measurement update frequency drops or accuracy deteriorates. Furthermore, these methods are thus incapable of adjusting the emphasis on either source while encountering external visual interference.
## 3 Results and Discussion
### Short-, Mid-, and Long-Term Reproducibility  
Short-, mid-, and long-term reproducibility were investigated, and their values are summarized in Table 1. The proton beam dose-rate (16 Gy/min), dose (4 Gy), energy (60 MeV) at the irradiation point (i.e., at the detector position), and a circular beam spot 25 mm in diameter were taken as constants for all the measures.  
Figure 4: Detector response as a function of the released dose in water. The linear fit shows an excellent confidence degree expressed by the R2 parameter.  
Figure 5: Percentage variations, with respect to the value measured at the maximum dose rate (39 Gy/min) of the collected charge for growing values of beam dose—rate.  
The short-term reproducibility was investigated, evaluating the detector response in ten repeated irradiations of 4 Gy, then repeated five times within one hour. For each set of the ten measures, the averaged detector sensitivity, expressed in nC/Gy--i.e., as the ratio between measured charge and the corespondent released dose--was calculated. The maximum observed variation in the sensitivity was found to be less than 2% during the whole hour.  
Similarly, the mid-term reproducibility was evaluated estimating the detector sensitivity ten times in five consecutive days. The maximum percentage difference observed was about 2.3%. For the long-term reproducibility, the sensitivity was measured ten times within 17 months, using the same irradiation conditions. In this case, the maximum percentage difference was 4.3%.
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 3 Proposed Approach: Stpa-Rl
### Training with Reinforcement Learning
#### 3.2.2 Rewarding Safe Actions Based on State Conditions  
Based on the context and state conditions, rewards are assigned when safe actions are performed. This encourages RL to favor safe actions and maximize rewards. Given the current state \(s\), the actor network outputs a probability distribution over actions \(a\), denoted as \(\pi(a|s,\theta_{actor})\). When a safe action \(a_{safe}\) is selected from the distribution, a reward \(R_{safe}\) is provided to encourage the learning of safe actions, and it is expressed as  
\[R_{safe}(s)=\pi(a_{safe}|s,\theta_{actor})\]  
The reward-based learning mechanism of STPA-RL plays a crucial role in teaching agents about safe behavior within the system. By balancing rewards that reflect the system's safety priorities, it motivates agents to explore a range of safe behaviors without leaning towards conservatism or risky actions.
# Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles
## 2 Background and Related Work
### Cybersecurity Hazards of In-Vehicle Ethernet
#### 2.2.2 Cryptographic Solutions
Consequently, there is an imperative to develop in-vehicle Ethernet networks tailored to meet the demanding requirements of speed and real-time performance, thus enhancing the safety of self-driving cars.
###### Abstract  
In order to satisfy the requirement of high-rate high-precision applications, 1 Hz BeiDou Navigation Satellite System (BDS) satellite clock corrections are generated based on precise orbit products, and the quality of the generated clock products is assessed by comparing with those from the other analysis centers. The comparisons show that the root mean square (RMS) of clock errors of geostationary Earth orbits (GEO) is about 0.63 ns, whereas those of inclined geosynchronous orbits (IGSO) and medium Earth orbits (MEO) are about 0.2-0.3 ns and 0.1 ns, respectively. Then, the 1 Hz clock products are used for BDS precise point positioning (PPP) to retrieve seismic displacements of the 2015 Mw 7.8 Gorkha, Nepal, earthquake. The derived seismic displacements from BDS PPP are consistent with those from the Global Positioning System (GPS) PPP, with RMS of 0.29, 0.38, and 1.08 cm in east, north, and vertical components, respectively. In addition, the BDS PPP solutions with different clock intervals of 1 s, 5 s, 30 s, and 300 s are processed and compared with each other. The results demonstrate that PPP with 300 s clock intervals is the worst and that with 1 s clock interval is the best. For the scenario of 5 s clock intervals, the precision of PPP solutions is almost the same to 1 s results. Considering the time consumption of clock estimates, we suggest that 5 s clock interval is competent for high-rate BDS solutions.  
BDS; precise point positioning; seismic displacements; Nepal earthquake; satellite clock corrections +
Footnote †: journal: Sensors
# Nosemosis in Honeybees: A Review Guide on Biology and Diagnostic Methods
## 1 Introduction  
Nosemosis (_Nosema_ spp. infection) is a dangerous, widespread disease in honey bees (_Apis mellifera_) around the world. Nowadays, we distinguish two significant pathogenic microsporidia species for the honey bee: _Nosema apis_ and _Nosema cernane_. It is worth mentioning that in 2017, in Uganda, a third species, _Nosema neumanii_[1], was discovered, but so far there is no data about the pathogenicity of this species. Additionally, its presence outside of Uganda has not yet been confirmed. _N. cernane_ infection is still a challenge for scientists as it weakens colonies, often leading to their death. Moreover, the infection spreads very fast in new regions, possibly displacing _N. apis_ infections [2; 3; 4]. There are reports of high colony mortality caused by both _N. apis_ and _N. cernane_[5; 6; 7]. Recent health status monitoring studies in European aparies [8; 9; 10] show that _N. cernane_ definitely occurs more often than _N. apis_, but mixed infections are also present. There are many methodologies used in the diagnosis and study of _Nosema_ infections, including both qualitative and quantitative approaches. This review guide may be a useful tool for researchers and veterinarians as it presents the most important basic information on _Nosema_ biology, the most commonly used methods in diagnostic and research areas, and potential uses for these methods. We also present the advantages and disadvantages of these methods, which have been developed mostly in Europe, where nosemosis is widely prevalent [2]. Although the number of studies on nosemosis is constantly growing, and the methods of identification for each species are still being improved to make results faster, easier to obtain, and more accurate, it is sometimes difficult to decide which method is best for a particular type of research. This review aims to present the most useful methods in a concise and systematic way in order to make the choice easier and more informed by adding crucial information about the biology and pathogenicity of this Microsporidium.
# Design of a System for Verification of Automatic Guided Vehicle Routes Using Computer Emulation
## 3 Results
### Creating a Communication Interface via the DPS Bridge App  
Based on the approach described in Section 2.2 to ensure communication between the real elements of the system, its own DPS Bridge application was created. The basis of the created application is the programming language C. The DPS Bridge application bridges the connection between the Tecnomatix Plant Simulation and the SQL database systems, which are filled with real-world data through online automatic data collection from the RTLS system. The DPS Bridge is able to connect to the database from which it reads and subsequently processes data. The processed data are sent back via the virtual Component Object Model (COM) interface to the Plant Simulation software environment. COM objects allow for control simulation from other applications that are able to process COM objects.  
The application was created on the Windows desktop platform. The functionality of the DPS Bridge after running the application reviews the channels providing inter-process communication between the server and one or more clients. These channels provide complete duplex communication in the retina, based on client messages and impersonation, allowing connected processes to use their own set of permissions on remote servers. When browsing, the application detects whether a channel with a DPS Bridge server is available. Based on this, there may be two states of application startup:  
1. Server mode--is running if a channel with a DPS Bridge server is not available. After establishing communication, the application triggers a thread through which it is waiting for a request from the client. If a message called \"START_AGV\" is received through the thread server, the server runs the AGV by writing a value of 1 in its internal database called \"Decide_parameters\". After the value is entered into the internal database, the AGV is set in motion. Verification that the AGV is in motion is done by reading from the internal AGV database, into which the values are written after passing the RFID tag. After going through the tag, this information is sent to the simulation model created in Plant Simulation. After the client sends a request or the plant simulation contact with the server is stopped, the application is disabled.
2. Client mode--in this mode, the application can communicate with the DPS Bridge server. The mode is necessary to identify status information from the AGV. At the same time, DPS Bridge is running in the background, so the server is available. That was created in casee the application was launched via Server mode and, at the same time, Client mode was running in the background (see Figure 12). The DPS Bridge connects directly to the server via communication channels, eliminating the need to recreate the communication channels via which messages are delivered between the application and the server.
# Design of Quadruple Offset Butterfly Valve Used in Power Plants
## 3 Finite Element Analysis
### Discussion  
At present, the QOBV is being developed, and its experiments are being conducted confidentially, making it difficult to carry out sealing tests. Consequently, the most effective approach is to use a simulation program to estimate the sealing performance. The authors compared the contact pressure results of the QOBV with those of the TOBV and verified the reduction in contact pressure of the QOBV using the ANSYS simulation program. In the previous study by [PERSON] (reference [18]), contact stresses of a 2D model of the triple offset butterfly valve (TOBV) were derived under symmetry conditions, specifically only at the minimum and maximum slope angles of the seal contact surface (66.1\({}^{\circ}\) with 97.15 MPa and 86.1\({}^{\circ}\) with 66.81 MPa), as illustrated in Figure 24. This study conducted a 3D analysis of the TOBV and computed contact stresses for slope angles ranging from 66.1\({}^{\circ}\) to 86.1\({}^{\circ}\). The 3D simulation provides more realistic results than the symmetrical 2D simulation, thereby improving accuracy. Therefore, comparing our findings with those of [PERSON]'s previous work would be inappropriate. We evaluated the contact stress of the TOBV through 3D simulation and contrasted it with that of the quadruple offset butterfly valve (QOBV) in chapter 3.1.4. The maximum contact pressure of the TOBV was 148.9 MPa, whereas that of the QOBV was 93.47 MPa at an 86.1\({}^{\circ}\) angle. This represents a 55.431 MPa (37.2%) reduction in the maximum contact pressure.
## 4 DRFM Performance Results
### Effects of DRFM ADC Resolution  
One of the principle advances in EW receivers over the last 30 years has been the increasing speed and resolution of analogue-to-digital converters (ADC). The initial DRFM systems often relied upon mono-bit ADCs, capable of high speeds but only a single bit, which made them very sensitive to the threshold detection level. Modern ADCs are capable of much more, with most contemporary EW systems operating with ADCs sampling at giga-samples per second and in excess of 10-bits of resolution.  
To understand the effects of the ADC resolution on DRFM performance, it was varied across a number of simulations, with the system implemented at 1, 2, 4, and 6 bits of resolution. In this case, only the I-Q DRFM was evaluated, with the system presenting surprisingly  
The results for the LFM and PSK waveforms are summarized in Table 3, comparing side-lobe levels for the various ADC resolutions. However, the major impact of the change in ADC resolution across the waveforms was the increased noise floor and corresponding reduction in dynamic range. The impact on dynamic range is not surprising as the number of bits in an ADC has a direct impact on the dynamic range in any receiver.  
For the Barker code, the single bit system is well suited to recording the PSK waveform as it is essentially making a detection decision with each bit of either a \"1\" or a \"0\" present in both I/Q channels. Similarly, the LFM waveform is operating at sufficient speed that the change in phase is recorded. Both figures show this effect breaks down as the signal in the side lobes, as the low ADC resolution values have insufficient dynamic range to replicate these characteristics of the matched filter.  
The lack of straddle loss in the model, discussed in Section 4.2, is another contributing factor in Figures 13 and 14. Without the straddle loss at the DRFM receiver and matched filter, the two signals maintain the same set of original samples with distortion, not two asynchronous set of samples.