esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer
## 3 Results
### Region-Wise Performance  
By inputting the tumor and nerve masks into the segmentation models, we extracted PNIs based on the distance between tumors and nerves (Figure 2). The pipeline using the multiple segmentation model, Md5, exhibited excellent performance, with an AUC of 0.92 (Table 3, Figure 3). Moreover, the standard deviation was the lowest among the models,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Accuracy** & **IoU** & **Sensitivity** & **Precision** & **F1-Score** \\ \hline \multirow{4}{*}{Nerve} & U-Net a  & 0.987 & 0.887 & 0.943 & 0.937 & 0.940 \\ \cline{2-7}  & DeepLabv3+ a  & 0.985 & 0.837 & 0.892 & 0.931 & 0.911 \\ \cline{2-7}  & U-Net (m) b  & 0.893 & 0.801 & 0.867 & 0.924 & 0.891 \\ \cline{2-7}  & SegFormer (m) b  & 0.921 & 0.829 & 0.921 & 0.893 & 0.907 \\ \hline \multirow{4}{*}{Tumor} & U-Net a  & 0.900 & 0.676 & 0.887 & 0.740 & 0.805 \\ \cline{2-7}  & DeepLabv3+ a  & 0.922 & 0.769 & 0.903 & 0.839 & 0.869 \\ \cline{1-1} \cline{2-7}  & U-Net (m) b  & 0.893 & 0.611 & 0.856 & 0.681 & 0.757 \\ \cline{1-1} \cline{2-7}  & SegFormer (m) b  & 0.838 & 0.686 & 0.838 & 0.791 & 0.814 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the segmentation models.  
indicating that the multiple segmentation model was more stable than the combined binary segmentation model.  
We found that the pipeline using the multiclass segmentation network showed better performance, although the binary segmentation network outperformed the simple multiclass segmentation model regarding pixel-wise performance for tumors and nerves. The combined networks are assumed to cause error accumulation and degrade performance.
## 2 Wireless Body Area Network
### WBAN System Architectures  
The role of a fully functioning WBAN is to manage the communication of a set of heterogeneous nodes placed on or inside the human body. Implanted nodes may operate at 400 MHz in the case of Medical Implantable Communication Service (MICS), whilst wearable nodes operate within the Industrial Scientific Medical (ISM) band at 2.4 GHz. Due to the low power and short range limitations of sensor devices, a \"sink\" or BCU in the form of a mobile phone or bespoke portable device would receive the sensor output communications from the various nodes and perform any necessary local processing. The BCU would then act as a local gateway for further communications over, e.g., Wi-Fi or General Packet Radio Service (GPRS) to a remote server for further processing and monitoring. It is assumed that a BCU would have greater energy resource than a sensor node.  
Figure 3 shows a typical BAN architecture where the personal device (mobile phone in this example) acts as the BCU to manage the data communications to the secure medical server via Wi-Fi, GPRS and the Internet. System designers must also cater for health practitioners initiating requests for key data from a specific sensor or a sensor group in priority to the normal steady state life-signs. This emergency data request could be in response to a specific health condition where a second sensor data reading is required as a priority to gain an overall picture of a potential condition. These alarm notifications would be triggered when a specific change in physiological readings is detected and highlights the closely coupled nature of individual sensors within a WBAN.  
As WBAN technology becomes an integral component of healthcare management, the demands on the architecture are likely to be greater as the number and types of sensors increase along with data volumes. This means that architectures will need to cater for ever more demanding levels of energy efficiency and QoS requirements.  
Figure 3: Typical WBAN architecture.
## 2 Results and Discussion
### Discriminant Analysis (DA)  
Discriminant analysis was performed using a set of observations for which the classes are known. In this study, the predefined classes were 2 (authentic and unauthentic Harumanis mango) and were obtained from the CA. This set of observations is sometimes referred to as the training set. A Linear discriminant analysis (LDA) was used to set a linear function of variables. A confusion matrix was used as the validation method. As in a CA, 26 selected compounds were used to determine whether groups differ with regards to the mean of a variable, and to use that variable to predict group membership. A DA can also determine the variables that discriminate samples between clusters in the CA. The results of DA, as shown in Table 2, showed the classification matrix for discriminant analysis of VOCs in Harumanis, Tong Dam, and Susu mangoes, using a standard DA mode that gave a 100% correct classification of the mangoes according to the varieties. The DA showed that each group differed from the others in terms of different compositions, as no confusion matrix occurs on the data set [19].
## 2 Radiative Transfer  
Let, for now, the model atmosphere consist of a plane parallel, vertically heterogeneous, absorbing atmosphere, a surface with Lambertian reflectance, and an optically thin scattering layer of infinitesimal geometrical thickness (Figure 1). Light hitting the scattering layer may either be transmitted without interaction, absorbed, or isotropically scattered into the upper or lower hemisphere (or half-space). In the following, we derive an equation for the satellite measured radiance \(I\) for a plane parallel geometry; in Section 2.11, we adapt our results for a pseudo spherical geometry.  
We separate the radiance reaching the satellite instrument in the components \(I_{C}\), \(I_{SD}\), \(I_{CD}\), \(I_{SI}\), \(I_{CI}\), and \(I_{SIF}\):  
\[I=I_{C}+I_{SD}+I_{CD}+I_{SI}+I_{CI}+I_{SIF} \tag{1}\]  
\(I_{C}\) is the radiance directly scattered from the scattering layer to the satellite. \(I_{SD}\) represents the radiance originating from the surface due to direct illumination of the surface and includes components due to multiple scattering of the Lambertian surface flux (\(I_{SD_{i}}\)). \(I_{CD}\) represents the radiance originating from the scattering layer due to direct illumination of the surface including components due to multiple scattering (\(I_{CD_{i}}\)). \(I_{SI}\) represents the radiance originating from the surface due to diffuse illumination of the surface including components due to multiple scattering (\(I_{SI_{i}}\)). \(I_{CI}\) represents the radiance originating from the scattering layer due to diffuse illumination of the surface including components due to multiple scattering (\(I_{CI_{i}}\)). \(I_{SIF}\) is the radiance originating from solar induced chlorophyll fluorescence at 760 nm (SIF) transmitted through the scattering layer but ignoring multiple scattering because of the weak signal.  
If not otherwise noted, in the following, F stands for flux, I for intensity (radiance), T for transmittance, \(\tau\) for vertical optical thickness, and g for gaseous absorption. A superscript s stands for the scattering layer in general. A subscript e, a, and s stand for extinction, absorption, and scattering of the scattering layer, respectively. As an example, the term \(T_{I}^{g}\) represents a transmittance of intensity through a gaseous absorber.
# Improving Sorbents for Glycerol Capture in Biodiesel Refinement
## 2 Results
### Glycerol Capture from Biodiesel  
Capture of glycerol from biodiesel based solutions was also evaluated (see Section 3.7). Biodiesel was spiked with a known concentration of glycerol. Figure 9 presents representative data for capture of glycerol from this solution (additional results, Supporting Information, Figure S6 through S9). The base ED11 sorbent (no sulfonation) did not remove glycerol from the biodiesel solution. Though the data sets tend to be noisier than those collected in aqueous solution (Figure 8), capture of glycerol by the sulfonated ED11 and D13 based sorbents (Types 4 and 6) was similar to that observed for capture  
Figure 8: Nitrogen sorption analysis for divinylbenzene resins and commercial sorbents. (**a**) Nitrogen adsorption/desorption isotherms for PDVB-2 (black) and PDVB-3 (red); (**b**) Nitrogen adsorption/desorption isotherms for BD ZorbX (blue), and DW-R10 (green); (**c**,**d**) Pore size distributions for the sorbents colored as in (**a**,**b**), respectively. Nitrogen adsorption by DW-R10 was insufficient for pore diameter analysis.  
from aqueous solution. Capture of glycerol by the S65-Ph(1) sorbent was less than that from aqueous solution. It is possible that this is a result of hydrophilic/hydrophobic interactions as the backbone of this sorbent would tend to be more hydrophilic than those of the others in the series.  
Figure 10 provides single point comparison of glycerol capture from biodiesel by developed sorbents, the commercial resins, and the synthesized poly(divinylbenzene) resins (PDVB-2 and PDVB-3). We have also included data for sodium exchanged versions of the commercial media. D13-Ph2(5), a Type 5 sorbent, dramatically out performs the PDVB resins and the commercial sorbents, binding 100% of glycerol from the challenge solution. As in the aqueous study, D13b(6) binds more target than the lower surface area D13a(6) and D13c(6) sorbents. This pattern holds for the two Type 3 sorbents based on ED11-Ph(3).
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 2 Materials and Methods
### Satellite Modem Operating Specifications  
For remote GNSS-IR sensors, we use the independent LEO satellite provider Swarm Technologies. The energy consumption and, consequently, transmission scheduling will depend on Swarm's service specification and operation of the Swarm M138 modem built into our board. The Swarm modem has four operating states: (1) Sleep Mode, (2) GPS Acquisition Mode, (3) Receive Mode, and (4) Transmit Mode, Figure 4.  
When the modem powers on, it enters GPS acquisition mode to determine the time and location. The modem will also re-enter GPS Acquisition Mode every 4 h or when awoken. Once a GPS fix has been acquired (30 s typical duration), the modem enters the Receive Mode, wherein it listens for a packet from any satellites passing overhead. This mode lasts until either a packet is received from a satellite (at which point it enters Transmit Mode), the modem is instructed to enter Sleep Mode, or enough time elapses that the modem automatically re-enters GPS Acquisition Mode. Robust operation and enhanced availability [15] is built into the M138 modem, as well as ensured by handling the exceptions, such as those caused by lost signals (Swarm or GNSS) or power.  
Figure 3: Board schematic with GNSS receivers, Raspberry Pico microcontroller and Swarm modem.  
Figure 2: Board design: PCB layers and board populated with Raspberry and Swarm modules.  
If a packet is received from a satellite, the modern enters Transmit Mode, attempting to transmit queued packets and receive an acknowledgement. If successful, it will return to Receive Mode, unless put into Sleep Mode. Table 1 shows that transmission is 1-2 orders of magnitude more costly, while Sleep Mode uses 2 to 3 orders of magnitude less energy. Communication incurs a dominant part of energy consumption in IoT nodes [16]--even more so for satellite access. For instance, Swarm reports that sending a maximum-length 192-byte packet at \(P=2.8\) W takes \(\Delta T=3.7\) s and \(E_{total}=12.24\) J, while in comparison Raspberry Pico benchmark for embedded code, hello_sleep runs at 1.5 mW, as per the datasheet.  
For Swarm modem's operating modes, the energy-saving strategy includes:  
1. Keep the modem in Sleep Mode as much as possible. When not in Sleep Mode, its default state is Receive Mode, which uses much more power.
2. Being awake dominates energy usage, either from the actual transmission energy or the GPS Acquisition and Receive Modes.
3. Failure to transmit will waste considerable energy. Thus, one should schedule transmission to when there is a high probability of successful communication.
# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 3 Materials and Methods  
### UAV Survey System  
The UAV aerial survey system generally consists of three components: the UAV platform, the flight control system, and the aerial camera equipment. The specific parameters of the aerial camera system used in this study are presented in Table 1.  
In this study, a DJI Matrice 300 RTK quadcopter UAV produced by DJI Innovation Technology Co., Ltd. in Shenzhen, China was employed for the survey.  
The device is equipped with the DJI Zenmuse H20T camera produced by DJI Innovation Technology Co., Ltd. in Shenzhen, China, featuring HD visible light, HD wide-angle, and HD thermal infrared 3-in-1 modules (Figure 2). This configuration enables continuous aerial photogrammetry for up to 55 min in an open 15-km area. The model is equipped with an RTK module, which provides centimeter-level positioning accuracy through real-time dynamic differential technology. Additionally, by combining time synchronization compensation between the flight control system and the camera, it enables image-control point-free photogrammetry. The aerial photography system is equipped with six-direction positioning and obstacle avoidance technology, which enables automatic detection of flight obstacles, thereby improving operational safety.  
\begin{table}
\begin{tabular}{c c} \hline
**Equipment Parameters** & **Norm** \\ \hline Weight (with battery) & 6.3 kg \\ \hline Maximum horizontal flight speed & 23 m/s \\ \hline Navigation system & GPS + GLONASS + BeiDou + Galileo \\ \hline RTK position accuracy & 1.5 cm + 1 ppm (vertical); \\  & 1 cm + 1 ppm (horizontal) \\ \hline Camera Pixels & Wide-angle 12 megapixels; \\  & Zoom 20 megapixels \\ \hline Pitch of the head & Pitch: \(-120^{\circ}\) to +30\({}^{\circ}\) \\  & Horizontal: \(\pm 320^{\circ}\) \\ \hline Maximum wind speed & 15 m/s \\ \hline Maximum flight time per trip & 55 min \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of the UAV photogrammetry platform.  
Figure 2: DJI Matrice 300 RTK UAV.
# X-Band Photonic-Based Pulsed Radar Architecture with a High Range Resolution
## 4 Conclusions
\begin{tabular}{l l} BW & Bandwidth \\ DDS & Direct digital synthesizer \\ MLL & Mode-locked laser \\ MZM & Mach-Zehnder modulator \\ DPMZM & Dual-parallel Mach-Zehnder modulator \\ FMCW & Frequency-modulated continuous wave \\ LFM & Linear frequency modulation \\ BPD & Balanced photodetector \\ PRF & Pulse repetition frequency \\ PRI & Pulse repetition interval \\ RCS & Radar cross-section \\ ISAR & Inverse synthetic aperture radar \\ IF & Intermediate frequency \\ AWG & Arbitrary waveform generator \\ GSG & Gating signal generator \\ OC & Optical coupler \\ OD & Optical delay \\ CIR & Circulator \\ PM & Phase modulator \\ HC & Hybrid coupler \\ HPA & High power amplifier \\ PD & Photodetector \\ TxPD & Transmit photodetector \\ DOBPF & Dual optical bandpass filter \\ ADC & Analog-to-digital converter \\ DSP & Digital signal processor \\ HRRP & High resolution range profile \\ \end{tabular}
## A Passive Testing Approach for Protocols in Wireless Sensor Networks  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\), [PERSON] \({}^{3}\) and [PERSON] \({}^{2,4}\)**  
\({}^{1}\) School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China  
\({}^{2}\) Telecom SudParis, SAMOVAR, CNRS, Universite Paris-Saclay, Evry 91000, France;  
E-Mails: [EMAIL_ADDRESS] (S.M.); [EMAIL_ADDRESS] (Z.Z.)  
\({}^{3}\) School of Information Systems, Singapore Management University, Singapore;  
E-Mails: [EMAIL_ADDRESS] (H.-X.T.); [EMAIL_ADDRESS] (H.-P.T.)  
\({}^{4}\) School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-10-5168-4520.  
Academic Editors: [PERSON], [PERSON] and [PERSON]  
_Received: 12 October 2015 / Accepted: 13 November 2015 / Published: 19 November 2015_  
**Abstract:** Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN). However, one stage that is often neglected before their deployment is the conformance testing process, a crucial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works propose to specify the protocol with state models or to analyze them with simulators and emulators, we here propose a logic-based approach for formally specifying some functional requirements of a novel WSN routing protocol. We provide an algorithm to evaluate these properties on collected protocol execution traces. Further, we demonstrate the efficiency and suitability of our approach by its application into common WSN functional properties, as well as specific ones designed from our own routing protocol. We provide relevant testing verdicts through a real indoor testbed and the implementation of our protocol. Furthermore, the flexibility, genericity and practicability of our approach have been proven by the experimental results.  
**Keywords:** passive testing; wireless sensor networks (WSN); conformance testing
# Data-Driven Control Algorithm for Snake Manipulator
## 3 Simulation of DDPG Control Based on 2-Link Model
### Analysis of Experimental Results  
This simulation design is a single-point control precision simulation experiment. The sampling time is 0.02 s. Single episode time is 10 s, with a total of 1800 episodes. Two simulation experiments were designed. They involve the additive torque disturbance value \(\begin{bmatrix}1&1\\ 1&1\end{bmatrix}\) and undisturbed value. The training progress is shown in Figures 7 and 8.  
Figure 5: Q network structure design.  
Figure 6: Action network structure design.  
In Figures 7 and 8, the blue curve represents the change in the total reward value for each episode of training. The red curve represents the change curve of the average value of the total episode reward. The cyan curve is the Q value change curve for each episode of the evaluation network. With the increase in the number of training episodes, the fluctuation of the curve tends to be stable, the network finally converges, and the agent reaches the optimal value in this state. Overall, there is no difference between the two situations. Due to the anti-disturbance adjustment of the controller, the curve fluctuation is different in early training. It is proven that the DDPG controller has a strong anti-disturbance ability and strong stability.  
Through the 2-joint experimental simulation of the snake manipulator, it is proven that the DDPG algorithm can improve the anti-interference and stability of the manipulator control system. Through many repetitions of training and learning, the position control accuracy of the end of the manipulator can be improved. However, it is difficult to establish the overall model of the snake manipulator. Thus, it is difficult to realize the model-based DDPG control method.
## 3 Wireless Communication and Power Transmission
### Wireless Information Transmission
Simultaneously, CardioMEMS EndoSensor(tm), recognized as the smallest wireless implantable blood pressure sensor available on the market, is specifically designed for monitoring the blood pressure of patients with cardiovascular diseases (including heart failure, abdominal aortic aneurysm, and hypertension). This device is implanted outside the vascular repair site, with a volume of approximately 0.23 cubic centimeters. It does not necessitate electronic components or batteries and has a service life of over 3 years [78]. Ultrasonic Waves: Different from the previous four communication methods, this method transmits information through the vibration of sound waves. Ultrasonic waves have good penetration in the human body. Using this method, sensors can be placed deeper in the human body to obtain physiological data that is difficult to detect by other methods. Medical ultrasonic equipment has found extensive applications. Ultrasonic imaging devices, including B-mode ultrasound and color Doppler ultrasound, are capable of clearly depicting the internal structure and tissues of the human body, thereby facilitating physicians in disease diagnosis. Ultrasonic diagnostic equipment, employed in cardiac ultrasound, vascular ultrasound and other examinations, can detect internal lesions and anomalies within the human body. Nevertheless, at present, there is a dearth of commercial ultrasonic communication sensors for implantable devices. This represents an issue that demands resolution and can be regarded as a potential research avenue in the future.
# Nanotechnology, Nanomedicine, and the Kidney
## 3 Renal Clearance of Nanoparticles
### Surface Charge  
The surface charge of nanosized molecules is another essential determinant of renal filtration behavior. The two main factors responsible for the effect of molecular charges are, (i) the interaction between the NP's charges and the charges within the GFB, and (ii) the HD increase due to particle adsorption of serum proteins with an opposite charge.  
The influence of the molecular charge is significant for molecules in the 6 to 8 nm range. Studies evaluating the impact of the molecular charge on renal clearance have shown that for molecules of a similar size, clearance is faster for positively charged molecules, followed by neutral molecules, and finally negatively charged molecules, due to the presence of the negatively charge GBM in the GFB [23; 35].  
Surface charges also influence the clearance of nanoparticles by increasing their HD over the upper limit of the GFB. For example, [PERSON] et al., showed that purely anionic and cationic charges on the QD's surface are associated with serum protein adsorption, resulting in an increased HD of up to 15 nm and a reduced renal clearance [18].