esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study examined the porosity, flow dynamics, and heat and moisture transfer within a packed bed of soybeans. The structural characteristics and flow parameters of the packed bed were comprehensively analyzed across various particle size ratios ranging from 4.44 to 6.25. The heat and moisture transfer within a packed bed of soybeans at various particle temperatures were examined, yielding the following research conclusions:  
1. The results indicate that the impact of the tube wall is minimal when the particle size ratio is low. The structural characteristics of the bed have a significant influence on the flow characteristics. Stagnation or backflow can occur in the wakes of the particles, while high velocities are present in areas with high porosity. Furthermore, altering the temperature of soybean particles changes the parameters of transient heat transfer, thereby affecting the convective heat transfer process. As the temperature of soybean granules increases, the water transfer rate in the packed bed also increases, along with the mass transfer rate.
2. The temperature distribution of the packed bed is uniformly distributed in steps. The temperature of the hot air gradually increases and the drying rate is accelerated, so the elapsed time to reach safe moisture becomes increasingly less. The molecular diffusion is incomplete, and the temperature uniformity becomes worse.
3. Analysis of ventilated drying of soybean pellets reveals that the temperature and moisture of the pellets in the front of the vented inlet change the fastest, while the pellet's interior remains high in moisture. The moisture differential between the interior and surface of the particles decreases as drying time increases. The local non-equilibrium heat and moisture transfer model proposed in this study correctly predicts soybean particle temperature and moisture fluctuations at various scales.  
Figure 17: Average moisture of particles in packed beds at different times.  
**Author Contributions:** Conceptualization, Z.N. and X.L.; methodology, Z.N.; software, Z.N.; validation, Z.N., X.L. and Z.L.; formal analysis, Z.N.; investigation, Z.N.; resources, Z.N.; data curation, Z.N.; writing--original draft preparation, Z.N.; writing--review and editing, Z.N.; visualization, Z.N.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the National Natural Science Foundation of China (Grant No. 51406123) and the University Synergy Innovation Program of Anhui Province (Grant No. GXXT-2022-017), and the Education Revitalization Project of Anhui Province (CN), China (Grand No. 2021 cyxy023).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Surface Salinity (OSS)
#### 2.4.3 Summary and Future Direction  
Considering the effect of OSS on marine ecosystems and ocean-related economies, the future direction of OSS observation using the RS systems can contribute to ocean sus  
Figure 8: Ocean surface salinity map derived from 8-day averaged SMAP observations.  
tainability and monitoring studies. It is also important to investigate different RS systems for OSS estimation and select the optimal RS systems and corresponding techniques for reliable monitoring of OSS. Frequent RS measurements with reasonable spatial resolutions should be combined with state-of-the-art ML algorithms to provide accurate long-term monitoring of OSS.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 2 Related Work
Additionally our contribution combine both, the local or intra-communication between CH members and their CH and the inter-communication between CHs. For intra-communication the FSA as RAP is implemented (although other candidates might have been assumed). For inter-communication we design a deterministic time slot assignment, TDMA as MAC protocol. The literature available in TDMA scheduling is quite extensive. For instance, a nice survey on TDMA scheduling applied to wireless multihop networks can be found in [15]. In both, intra-communication and inter-communication, we claim to the slot reuse concept in a similar way to cellular networks. The E2E delay is analytically obtained by analyzing the corresponding open feed-forward queuing network. To the best of our knowledge this has not been derived before in open literature.
# Understanding Forest Health with Remote Sensing-Part II--A Review of Approaches and Data Models
## 2 Trends in Close-Range RS Approaches for Assessing FH
### Multi-Sensor Approaches
Based on this sensor combination, HyspIRI is expected to be useful in quantification of forest canopy biochemistry and components in the biogeochemical cycles of FES [21; 201; 202; 203], the health status of forests based on biochemical-biophysical ST, or the pattern and spatial distribution and diversity of forest plant species and communities [204]. Furthermore, it will be able to discriminate and monitor forest species [204; 205], forest plant functional types, [206; 207] or invasive species [21] as well as natural disasters and disturbance regimes, i.e., volcano eruptions, wildfires, beetle infestations [208; 209], and the global carbon cycles. The Hyperspectral Imager Suite (HISUI) is a coming space-borne multi-sensor platform combining a hyperspectral imager (185 spectral channels) and a 5-band multispectral imager, which will be launched on the Advanced Land Observation Satellite 3 (ALOS-3) [200]. Table 8 lists example findings using multi-sensor approaches in common FH applications.
# Small-UAV Radar Imaging System Performance with GPS and CDGPS Based Motion Compensation
## 2 Materials and Methods  
### Measurement Devices  
The M-UAV radar prototype described in [22] and shown in Figure 1a is used to collected radar data. A second ground-based GPS receiver (see Figure 1b) is also deployed for the implementation of the CDGPS.  
The detailed description of the M-UAV prototype with all mechanical and electronic devices is given in [22]. Here, we briefly summarize the main hardware components:  
* Small M-UAV platform: DJI F550 hexacopter is a mini UAV able to fly at a very low speed (below 1 m/s), ensuring a dense data sampling, and is capable of taking-off and landing from a constrained area;
* Radar system: PulsON P440 is a compact and short range radar able to transmit ultra-wide band pulses (about 1.7 GHz bandwidth) in the frequency spectrum between the S and C Bands (3.95 GHz carrier frequency) [25];
* Radar antennas: the radar system has been equipped with two Ramsey LPY26 antennas, which are log-periodic PCB antennas, having a radiation pattern whose aperture angle is of about \(80^{\circ}\) in the along-track and \(110^{\circ}\) in the across-track;
* GPS receivers: two single frequency, single constellation (GPS-only) u-blox LEA-6T devices, one mounted onboard the UAV and the other one used as ground-based station;
* CPU controller: Linux-based ODROID XU4, which is devoted to manage data acquisition for both radar system and onboard GPS receiver, while assuring the time synchronization between radar and GPS clocks.  
The radar module is mounted very close to the flight battery, below the UAV autopilot, see Figure 1a. The distance between the radar antennas and the drone center of mass is about 10 cm. Identical antennas, pointing at nadir direction (down-looking mode) [26] are used to transmit the probing signal and receive the backscattered one. The radar operates in monostatic mode being the distance between the transmitting and receiving antennas negligible in terms of the radar signal wavelength.
## 2 Experiments
### Experimental Apparatus  
The test equipment was designed to simulate the environment where the jet flow impact is applied to the primary barrier, as shown in Figure 3a. The impact test machine was designed to apply horizontal impact load by free fall of a dropped object onto the specimen at a certain height controlled by an electromagnet. This equipment consists of three parts: impactor, fixing jig, and measuring equipment. The impact energy applied to the test specimen is estimated through the drop height and speed of the impactor, which is designed to apply an impact load on large and small corrugation specimens. The total width of the impactor was 220 mm, and the radius of curvature was 35 mm to avoid an unwanted tearing phenomenon. The weight of the impactor alone could not generate sufficient loads; therefore, the drop object was added to specifically apply impact loads in the downward direction. The total weight of the drop object with the impactor was 340 kg. The drop object was fabricated to constrain the rotation of the impactor during the impact tests.  
Figure 3b displays the impact test jig with a large corrugation specimen that was erected and fixed during the impact testing. The two erection plates maintained the distance from the corrugation knot of the primary barrier. One of the erection plates held the specimen with bolts and nuts, whereas the other prevented the impactor from escaping the path to the specimen. Owing to the presence of the erection plates holding the primary barrier, it was not possible to observe the deformed shape of the primary barrier using a high-speed camera. Six connection rods were installed at both sides between the erection plates to prevent the erection plates from moving. The bottom plate fixed both the erection plates and the entire experimental apparatus on the impact test machine. Four load sensors under the fixing jig measured the real time impact load generated by the impactor. The deformation or behavior of a specific object can be measured by photographing with a high-speed camera. In the present study, however, as the structure of the impact test jig hindered capturing photographs of the deformed shape of the specimen during the impact test, only the displacement of the impactor was measured to calculate its acceleration.  
Figure 2: Specim preparation for (**a**) membrane panel, (**b**) large corrugation, and (**c**) small corrugation.
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
###### Abstract  
Lamb wave-based non-electromagnetic communication is an effective solution for real-time information exchange in health monitoring networks of large metallic plate structures. The multimodal nature, dispersive characteristics, and the influence of reflected waves during the propagation of Lamb waves severely limit the duration of communication signals. Within this constrained time, constructing communication signals reasonably is crucial for improving the transmission rate of Lamb wave acoustic data. A coding method based on frequency-division multiplexing-pulse-position modulation (FDM-PPM) is proposed to address the low transmission rate in Lamb wave communication systems. Experimental results demonstrate that the proposed Lamb wave communication system can achieve a maximum transmission rate of up to 50 kbps with a bit error rate as low as 90.7%. Compared with methods using Amplitude-Shift Keying (ASK) and pulse-position modulation (PPM), this method effectively enhances the transmission rate of the Lamb wave communication system while reducing the energy consumption of the excitation signal.  
Keywords:lamb wave; frequency-division multiplexing; pulse-position modulation +
Footnote †: journal:
# Tip Estimation Method in Phantoms for Curved Needle Using 2D Transverse Ultrasound Images
## 3 Results  
### Experimental Platform Setup  
To verify the proposed tip tracking and shape sensing method, a robotic ultrasound system has been built, which includes a KUKA IIWA robot arm, a Wisonic ultrasound scanner, an NDI optical tracker, an NDI electromagnetic (EM) tracker, and a computer. As shown in Figure 7, the US probe is mounted on the effector of the robot arm by the gripper attached to the passive marker. The phantom or ex-vivo (like chicken breast in Figure 7) is punctured by an 18G beveled-tip needle with 200 mm long, while the needle tip is completely exposed for validation. The diameter of the needle is 15 pixels in the image. The NDI optical tracker is used to localize the marker bound with the probe, while NDI electromagnetic tracker is used to validate the tip position.  
Experiments have been taken in a water tank, which provides a liquid environment for the ultrasound. And the needle is placed in water or inserted in the silica gel phantom (shown in Figure 8a), pork and chicken breast. The depth of the ultrasound is set to 4 cm. In this study, the needle is usually detected in 1 to 3 cm from the US probe. During the experiment, the robot arm automatically moves with the ultrasound probe along the direction of the needle insertion without any contact with the tissue (shown in Figure 8b). The whole scan length is at most 160 mm which depends on the scan intervals (shown in Table 1). The scan interval decreases with the increasing collected points.  
Before data collection, the US image and the marker need to be calibrated. After that, the experiment starts after the needle finished puncturing manually. The robot arm is used to move the US probe scanning along the needle. Meanwhile, pose data are collected from the optical tracker and US images from the ultrasound scanner. Finally, the tail of the needle and its tip are measured by the optical and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scan Interval** & **Scan Length** & **Points** \\ \hline
1 mm & 160 mm & 160 \\
2 mm & 159 mm & 80 \\
3 mm & 160 mm & 54 \\
4 mm & 157 mm & 40 \\
5 mm & 156 mm & 32 \\
6 mm & 157 mm & 27 \\
7 mm & 155 mm & 23 \\
8 mm & 153 mm & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scan lengths with different scan intervals.  
Figure 7: The devices of the experiment.  
electromagnetic sensors, respectively, for the curve fitting and the tip validation. The needle is inserted manually, imitating the real situation of percutaneous intervention.
## 2 Materials and Methods
### InSAR Processing
Under the WMLE principle, the noise-free image can be evaluated by maximizing a weighted likelihood function of the noisy data  
\[\hat{f}(n)=\arg\max_{f}\sum_{m}w(n,m)\log[p(g(m)|f)] \tag{9}\]  
where \(\hat{f}(n)\) is the estimated noise-free image \(f(n)\), \(g(n)\) is the observed noisy image, and the weights \(w(n,m)\) can be regarded as a measure of the extent to which a pixel at \(m\) has a similar distribution to the reference pixel at \(n\). The formulation of the weights \(w(n,m)\) is the main challenge with the WMLE. In the PPB algorithm, the solution consists of expressing the weights as the probability, conditional on the observed image \(g\), that two pixel patches centered at positions \(n\) and \(m\) are modeled by identical distributions. If we assume that pixels in the patches are independent of one another, the weights can be formalized as  
\[w(n,m)=\prod_{k}p(f(n+k)=f(m+k)|g(n+k),g(m+k))^{1/\alpha} \tag{10}\]  
where \(k\) varies over the patch and \(\alpha\) is a decay parameter. According to Bayesian reasoning, without knowledge of prior probabilities, \(p(f(n+k)=f(m+k))\), the posterior probabilities in Equation (10) are assumed to be proportional to the likelihood function, \(p(g(n+k),g(m+k)|f(n+k)=f(m+k))\). For SAR images in intensity format, if the pixel amplitudes, \(a=\sqrt{g}\), are assumed to be independent and identically distributed according to the Nakagami-Rayleigh law [34], the weights of the PPB filter can be determined as follows [45]:  
\[w(n,m)=\exp\left[-\frac{1}{h}\sum_{k}\log\left(\frac{a(n+k)}{a(m+k)}+\frac{a( m+k)}{a(n+k)}\right)\right] \tag{11}\]  
and the speckle-free image is given by  
\[\hat{f}(n)=\frac{\sum_{m}w(n,m)a^{2}(m)}{\sum_{m}w(n,m)}. \tag{12}\]  
In Ref. [45], a further improvement of the model considers the probabilities in Equation (10) as also depending on previous estimates of the despeckled image, thereby leading to an iterative scheme where the weights are updated according to the previously filtered image. The Non-Local Interferometric Estimator (NL-InSAR) [46], an extension of the PPB to InSAR data, is a state-of-the-art coherent filter that jointly yields a consistent estimate of coherence and a noise-free estimate of the interferometric phase, provided that coherence is sufficiently high.
# An Overview of Fog Data Analytics for IoT Applications
## 4 Current State-of-the-Art  
In the era of faster communication and even faster computational needs, fog computing prevails in the industry with such requirements by processing data closer to the source. Modern advancements such as big data analytics, machine learning, and deep learning are combined with fog for generating data analytics on the fly with adaptive learning [31; 32; 33; 34]. Leveraging the technology of blockchain to accommodate privacy and security  
Figure 6: Data analytics using fog computing.  
features in the fog layer is proposed in [35; 36; 37]. Software-defined networks (SDNs) with customized routing protocols are used to increase the energy efficiency and performance between fog nodes [38]. With SDNs and named data networking (NDN) [39], traditional networking concepts are redesigned to harness new advantages in fog computing. They drive innovations in various fields and transform the experience for greater efficiency and convenience. Recent developments such as quantum computing paradigms are also considered to optimize the performance of fog-based systems for parallel processing and dynamic load scheduling using quantum-computing-inspired optimization (QCIO) [40]. Finally, some areas harnessing the power of fog computing with IoT devices are explored for state-of-the-art implementation.
## 4 Dynamic Placement of SFC Based on Meta Reinforcement Learning and Fuzzy Logic
### Outer Model  
The general training process of the outer model is depicted in Figure 4. We can see that the acquisition of initial parameters includes the training of the task DNN and the training of the general DNN. The former entails conducting step-by-step optimization training for each scenario, while the latter involves periodically synchronizing updates on a batch of sampled scenarios. Specifically, we randomly select some IoT-MEC scenarios. For each chosen IoT-MEC scenario, we copy a general DNN named the task DNN and train it with a batch of trajectories randomly sampled from experience replay. In other words, the parameter update of each task DNN is performed based on the globally shared initialization of parameters that are inherited. After the completion of network parameter updates for each trajectory in the batch, the adapted policy's capability for each IoT-MEC scenario can be evaluated by calculating the loss function of the query set. And the calculated loss functions are used to optimize the general DNN parameters. After completing the iteration over all selected IoT-MEC scenarios, the general DNN parameter is updated to incorporate the learning experience gained from policies associated with known learning scenarios. The algorithmic process of meta learning is shown in Algorithm 1.  
```
0: Different IoT-MEC scenarios, iteration times \(N\)
0: Trained general DNN model with better initial parameters which used in inner model
1: Randomly initialize the parameters of general DNN \(\theta\)
2: Empty the experience replay
3:for\(i=1,2,3,\dots,N\)do
4: Sample a batch of IoT-MEC scenarios without repetition
5:for each IoT-MEC scenario do
6: A copy of the general DNN is created as the task DNN
7: Sample \(K\) trajectories from experience replay at random
8: Compute adapted parameters of task DNN with gradient descent
9: Sample mini-batch trajectories from experience replay as query set
10: The gradient of the loss function on the query set is evaluated
11:endfor
12: Update the general DNN parameter \(\varphi\).
13:endfor
```  
**Algorithm 1** The general training process of the outer model  
Figure 3: The procedure of link evaluation.
## 3 Signal Processing
### Signal Preprocessing  
The preprocessing of the data starts with the data acquisition at the XADC, which converts the generated analog voltages of the electrical circuit into digital values, represented as the ADC interface in Figure 7. The XADC is a multichannel ADC with a theoretical maximum sampling rate of 1 MSps. Internally, the XADC consists of two synchronized ADCs with a sampling rate of 1 MSps and a configurable multiplexer used for switching the different measurement channels. For this reason, the number of used channels is not negligible, since it defines the final sample rate per measurement channel and can lead to wrong conversion results. In the described architecture, four channels are used, where two channels are mapped to ADC1 and the other two channels are mapped to ADC2, respectively, leading to a sample rate of 500 kSps on ADC1 and ADC2. To guarantee data consistency, ADC2 has been configured to work synchronously with ADC1, concluding in a final sample rate of 500 kSps, which defines the measurement window size and buffer sizes. Switching the channels and initial configuration of the XADC is guaranteed by the ADC interface.  
A gradient-based algorithm is used as a feature extractor for the machine learning part (cp. Section 3.4.). The gradient calculation is sensitive to interferences, since a high-frequency noise signal is always accompanied by fast-changing amplitudes, leading to higher gradients. For this reason, the data are smoothed using a moving average filter. For this purpose, a shift register was implemented that satisfies the condition \(n=2^{i}\), with \(i\in\mathbb{N}\) and shift register length \(n\), thus efficiently implementing the division by a power of 2. The division by a power of 2 can be implemented by a shifting operation of \(i\) bits, represented as the SHR block in Figure 7. Since determining the sum for large measurement window sizes would require a lot of resources, the filter value \(m\) was calculated according to formula:  
\[m(x)_{t}=\frac{1}{n}\left(\sum\
olimits_{i=0}^{t-1}x(i)-\sum\
olimits_{i=0}^{t -1-n}x(i)\right) \tag{1}\]  
The first part of the equation can be implemented efficiently by continuously accumulating the incoming samples. The second part represents the same summation, but the signal is shifted by \(n\) samples in time. Therefore, the input signal is delayed for \(n\) samples by the shifting register  
Figure 8: Electric circuit for transforming the low impedance input signal to high impedance signal, shifted by 0.5 V and limited to a 0–1 V ADC measurement range.  
and is subtracted continuously. This offers the advantage of linear scalability, while the arithmetic operation is reduced to a summation and subtraction, leading to complexity reduction and avoiding timing problems.