esa-sceva/satcom-chunk-collection huggingface dataset:
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
###### Abstract  
Laser driven Wake-Field Acceleration (LWFA) has proven its capability of accelerating electron bunches (e-bunches) to up to 4 GeV energy in a single stage while reaching gradients up to hundreds of GV/m. Because of the short period of the accelerating field (typically ranging from 100 fs to 1 ps duration) and the requirement of extremely small beam size (typically smaller than 1 \(\upmu\)m) to match the channel, e-bunches can reach extremely high densities. They can be either extracted directly from the plasma or externally injected. The study of the external injection is interesting for two main reasons. On the one hand this method allows better control of the quality of the input beam and on the other hand it is in general necessary when a staged approach of the accelerator is considered. The interest in producing, characterizing and transporting high brightness ultra-short e-bunches has grown together with the interest in LWFA and other novel high-gradient acceleration techniques. In this paper we will review the principal techniques for producing and shaping ultra-short electron bunches with the example of the SINBAD-ARES (Accelerator Research Experiment at SINBAD) linac at the Deutsches Elektronen-Synchrotron (DESY). Our goal is to show how the design of the SINBAD-ARES linac satisfies the requirements for generating high brightness LWFA probes. In the last part of the paper we shall also comment on the technical challenges for electron control and characterization.  
LWFA; fs bunches; compression; external injection; matching +
Footnote †: journal: Applied Physics Letters
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol
### Setup Phase (SP)
#### 3.2.4. Data Collection Period (DCP)
(3) Data collection process in DCP  
Data collection of SS-MAC is completed by two steps: First, member nodes forward their sensed data to their CHs. Then, from the highest level, CHs send the data received from members to their respective parents. After forwarding level-by-level, the data are finally aggregated to the sink. If \(T_{slot}\), \(SID(i)\) and \(SAN(t)\) represent the duration of a data slot, the order of node \(i\) in the node ID list and the number of slots allocated to the \(t\)-th node, respectively. The time that node \(i\) needs to wait before sending its data packets is:\[\mathit{Tstart}(i)=T_{slot}\times\sum_{t=1}^{\mathit{SID}(i)-1}\mathit{SAN}(t)\]  
To reduce energy consumption and delay, CHs do not respond to data packets sent by member nodes individually, but reply to all member nodes through an ACK broadcast after receiving all data packets. The ACK contains information about packet errors and packet loss. Nodes will resend the problematic packets to the next DCP. ```
0:\(C[i,\omega]\): Optimal value of algorithm
0:\(B[i,\omega]\): Node sorting result
1:\(B[i,\omega]=C[i,\omega]\);
2:\(i=n\);
3:\(\omega=W\);
4:while\(i>1\) and \(\omega>1\)do
5:if\(B[i,\omega]>B[i-1,\omega]\)then
6:\(i=i-1\);
7:\(\omega=\omega-\omega_{i}\);
8:else
9:\(i=i-1\);
10:endif
11:endwhile
```  
**Algorithm 2** SS-MAC node sorting algorithm.
# Optimal Prefix Free Codes with Partial Sorting +
## 5 Discussion
### Potential (Lack of) Practical Impact
#### 5.2.2 Generalisation to Non Binary Output Alphabets  
[PERSON] [1] described both how to compute optimal prefix free codes in the case of two output symbols, and how to generalize this method to more output symbols. [PERSON] et al. [29] showed that compressing to 256 output symbols (encoded un bytes) provides some advantages in terms of indexing the compressed output, with a relatively minor cost in terms of the compression ratio.  
Keeping the same definition of the EI signature and alternation \(\alpha\) of an instance, the GDM algorithm described in Section 2.3 (and its analysis) can be extended to the case of \(D\) output symbols with very minor changes, and will result in an algorithm which running time is already adaptive to \(\alpha\), with the same running time as if there were only \(D=2\) output symbols. Yet this would not be optimal for large values of \(D\): on instances where \(D=n\), the optimal \(d\)-ary prefix free code is uniform and returned in time at most linear in the input size, whereas an algorithm ignoring the value of \(D\) could spend time within \(O(n\log n)\).  
Further improvements could be achieved by extending the concepts described in this work to take into account the value of \(D\), such as to \(D\)-ary-EI signature and \(D\)-ary alternation. Such improvements in running time would be at most by a multiplicative factor of \(\log_{2}D\), and seem theoretically interesting, if probably of limited interest in practice.
# Secure Beamforming in 5G-Based Cognitive Radio Network
## 2 Related Work and Contributions
### Minimum Mean Squared Error (MMSE) Related Works  
In [17], the authors surveyed the hybrid beamforming methods for the Massive MIMO communications in the context of a hybrid transceiver. This method also included an MMSE regularization for a hybrid precoding in the mmWave massive MIMO systems.  
An MMSE MIMO-orthogonal frequency division multiplexing (OFDM) detector was proposed in [18] to balance an error performance and the spectral efficiency. It used maximum likelihood (ML), simple MMSE, and ordered successive interference cancellation (SIC) methods. This method was used in 5G CRN in massive MIMO to develop a secure transmission and to mitigate an interference and a peak-to-average power ratio.  
In [19], the objective of an MMSE receiver was used to reduce the MSE of the estimated signal in contrast to the transmitted signal. The massive MIMO system performance was examined under the characteristics of a different linear receiver. A performance of an MMSE receiver in a realistic system was found in [20] under interference conditions.  
In [21], an opportunistic transmission protocol (OTP) was examined according to the three types of beamforming techniques. One of the most perfect beamforming techniques is MMSE-SIC detection method. In this article, all the beamforming techniques and the OTPs were executed by the secondary user (SU) in a decentralized process.  
Reference [22] proposed a transmit filter design using an MMSE approach to ensure a low complexity for a MIMO interference channel. The MMSE scheme combined the signal and the interference leakage of each transmitter.  
Both a ZFBF scheme and the MMSE methods were used in [23]. In this article, a multi-user detection (MUD) method is examined in multicarrier-direct-sequence code-division multiple-access (MC DS-CDMA), which inspired a low complexity and a high flexibility. A scope of a low-complexity MUDs was derived based on the ZF, MMSE, and interference cancellation (IC) principles. The proposed MUD was implemented using a modular structure and most of the modules were independent of each other.  
The proposed contribution contains the integration of a zero forcing and the MMSE technique, which are implemented in the desired signal vector. The proposed technique is also known as the regularized zero forcing method [24]. However, the proposed method power transmission is limited by the iterative solutions of the normalized mean square error method. This provides an enhanced solution in the secrecy rate when compared to the existing zero forcing technique. In the proposed method, the bit error rate (BER) and the secrecy outage probability (SOP) are greatly reduced. The throughput and the channel capacity are increased.
# Novel Thermal Management Strategy for Improved Inverter Reliability in Electric Vehicles
## 4 Validation of the Proposed Thermal Management Algorithm
### Validation under Standardized WLTP Driving Cycle  
Finally, the proposed TCT algorithm has been evaluated through a WLTP driving cycle. Figure 8 shows the results obtained when the thermal management strategy is not active. In this scenario and as a consequence of the high power losses (Figure 8c), \(T_{j,max}\) is exceeded in various intervals of the WLTP cycle (Figure 8d). However, when using the proposed thermal management approach (Figure 9), the junction temperature does not exceed the limit value thanks to the regulation of the switching frequency (Figure 9d). It is important to remark that when the junction temperature remains under the maximum limit, \(\delta F_{sw}\) is saturated to zero, and the nominal switching frequency is restored to its default value of 25 kHz. Additionally, as shown in Figure 9a, the thermal management strategy does not interfere in the normal operation of the drive, extending its life-cycle and helping to achieve the \(CO_{2}\) emission reduction goals. Although a slightly higher torque ripple is present when the TCT algorithm reduces the switching frequency, its effects are filtered by the EV's large inertia, making such a ripple negligible for the passengers' comfort.  
Figure 8: Simulation results of the SiC based IPMSM drive under standardized World Harmonized Light-duty Vehicle Test Procedure (WLTP) driving cycle without TCT regulation.  
Figure 7: Thermal regulation dynamics vs. TCT parameter \(\alpha\).
# Ultrasonic Power and Data Transfer through Multiple Curved Layers Applied to Pipe Instrumentation
## 4 System Implementation
### Full System Simulation  
The full system implementation designed on LTSpice, Figure 6, was implemented based on the experimental setup described in Section 3. The electrical signal applied to electrodes of the outside piezoelectric transducer was set to operate with an amplitude of 40 Vpp at 1.119 MHz, where the maximum power transfer and voltage variation occur, according to Section 3.1. The whole system simulation is shown in Figure 6. In the outside block, in black, an envelope detector and a voltage comparator, in gray and brown, respectively, were designed. The output signal represents the data transmitted from the inside block. The isolated part of the system, the inside block, ensembles the components to conditionate the received power, the voltage doubler circuit, and the voltage regulator, in purple and dark green, respectively. The other components simulate the data transmission, here chosen as a digital pulser and n-channel MOSFET, in light green and orange, respectively. The digital pulser has a transmission rate of 4000 bps and the output is directly connected to the MOSFET gate. Figure 7 shows three waveforms acquired from a full-system simulation, where the cyan one represents  
Figure 5: (**a**) Power transfer analysis and (**b**) input voltage variation of experimental and simulated results.  
the voltage that is received by the inside transducer, the purple is the signal after the carrier signal passes through the envelope detector, and lastly, in black, the demodulated signal, which is the digital data when the enveloped signal passes through the voltage comparator. The generated voltage on the inside transducer has a maximum baseline of approximately 14 V, and reaches around 1 V when the MOSFET is conducting. Due to the use of non-ideal elements implemented in the simulation, the minimum baseline voltage has a non-null voltage, as expected from the MOSFET on-resistance. The enveloped signal varies by approximately 1.5 V after passing through the envelope detector circuit, while the demodulated signal has a squared waveform of the same frequency and amplitude of the digital pulser.
## 4 Discussion
### Wearable Sensing Device  
The wearable sensing device (WSD) is a device that aims to evaluate the skills of volleyball players using a MEMS-based IMU in its data collection. To the best of our knowledge, only one study used this device [20]. [PERSON] et al. [20] reported that the WSD successfully recorded the inertial information of the volleyball players and stored all data on a MicroSD card for later analysis. Furthermore, with this device, it is possible to observe the data of all the IMU movements performed by the volleyball players and join them to a video system for further analysis, a handy tool for coaches/teachers and volleyball players. In addition, it was still possible to compile data on the different skill levels of volleyball players, which can be used by researchers/coaches/teachers in additional studies/research. [PERSON] et al. [20] examined 15 features to characterize each data segment. The support vector machine, k-nearest neighbor, and naive Bayes classifiers estimated the performance levels represented by the skill data. Comparisons of these classifiers indicated that support vector machines could achieve high accuracy (94%) in assessing skill levels, which can help coaches/teachers monitor players throughout the entire sporting season (training and game). Therefore, based on the results obtained in [PERSON] et al. [18], it was possible to distinguish the different skill levels found in players using the WSD. Thus, we can conclude that this device can effectively estimate the skill level of a volleyball player, and a training program suitable for each player can improve the skill level and the effectiveness of the training or game. However, further studies are needed using this device to determine its reliability.  
The WSD is a reliable device for differentiating the skill levels of different volleyball players; however, this device does not transmit wireless inertial sensor information for real-time data analysis, i.e., this system can only use machine learning to assess different levels of players from offline analytics.
# A Study on Multiple Factors Affecting the Accuracy of Multiclass Skin Disease Classification
## 2 Related Work  
Skin diseases are common types of illness worldwide. It is of importance to diagnose and treat skin disease early, as some severe skin diseases might cause death [12]. With the help of deep neural networks, skin disease classification has been actively studied. A multiclass skin disease classification scheme was proposed using pretrained AlexNet for feature extraction and error-correcting output codes for support vector machine as the classifier and achieved 86% accuracy on five skin lesion categories [13]. However, the dataset was not balanced for each class, and further work is needed. Research on the combination of four popular machine learning algorithms, namely artificial neural networks, linear discriminant analysis (LDA), naive Bayes, and support vector machines (SVMs), with two feature sets, namely color and texture, was explored [14]. It was found that LDA and SVMs show better accuracy. Automatic classification of clinical skin disease images was proposed using high-level position information [15]. Instead of hand-crafted features, the work used high-level position information to generate better deep visual features and outperformed state-of-the-art clinical skin disease classification methods. A methodology of using the rough set method to extract the best features and feedforward neural network to predict the existence of skin disease was proposed [16]. Morphological- and wavelet-based fractal texture features were used along with stacked auto-encoder-based features to classify four skin diseases [17]. The combined feature set used greatly improved the accuracy of identifying melanoma, nevus, basal cell carcinoma, and sebortheic keratosis diseases, with at least 96% accuracy. A survey of skin disease classification from images was conducted [18]. Traditional techniques and deep learning-based skin disease classification were compared, and it was concluded that the deep learning approach is more efficient and faster for extracting features.
## 4 Experiments Results and Discussions
### Small Scale Crack Detection Experiments and Discussions  
During the real monitoring, it is also necessary to observe the coating status of the local area at a high resolution. At this time, the shooting range will be narrowed. This requires the detection scheme to realize the diagnosis of small cracks with high accuracy. This section will describe such experiments.  
Figure 5 shows the surface temperature maps and the detection results of TBCs obtained under different coating porosity, length and position of small scale cracks. The crack length changes from 10 \(\upmu\)m to 40 \(\upmu\)m. The positions of cracks are at the center, upper left, upper right and bottom right of the image. The middle row is the feature visualization map. It can be seen that compared with Figure 4, the feature visualization effect of this figure is weakened, which shows that the detection accuracy of the algorithm is reduced in the case of small cracks, but it can still accurately identify the cracks. Through the proposed scheme, all the cracks are detected and located accurately with the anchor boxes. This is because MSE-Faster R-CNN has good global feature decoupling. After the convolution kernel fusion of different scales is introduced, some low activation neurons are successfully  
Figure 4: Confusion matrix of large scale crack detection using MSE-Faster R-CNN.  
removed, and thus the feature extraction ability of the model is improved. Despite the similarities between small internal cracks and pores, there are still some differences. Pores are typically isotropic and have more uniform effects on the global, while cracks are anisotropic and have greater effects on the local. If MSE-Faster R-CNN can distinguish the differences between the two kinds of \"holes\" then this problem can be effectively solved.  
Figure 6 demonstrates the confusion matrix of the small scale crack detection. In general, the accuracy of crack detection is decreased compared with that of large cracks, from 84.4% to 81.74%. The detection accuracies are relatively high when there is no crack or the crack length is 40 \(\upmu\)m, which are all around 85%. When the crack is 20 \(\upmu\)m and 30 \(\upmu\)m, some crack samples are easily confused with the nearby classes. Their accuracies are both  
Figure 5: Examples of small scale crack detection results of using MSE-Faster R-CNN. **(a)** Porosity = 5%, Crack length = 40 \(\upmu\)m; **(b)** Porosity = 20%, Crack length = 30 \(\upmu\)m; **(c)** Porosity = 15%, Crack length = 20 \(\upmu\)m; **(d)** Porosity = 25%, Crack length = 10 \(\upmu\)m.  
less than 80%. The reason is as aforementioned in Figure 6: Small internal cracks are easily confused with pores due to their similarities in morphology. The change of temperature caused by a 10 \(\upmu\)m crack is relatively weak. A similar conclusion can be drawn from another phenomenon; that is, compared with the results in Figure 5, the probability of confusion happening in a neighbor crack class is increased, but it is more difficult to confuse with further crack categories.
# Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion
## 2 Materials and Methods
### Modulation  
In MC, to convey distinct messages, each possible message is associated with a distinct molecule signal. To accomplished this, as defined in [17], the information can be modulated based on the properties of the molecules which allows different types of MC schemes: Concentration-based, Type-based, Timing-based, Spatial, or a combination of these (hybrid).  
The concentration-based techniques are a representation of the information that relies on varying the concentration of the molecules. This is achieved by making the amount of released molecules carry information over discrete period time slots, the symbol slots. The simplest form of this approach, analogous to traditional communications, is the on-off keying (OOK), where each symbol represents a one-bit value. Bit-1 corresponds to the transmitter releasing a fixed number of molecules and bit-0 corresponds to the transmitter not releasing anything within the symbol slot. Another possible concentration-based technique is the Binary Concentration Shift Keying (BCSK) where in each symbol slot, sending a certain concentration of a molecule represents bit-1 and a different concentration represents bit-0. In this work, we will consider both OOK and BCSK.
## 2 Materials and Methods
### Remote Sensing Calculations and Field Measurements  
This study employed Landsat time series data and GEE cloud computing to estimate four vegetation indices, with the GEE cloud computing process proving to be both rapid and effective. The computation of four remote sensing vegetation indices (EVI, NDVI, SAVI, and NDWI) was conducted using Landsat 7 and 8 Images through the Google Earth  
Figure 2: Workflow of the research. Note: Tmax: maximum temperature; Tmin: minimum temperature; RH: relative humidity; WS: wind speed; Pe\({}_{\text{eff}}\): effective precipitation; Kc: crop coefficient; SVR: support vector regression; RF: random forest; XGB: extreme gradient boosting; EVI: Enhanced Vegetation Index; NDVI: the normalized difference vegetation index; SAVI: soil-adjusted vegetation index; NDWI: the normalized difference water index; Sc: scenario.  
Engine (GEE) ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/))) at a spatial resolution of 30 m at data level 2, with temporal resolution limited to the period from 2001 to 2020, focusing on the same season from April to November (Table 1). The Google Earth Engine (GEE) functions as a collaborative platform developed by Google, Carnegie Mellon University, and the United States Geological Survey, offering access to a wide array of functions and a vast archive of global satellite imagery and thematic maps spanning nearly four decades [29].
# Digital Shearography for NDT: Phase Measurement Technique and Recent Developments
## 5 Potentials and Limitations
### Limitations  
Although some limitations already have a corresponding solution, there are still problems for NDT by digital shearography:  
1. It is difficult to measure defects away from the object surface using digital shearography. Although directed acoustic shearography can detect subsurface defects, the defects away from the object surface are difficult to detect because they cannot be loaded to produce enough abnormality on the surface that can be detected by shearography.
2. Although a new digital shearography system has been developed for specular surface materials, the measurement field is limited by the size of the rough surface in the optical path.
3. As an interference-based technique, digital shearography is still sensitive to environmental disturbances. Large rigid-body motions, strong thermal air currents, etc. will all affect the measurement results.
4. The phase map obtained by digital shearography still contains lots of noise, which will affect the detection of defects. How to effectively improve the quality of the phase map during dynamic measurement is a problem.