esa-sceva/satcom-chunk-collection huggingface dataset:
# Polarization Sensitive Imaging with Qubits
## 4 Discussion  
In this paper, we present and compare the first experimental images and characterization of a birefringent material using direct quantum state tomography, DQST, and inverse numerical optimization, IQST, post-processing techniques. Images of concurrence and entanglement are reconstructed from the density matrix. These images reflect the computed entanglement between two qubits for each pixel and are a measure of the level of birefringence. Direct and inverse techniques are used to compare concurrence and entanglement image quality. Well-defined enhancement of images of entanglement and concurrence of a 3M tape are presented using the IQST technique of the 3M tape in images of entanglement and concurrence, Figure 3d,f, respectively, compared to the DQST, Figure 3c,e. Figure 4 shows the cross-section of the calculated entanglement taken at the position of the small double-sided arrow between Figure 3e,f. These results highlight the experimental noise reduction achieved by using numerical optimization techniques and overall image enhancement.  
Classical optical techniques were used in reference [24] to image carcinoma cells of lung tissue via polarization sensitive optical coherence tomography (PS-OCT). The authors reported a birefringence signature. Similar classical polarization techniques were applied to imaging of other cancerous tissues [25, 26], where birefringence was clearly visible in the malignant tissues. The technique described in this work has the potential to characterize birefringence of similar malignant tissues by means of polarization sensitive measurements using density matrices of two-level quantum entangled photons.  
Figure 3: (**a**) 3M scotch tape, (**b**) tape between cross-polarizers, (**c**) DQST concurrence image, (**d**) IQST concurrence image, (**e**) DQST entanglement image, (**f**) IQST entanglement image.  
**Author Contributions:** Conceptualization, methodology, validation, writing, review and editing, V.S. and R.D.; visualization, V.S.; supervision, R.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by Corning Incorporated Foundation.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The XMUSPEECH System for Accented English Automatic Speech Recognition
## 2 System Structure  
### Acoustic Modeling  
We followed the conventional steps to train hybrid GMM-HMM acoustic models referring to the Kaldi [19] recipe for CHIME6 ([[https://github.com/kaldi-asr/kaldi/tree/master/egs/chime6/s5_track1](https://github.com/kaldi-asr/kaldi/tree/master/egs/chime6/s5_track1)]([https://github.com/kaldi-asr/kaldi/tree/master/egs/chime6/s5_track1](https://github.com/kaldi-asr/kaldi/tree/master/egs/chime6/s5_track1)) (accessed on 24 August 2021)). It has been shown that sequence-level training criteria such as lattice-free maximum mutual information (LF-MMI) performs better than frame-level criteria for ASR [20]. Our systems are based on the TDNN-F [13] acoustic model using the LF-MMI training criterion. We experimented with various network structures. All of our experiments are based on the Kaldi toolkit.  
* **TDNN-F**: The TDNN-F model is the first 11 layers of TDNN-F in the recipe for CHIME6 of the Kaldi (egs/chime6/s5_track1/local/chain/tuning/run_tdnn_lb.sh).
* **CNN-TDNNF-Attention**: The CNN-TDNNF-Attention model consists of one CNN layer followed by 11 time-delay layers and a time-restricted self-attention layer [21], and we applied a SpecAugment [22] layer on top of the architecture to make it more robust. The CNN layer has a kernel size of 3 \(\times\) 3 and a filter size of 64. The 11-layer TDNN-F shares the same configuration as the previously illustrated TDNN-F, except it substitutes the first TDNN layer with a TDNN-F layer, which has 1536 nodes, 256 bottleneck nodes, and no time stride. The attention block has eight heads, the value-dim and key-dim are set to 128 and 64, respectively, the context-width is 10 with the same number of left and right inputs, and the time stride is 3.
* **Multistream CNN**: We positioned a 5-layer CNN to better accommodate the top SpecAugment layer, followed by an 11-layer multistream CNN [14].
# Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study
## 4 Results
### Bias and Standard Deviation Analysis (Pixel-Level)
This is a consequence of training the proposed network with the MSE loss which implicitly learns to denoise the output images. The bias of the separation results using the CM-based method and the DL-based method (with 80 training pairs) reached a similar level when \(\Delta t\) = 10 min for \([^{18}\)F]FDG and \(\Delta t\) = 15 min for \([^{11}\)C]MET; see Figure 11). However, when \(\Delta t\) = 2 days, the CM-based method surpassed the DL-based method (with 80 training pairs) in terms of bias. Note that as the number of training pairs increases, the bias of the separation results using the DL-based method decreases. We can reasonably anticipate that the DL-based method will achieve lower bias than the CM-based method for all \(\Delta t\) if there are more data available. Next, we consider the bias and SD of the separation errors for a 10 pixel ROI within the tumour region (denoted with \(\Omega_{\text{TM}}\)) in the brain phantom. In general, using more training data yields lower mean values of the NRMSE for \(\Omega_{\text{TM}}\) over all \(\Delta t\); see Figure 11 in Appendix A for details. However, in some instances, the DL-based method with fewer training pairs was able to achieve superior mean values of the NRMSE as shown in Figure 11a,e,f,i. The reason for this is the fact that the proposed network was trained using a global loss function. The evaluation here focuses on a tiny local region at the pixel level, which is highly impacted by the stochastic network optimisation algorithm (Adam) and the random initialization of the learnable parameters. Figure 12 shows that, for each \(\Delta t\), using more training pairs gives lower bias for the DL-based separation for \(\Omega_{\text{TM}}\), while the SD values remain at a similar level due to the aleatoric uncertainty. A longer \(\Delta t\) yields lower bias and SD of the separation results for \(\Omega_{\text{TM}}\) using the CM-based method for both noisy and noise-free cases. Compared to the CM-based method, the DL-based method using 80 training pairs obtained lower bias and SD for \(\Delta t\) \(\leq\) 15 min. However, when \(\Delta t\) = 2 days, the CM-based method achieved even lower bias than the DL-based methods (with 80 training pairs), which again indicates that more training data which includes the case of \(\Delta t\) = 2 days would likely be required to further enhance the separation performance for the tumour ROI. Figure 12: Brain phantom (tumour ROI): bias and SD of separated static \([^{18}\)F]FDG images (**a**) and \([^{11}\)C]MET images (**b**) using DL-based and CM-based methods for the whole brain region \(\Omega_{\text{TM}}\).
## 8 Practical Deployment Considerations
### Hardware Cost and Integration  
Our experiments used commodity hardware, specifically Raspberry Pi 4B units, which cost approximately USD 50 each. These devices support CSI extraction through open-source tools such as Nexmon and require no modification to the physical infrastructure and user behaviour. In future implementations, lower-cost microcontrollers like ESP32 (costing under USD 10) could also be leveraged, enabling further reduction in cost and energy footprint. Since these platforms are already common in IoT ecosystems, integrating our system would not require any additional setup or maintenance.
# A Case Study on the Design and Implementation of a Platform for Hand Rehabilitation
## 5 Results
### Therapist Viewpoint  
Although the work's essence is in presenting the rehabilitation platform's design, one of the crucial parts are the exercises written inside RehabHand. A therapist can write an arbitrary rehabilitation exercise easily in natural notation.  
Figure 4 shows three simple exercises written with RehabHand. Albeit Hand Rehabilitation is a small domain, we have numerous combinations for constructing hand rehabilitation exercises. The first program (Figure 4a) allows the patient to repeat the opening and closing of the hand slowly. This strengthens the muscles of the entire wrist. The following program (Figure 4b) shows the exercise with the fingers' sequential lifting. We start with the index finger and finally finish with the thumb. When all the fingers are lifted, the patient needs to close the whole hand. The third program (Figure 4c) shows the lifting of a combination of fingers. First, we lift the index finger and close it, lift the index and middle fingers simultaneously, close them, and so on. With the RehabHand platform, the therapist can prepare exercises for an individual patient based on their specific needs.  
We can observe the simplicity of RehabHand notation. Actually, no additional specific knowledge is required to write exercises. Therapists write simple commands which they would normally speak to a patient. They can concentrate on motion disabilities for a specific patient and write appropriate, customized exercises. Thus, no programming knowledge is needed to write rehabilitation programs.  
Traditional human-computer interactions using Graphical User Interfaces (GUIs) are subject to frequent changes. For every version or release of an advanced device, a GUI is changed to support features behind the scene. It is the same with software for rehabilitation aids, assistive technology, and robot technology. Consequently, therapists have to adapt each time they switch to a newer rehabilitation device or change the assistive technology provider. In RehabHand, if we support new features or change hardware, the language's basic notation will stay the same. Therapists' main concern is exercise definition, and the exercise will work no matter which hardware or software is used. These tools (compared  
Figure 3: Language processing in RehabHand.  
with the RehabHand), are limited to one type of hardware manufacturer, while RehabHand can support different hardware types from different manufacturers.
## 2 Accelerator
### Accelerator Instrumentation
#### 2.5.2. Orbit  
The stabilization of the electron beam trajectory throughout SwissFEL is crucial for an optimal beam quality: In the accelerator the proper alignment with respect to the accelerating cavities and quadrupoles minimizes transverse wakefield effects and residual dispersion. In the undulator line the BPMs are essential to steer the electron beam on a straight line to keep the overlap between electron and photon beams.  
Beam position monitors [72] (BPMs), based on dual-resonator cavity pickups, measure the beam position in a dipole cavity, while using the signal from a monopole cavity for charge normalization. Injector, linac and transfer line pickups operate at 3.3 GHz with a relatively low loaded quality factor (\(Q_{L}\)) of 40 to resolve the two bunches of the SwissFEL, which are separated by 28 ns when operating both Athos and Aramis simultaneously. The pickups in the undulator line, where only a single bunch is present, work at 4.9 GHz with a higher \(Q_{L}\) of about 1000 to achieve better position resolution. The low-Q pickup signals are converted directly to baseband, the high-Q signals to an intermediate frequency of 135 MHz. After sampling with fast 16-bit analog-to-digital converters (ADCs), the digitized signals are extensively post-processed in field-programmable gate arrays (FPGAs) to correct systematic measurement errors, yielding position readings with submicrometer resolution.  
The signal arising from the monopole cavity is also used to monitor the bunch charge. It is processed and digitized with low noise, resulting in a charge-independent relative resolution well below 0.1% [72]. At very low charges an absolute resolution of a few fC is achieved.  
BPMs at locations with suitable (moderately low) dispersion, e.g., in bunch compressor chicanes near the first and last bending magnets, are also used to measure the electron beam energy.
## 4 System Design
### Application Structure
#### 4.2.1 Client-Side  
The client-side application will handle any aspect linked to a graphical user interface, data input from the user, input validation, providing visual representation for any action that should impact the user, and handling any possible misbehavior of the system. Being the single interaction point with the user, the client side will be responsible for extracting the user editing events and forwarding them to the centralized server-side software. Naturally, the client should visually present the other user interactions with the text resource while facilitating a natural experience of a common text editor.
# Calibration of a Hybrid Machine Tool from the Point of View of Positioning Accuracy
## 2 Materials and Methods
### LinuxCNC and the Machine Calibration Methodology  
The control system of the realized prototype is based on PC architecture and applies a modified kernel of the Linux operating system adapted for calculations of interpolation functions in real-time (real-time Linux) kernel and LinuxCNC 2.9.2 Debian 12 Bookworm RTAI management software. This type of control system was chosen for the prototype because of the open-source license, the possibility of defining non-linear kinematics, and the possibility of compensating systematic errors according to several criteria. The use of LinuxCNC primarily refers to the possibility of compensating for the backlash of the ballscrew spindle and systematic positioning errors according to the unidirectional and bidirectional methods. Error correction is realized by creating a file that introduces compensations for the indicated coordinates of the axis position (internal coordinates, i.e., slider position) in the machine coordinate system, eliminating systematic errors caused by the geometry of the spindle, guides, and assembly of machine elements.  
There are two procedures for calibrating the measuring system: unidirectional, which enables the systematic errors at a certain point to be approximately brought to zero; and bidirectional, which enables separate calibration of the measuring system for movement in the positive and negative directions. The unidirectional method is used to adjust machine tools of low- and medium-accuracy classes. The bidirectional method, an additional function in most control systems, adjusts precision machine tools and coordinates measuring machines.  
Table 1 shows the layout of the parameter file for bidirectional error compensation. The first column of the file represents the coordinates of the internal axis in the machine's coordinate system. In contrast, the other two represent the compensation values of the points when moving in the positive and negative directions. Position coordinates, as well as compensation values, are given in the table in millimeters. During calibration, it is possible to choose whether the compensations are specified as a compensation value or a real coordinate value for a given position.
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
## 4 GTCS Dataset Collection
### Data Collection and Feature Extraction  
We implemented four attack scenarios: botnet, brute force, DDoS, and infiltration. For each attack, we defined a scenario based on the implemented network topology and executed the attacks using the Kali Linux machines that were located in a separate network from the target machines. The attacking machines were Kali 1.1 and Kali 2.0. To perform the botnet attack, we used Zbot [67], a trojan horse malware package that can be run on Microsoft Windows operating systems to execute many malicious tasks. Zbot can mainly be extended through drive-by downloads and different phishing schemes. Because it uses stealth techniques to hide from different security tools, Zbot has become the largest botnet on the Internet [68]. The victim machines in our botnet attack scenario were running Windows 7 and Windows 8. It is important to note that firewalls, Windows Defender, and automatic updating were all disabled on all Windows victim machines to enable a wide spectrum of interesting cases to be captured. The Graphical  
Figure 1: Testbed of GTCS dataset collection system. Network Simulator-3 (GNS3) and Emulated Virtual Experience (EVE) cloud components are network software emulators that allow a combination of virtual and real devices to simulate complex networks. In addition, password complexity checks were not active, and all passwords were set to a minimum of three characters. For the brute force attack, we used the FTP module on the Kali 2.0 Linux machine to attack the machine in the victim network running Ubuntu 16.4. To carry out the DDoS attack, we used the High Orbit Ion Cannon (HOIC) tool [69], a popular free tool for performing DDoS attacks. HOIC works by flooding a target web server with junk HTTP, GET, and POST requests and can open up to 256 simultaneous attack sessions at once. For the infiltration attack, we sent a vulnerable application to the Metasploitable 2 Linux machine on the victim network. We then used the vulnerable application to open a backdoor and perform our infiltration attack. To capture the raw network traffic data (in pcap format), we used Wireshark and tcpdump [70; 71]. Wireshark is a network packet analyzer that can capture network traffic data in as much detail as possible, while tcpdump is a command line utility that helps capture and analyze network traffic. After collecting the raw network packets (i.e., pcap files), we used CICFlowMeter [72; 73] to process those files and extract the features of the network flow packets. CICFlowMeter is an open source tool that generates Bi-flows from pcap files and extracts features from these flows. The extracted features are shown in Table 1. The full dataset is available on ResearchGate for researchers and practitioners [74].
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 3 Selection of Microbes for the Phytoremediation of Metal Contaminated Saline Soils
### Metal Tolerance and Metal Inactivation Capacities
#### 3.2.4 Dark Septate Endophytes  
Though the research interest on dark septate endophytes (DSEs) is still recent, they are nowadays reputed to be resistant to multiple stressors, readily colonizing disturbed ecosystems [142, 143]. DSEs are a very diverse group of ascomycetes characterized by the ability of producing melanin, giving the appearance of brown hyphae [143, 144]. Severalauthors hypothesized that melanin could have a protective role against metal toxicity. [PERSON] et al. [145] demonstrated that the inhibition of the 1,8 dihydroxynaphthalene melanin synthesis by tricyclazole introduced in non-toxic concentrations and under Cd stress led to a reduction of growth and sporulation of the fungi _Exophila pisciplia_, a Cd-tolerant DSE. However, the inhibition of melanin synthesis did not reduce the Cd bioaccumulation. Moreover, according to [PERSON] et al. [146], melanins are molecules that can bind metals because of their chemical nature, i.e., rich in phenols, peptides, carbohydrates, fatty acids, and aliphatic hydrocarbons. It often forms deposits within the cell wall, thanks to chitin [147]. As reviewed by [PERSON] et al. [148], fungal melanins can bind metallic cations. [PERSON] et al. [149] extracted melanin from _Amorphotheca resinae_ and showed good sorption properties toward Cu(II), Pb(II), Cd(II), and Zn(II). The extracted melanins from this latter species and _Aureobasidium pullulans_ were also investigated for their binding properties with Cu\({}^{2+}\)[150]. The authors concluded that melanin could reduce toxicity and have a high affinity for Cu\({}^{2+}\). They suggested that this pigment could be used as a metal biosorbent. Furthermore, DSEs regulate some metal-resistance mechanisms of plants, such as the glutathione system [151]. DSEs can also deploy several PGP capacities. For instance, the study of PGP features of several strains of _Cadophora_, _Leptodontidiium_, _Philaophora_, and _Philaoccephala_ revealed their capacity to produce IAA and volatile organic compounds that were supposed to help plant growth via gaseous exchanges [104]. [PERSON] and [PERSON] [152] evaluated the phytoremediation capacities of five halophyte species, namely _Atriplex halimus_, _A. canescens_, _Sueda fruticosa_, _Marrubium vulgare_ and _Dittrichia viscosa_, from wetlands polluted by Cd, Cu, Zn, Pb, Ni, and Cr. They showed that fungal colonization by AMF and DSE helped to stabilize metals by sequestration in root apoplasm or by spore metal retention. AMF colonization was negatively correlated to the translocation of Cd, while DSE colonization was negatively correlated to the bioaccumulation of Cu and positively correlated to the translocation of Zn. Moreover, in 80% of plant species, an association between AMF and DSE was observed. Another study [153] showed that Louisiana marshes were colonized by AMF and diverse DSE, but DSE colonization was negatively correlated to AMF colonization.
## 2 Model and Parameter Estimation
### Kalman Filtering  
Kalman filtering is the most commonly used algorithm in GNSS positioning. Kalman filtering is based on the state estimation of the previous epoch combined with observations of the current epoch to recursive new state parameters with the state transition matrix. It is mainly divided into two steps: state prediction and parameter update. The prediction model can be expressed as  
\[\left\{\begin{array}{c}\mathbf{\overset{\mathbf{\overset{\mathbf{\overset{ \mathbf{\overset{\mathbf{\overset{\mathbf  
where **y** is the observation minus correction; **G** is the observation design matrix; **K**(\(k\)) the Kalman gain, which can be expressed as  
\[\textbf{K}(k)= \textbf{P}_{\begin{subarray}{c}\star\\ \textbf{X}(k)\end{subarray}}^{-}\textbf{G}^{T}(k)\Bigg{[}\textbf{G}(k)\textbf{ P}_{\begin{subarray}{c}\star\\ \textbf{X}(k)\end{subarray}}^{-}\textbf{G}^{T}(k)+\textbf{R}(k)\Bigg{]}^{-1} \tag{10}\]
# CPU-GPU-Memory DVFS for Power-Efficient MPSoC in Mobile Cyber Physical Systems
## 4 System Model and Problem Formulation
### Problem Formulation  
In this subsection, we define the problem formulation on which our proposed method was based.  
**Given**: Let us consider a system that has a set of applications, \(S_{App}=\{App_{1},App_{2},\ldots\)\(App_{i}\}\), where \(App_{i}\) is the \(i\)th application and \(App_{i}\) consists of a set of tasks, \(S_{task}=\{tsk_{1},tsk_{2},\ldots tsk_{i}\}\), where \(S_{task}\) always generates a fixed performance output \(Prf_{i}\) for the fixed DVFS configuration values \(R_{i}\) while executing \(App_{i}\) on the system. Here, \(R_{i}\) consists of the combination of the DVFS values for big CPUs (\(DVFS_{b_{i}}\)), LITTLE CPUs (\(DVFS_{L_{i}}\)), GPUs (\(DVFS_{g_{i}}\)) and memory (\(DVFS_{m_{i}}\)) such that \(R_{i}=<DVFS_{b_{i}}\), \(DVFS_{L_{i}}\), \(DVFS_{g_{i}}\), \(DVFS_{m_{i}}>\) leads to a fixed performance output \(Prf_{i}\). Now, we can consider \(Prf_{desired}\) as the desired value of the performance output for the execution of \(App_{i}\).  
**Find**: The desired DVFS configuration values (\(R_{desired}\)) are the combination of the desired DVFS values for big CPUs (\(DVFS_{b_{desired}}\)), LITTLE CPUs (\(DVFS_{L_{desired}}\)), GPUs (\(DVFS_{g_{desired}}\)) and memory (\(DVFS_{m_{desired}}\)).  
**Subject to**: Meeting the desired performance \(Prf_{desired}\) while consuming the least power (\(P_{least}\)) during the execution of \(App_{i}\) on \(R_{desired}\).