esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 4 Results and Analysis
### First Responders with Waypoint Mobility
For ease of data representation, we named each sub-scenario FR1, FR2, etc. and these short names are mapped to the full scenario names in Table 3.  
We have analyzed the MOS values of each sub-scenario. Among the six sub-scenarios, FR1 is the best performing scenario with the integration of IEEE 802.11g, 802.11s and G.711. FR5 which uses the IEEE 802.11n, 802.11e standards, and G.711 codec is the worst as compared to other sub-scenarios, as shown in Figure 6a for VoIP Initiators and in Figure 6b for VoIP receivers, respectively. The analysis shows that FR5 sub-scenario performance degradation is due to the 802.11n and IEEE 802.11e standards integration with the G.711 codec and AODV since FR6, which also uses same configuration parameter except the G.729 codec, is also not performing better as compared to other sub-scenarios that use the IEEE 802.11g standard. From the analysis of the first responders' scenario, we can conclude that in sub-scenarios where the IEEE 802.11g standard is integrated with IEEE 802.11s, the MOS values show increased VoIP performance as compared to other sub-scenarios, while sub-scenarios with IEEE 802.11n show decreased VoIP performance. Figure 5: First responders with waypoint mobility. The flags with dotted lines show the positions to which the nodes move, the dotted lines connected to a cloud show the IPv4 subnet association. The solid lines show VoIP peers.
# Thorium Recovery with Crown Ether-Polymer Composite Membranes
## 3 Results and Discussion
### The Process Performance of the Prepared Membranes
#### 3.2.4 The Proposed Mechanism of Thorium Ion Retention  
The results obtained in this study show that the sPEEK and ABCE-sPEEK membranes retain the thorium ion through specific superficial interactions followed by ionic expulsion (Figure 13).  
At a working pH, chosen between 0 and 4 units, the ionizing-complexing chemical species interact differently with the Th\({}^{4+}\) ion (Table 4).  
The choice of sPEEK membrane was made for comparison with the newly prepared composite membrane ABCE-sPEEK (Figure 13). The reference sPEEK membrane retains the thorium ion due to its strong acidity (pKa = 2) that manifests as such in the acidic environment) with pH 0 to pH 2, and as sulfonated anion at pH 2 to 4. The thorium ion retention occurs at the surface of the sPEEK membrane (Figure 13a,c,e). This retention of the thorium ion does not completely annihilate the 4+ charge. A large part of the thorium ion accumulates on the surface, which is justified by the ionic expulsion from the nanofiltration process [65; 66; 67; 68; 69; 70; 71; 72; 73].  
Figure 11: Dependence of retention (R%) on operating time: sPEEK membrane (in orange) and ABCE-sPEEK composite membrane (in blue).  
Figure 12: Dependence of the flux (J) on operating time: sPEEK membrane (in orange) and ABCE-sPEEK composite membrane (in blue).  
In the case of the ABCE-sPEEK composite membrane (Figure 13b,d,f), it was desired to combine the effect of the sulfonic groups of sPEEK with that of ABCE. The choice of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Membrane**} & \multicolumn{5}{c}{**pH (Solution of Th\({}^{4+}\))**} \\ \cline{2-7}  & & **0** & **1** & **2** & **3** & **4** \\ \hline \multirow{3}{*}{The ionizing-complexing groups} & sPEEK & R1-SO\({}_{3}\)H & R1-SO\({}_{3}\)H & R1-SO\({}_{3}\)\({}^{-}\) & R1-SO\({}_{3}\)\({}^{-}\) & R1-SO\({}_{3}\)\({}^{-}\) \\ \cline{2-7}  & ABCE-sPEEK & R2-NH\({}_{3}\)\({}^{+}\) & R2-NH\({}_{3}\)\({}^{+}\) & R2-NH\({}_{3}\)\({}^{+}\) & R2-NH\({}_{3}\)\({}^{+}\) & R2-NH\({}_{2}\) \\ \hline \hline \end{tabular}  
* R1: PEEK; R2: Benzo-15 CS–Ether.  
\end{table}
Table 4: The ionizing-complexing groups of the prepared membranes depending on the pH of the aqueous solution.  
Figure 13: Schematic presentation of the proposed mechanism of thorium ion retention on sPEEK (**a,c,e**) and ABCE-sPEEK (**b,d,f**) membranes: (**a,b**) before nanofiltration; (**c,d**) beginning of nanofiltration; and (**e,f**) ionic expulsion nanofiltration.  
crown ether is justified because the ionic radius of the thorium ion (r = 179 pm) is similar to that of sodium (r = 180 pm) with which this ether specifically complexes (Figure 13b).
## 3 Results and Discussion
### ITS Sequence Analysis  
ITS rDNA sequencing was used as a valuable marker for fungal species identification and classification to confirm the morphological characteristics [29]. The ITS-specific primers amplified one fragment (500-600 bp long) from the DNA of all examined isolates. The purified amplicons were sequenced. The obtained nucleotide sequences were aligned using the BLAST tool at NCBI site and revealed that all the isolates were _A. alternata_, with homology ranging from 99% to 100%. The annotated sequences were accessioned with numbers MN592771-MN592777 (Table 1).
###### Abstract  
The chaperone (or chaperoning) system (CS) constitutes molecular chaperones, co-chaperones, and chaperone co-factors, interactors and receptors, and its canonical role is protein quality control. A malfunction of the CS may cause diseases, known as the chaperonopathies. These are caused by qualitatively and/or quantitatively abnormal molecular chaperones. Since the CS is ubiquitous, chaperonopathies are systemic, affecting various tissues and organs, playing an etiologic-pathogenic role in diverse conditions. In this review, we focus on chaperonopathies involved in the pathogenic mechanisms of diseases of the central and peripheral nervous systems: the neurochaperonopathies (NCPs). Genetic NCPs are linked to pathogenic variants of chaperone genes encoding, for example, the small Hsp, Hsp10, Hsp40, Hsp60, and CCT-BBS (chaperon-containing TCP-1: Bardet-Bied syndrome) chaperones. Instead, the acquired NCPs are associated with multifunctional chaperones, such as Hsp70, Hsp90, and VCP/p97 with aberrant post-translational modifications. Awareness of the chaperonopathies as the underlying primary or secondary causes of disease will improve diagnosis and patient management and open the possibility of investigating and developing chaperonotherapy, namely treatment with the abnormal chaperone as the main target. Positive chaperonotherapy would apply in chaperonopathies by defect, i.e., chaperone insufficiency, and consist of chaperone replacement or boosting, whereas negative chaperonotherapy would be pertinent when a chaperone actively participates in the initiation and progression of the disease and must be blocked and eliminated.  
chaperone system; molecular chaperones; chaperopathies; nervous system; neurochaperonopathies; Hsps; neurodegeneration; neuromuscular disorders; chaperonotherapy  
2021  
The Neurochaperonopathies: Anomalies of the Chaperone System with Pathogenic Effects in Neurodegenerative and Neuromuscular Disorders  
11
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
## 1 Introduction
### Related Work
#### 1.3.1 Enhancing the Resilience of WDM Networks against Geographical Failures
In [19], the authors introduced two polynomial-time algorithms designed to choose an optimal pair of link-disjoint lightpaths between two network nodes. The objective was to maximize their minimum spatial distance (MSD), minimize the path length of the primary lightpath, and ensure that the backup lightpath maintains a specific MSD from the primary lightpath. This optimization was performed while disregarding safe regions around the source and destination nodes. Furthermore, the authors of [20] introduced a novel design framework known as geometric network augmentation (GNA). GNA identifies specific node pairs and recommends new cable routes to be implemented between each pair. This proactive approach ensures continuous network connectivity in the event of a regional failure of a specified magnitude.
# Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT
## 3 Federated Learning-Based Resource Management with Blockchain Trust Assurance Mechanism  
### Federated Learning for Resource Management  
Traditionally, computing tasks for various applications are performed in cloud servers in the core network. This centralized approach is simple to manage resources, but the requirement pressures on computer and communication resources are very high. In the current Mobile Edge Computing (MEC)-based 5G network, a portion of the computingtasks are offloaded to the network edge. The benefit is that it greatly reduces the pressure on core cloud servers and also greatly relieves the transmission pressure on the communication network, while reducing the processing latency of applications [13; 16]. This is an edge cloud operation approach. With the greatly enhanced computing and storage capacity of terminal devices, in the future, a portion of computing tasks will most likely be localized and processed at the devices. This localized operation method can achieve lower application processing latency and reduce the usage demand for communication resources while protecting local data privacy.  
FL is a distributed ML algorithm. The generated data are directly trained locally to obtain the local models, and all local models parameters are uploaded to the FL server for global model updating, as shown in Figure 1. The updated global model is sent to the devices involved in the model training for subsequent local model updating. The interaction continues until the global model is trained with the required accuracy and reliability. Since the local data on the devices do not need to be uploaded to the network, the privacy of the local data can be ensured.  
In Figure 1, we consider the presence of malicious nodes. If the local node participating in FL is malicious, it would obtain a wrong aggregated global model by uploading wrong local model parameters to an FL server, and this would further affect all the local nodes participating in FL training. Therefore, timely and accurate detection and removal of malicious nodes before performing FL is very important for FL-based resource management mechanisms in IoT.
# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 6 Ovarian Pathologies
### Ct  
Contrast-enhanced CT is performed for the preoperative staging in patients with ovarian tumours, providing information on the primary mass and presence of distant metastases. [PERSON] et al. [148] proposed a nomogram with combined radiomics and conventional contrast-enhanced CT features for the preoperative classification between serous and mucinous cystadenomas with an AUC of 0.92 in the external validation cohort. Using non-contrast CT images, [PERSON] et al. [149] constructed and externally validated a radiomics model and nomogram with good-to-excellent performance (AUC of 0.83 and 0.95, respectively) for the differential diagnosis between benign and malignant ovarian tumours. A large study including 1329 patients with ovarian tumours provided an AUC of 0.91 of the machine-learning-based radiomics model for the differentiation between the benign and malignant tumours on contrast-enhanced CT [150]. Furthermore, a multicentric study involving 665 patients from four centres reported an AUC of 0.836 for differentiating high-grade and non-high-grade serous carcinoma [151].  
CT-based radiomics can also be used to predict overall survival and progression-free survival in patients with ovarian cancer [152; 153]. In this setting, a multicentric study performed by [PERSON] et al. [154] demonstrated good accuracy of the radiomics signature on preoperative contrast-enhanced CT and nomogram for the prediction of 18-month and 3-year recurrence risks in patients with advanced high-grade serous ovarian cancer. [PERSON] et al. [155] recently validated a radiomics prognostic vector, which was independently associated with progression-free survival, in an independent cohort of high-grade serous ovarian cancer imaged with contrast-enhanced CT. Radiomics was also able to predict response to neoadjuvant treatment in patients with high-grade serous ovarian carcinoma based on the segmentation of mental tumour deposits [156].
# A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection
## 4 Experimental Evaluation
### The Experimental Data  
In order to facilitate an experimental performance comparison with similar studies, the system call datasets UNM_sendmail and UNM_live_lpr published by the New Mexico university are used in this section [70]. The UNM_sendmail data set consists of 346 normal traces and 25 abnormal traces. The abnormal data contains sunsendmailcp (sccp) intrusions and decode intrusions, in which the sccp intrusions enable the local user to obtain the root access by using special command options to make sendmail attach an e-mail message to a file, and the decode intrusions enable remote users to make changes to certain files on the local system. Data for UNM_live_lpr data set includes 15 months of activity and consists of 4298 normal tracks and 1003 abnormal tracks. The abnormal data contains lprcp symbolic link intrusions that take advantage of the vulnerability of the lpr program to control the files on the host computer and tamper with the contents of the files.  
The trace consists of a sequence of system calls in chronological order. The meaning of trace varies from program to program. Each trace file lists pairs of numbers, the first number represents the process identity (PID) of the execution process, and the second number represents the specific system call (SC). The child processes forked by the parent process are traced individually. We take UNM_sendmail as an example to show the specific format of experimental data as follows:  
\[8840\;4,\;8840\;2,\;8840\;5,\;8840\;66,\;8840\;5,\cdots,8840\;6\] \[8843\;115,\;8843\;15,\;8843\;99,\;8843\;120,\;8843\;17,\cdots, \;8843\;12,\] \[6545\;2,\;6545\;5,\cdots,\;6545\;2,\;6545\;2,\;6545\;2\]  
The data consists of different data units. Each data unit consists of PID and SC. In the experiment, data is segmented according to the PID number and the SCs after the same PID are arranged together in chronological order. In the above data, the numbers \(8840,8843\) and \(6545\) are PIDs and the number 4,2, 5, 66,..., 2 are SCs. The lists of system calls issued by 8840, 8843 and 6545 are denoted as R\({}_{8840}\) = (4, 2, 5, 66, 5,..., 6), R\({}_{8843}\) = (115, 15, 99, 120,..., 17) and R\({}_{6545}\) = (2, 55,..., 2) respectively.  
The vector form of a system call sequence composed of the frequency of the short system call sequences. Given \(n\) is the number of traces, m is the total number of short system call sequences. Then the \(i\)th element in the vector is the frequency at which the short system sequence numbered \(i\) occurs in the system call sequence of the process. The vector corresponding to the \(j\)th trace, represented by the frequency of short system call sequence, is denoted by \(\left(f_{1j},\cdots,f_{mj}\right)\). That is to say, samples are represented as \(\left(X_{j},y_{j}\right)j=1,\cdots,n\), where \(X_{j}=\left(f_{1j},\cdots,f_{mj}\right)\), \(y_{j}\in\left(+1,-1\right)\).
# The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals
## 3 The Implementation of Recognition Algorithm and the Experimental Analysis
### Experiment Analysis of Actual Data
#### 3.2.1 The Detection and Identification Results of Mechanical Signals  
In the figures of detection results, the black dots represent the optical fiber vibration signals. FFT is applied to the identification data extracted from the detection results, and the sum of the FFT results of different frequency bands is compared with the recognition threshold. The intrusion type of signals is judged as the identification result. In the figures of the identification results, black circles represent mechanical signals, red squares represent walking signals, and green triangles represent picking signals.  
The detection results of mechanical signals are as Figures 11 and 12:Figure 11: The detection results of mechanical drilling signals. (**a**) The first group of mechanical drilling signals; (**b**) The second group of mechanical drilling signals; (**c**) The third group of mechanical drilling signals.  
Figure 12: The detection results of mechanical picking signals. (**a**) The first group of mechanical picking signals; (**b**) The second group of mechanical picking signals; (**c**) The third group of mechanical picking signals.  
Figure 13: The identification results of mechanical drilling signals. (**a**) The first group of mechanical drilling signals; (**b**) The second group of mechanical drilling signals; (**c**) The third group of mechanical drilling signals.  
Figure 14: The identification results of mechanical picking signals. (**a**) The first group of mechanical picking signals; (**b**) The second group of mechanical picking signals; (**c**) The third group of mechanical picking signals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Picking** & **Mechanical** & **Walking** \\ \hline Mechanical drilling1 & 14 & 93 & 8 \\ Mechanical drilling2 & 30 & 107 & 10 \\ Mechanical drilling3 & 0 & 210 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistical identification results of Mechanical drilling signals.  
As shown in Figure 15, the reason why the mechanical drilling signal is recognized as picking signal is that the feature extracted by FFT has strong energy from 50 Hz to 64 Hz, but there is an obvious peak value at 12 Hz resulting in a small ratio of 0.7737 much below the threshold. As shown in Figure 16, the reason why the mechanical drilling signal is recognized as walking signal is that the characteristics of mechanical drilling are not obvious where there is no remarkable high frequency component and the low frequency component is large.
# Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies
## 3 MAT Solutions for PVI Shopping
### Navigation and Product Identification
#### 3.2.2 Computer Vision Based Systems  
CV based systems accept visual inputs from the camera and use CV techniques to extract valuable information and recognize objects in the surrounding environment. Finally, they provide information to the PVI through tactile or auditory channels [74]. Researchers classified CV based systems into tag based and non-tag based. In tag based systems, unique visual tags such as QR code, Barcode, and AR markers are placed on products to aid the recognition process. Recognition is accomplished by capturing an image of the tag and analyzing this image to determine the identity of the object based on its tag information. Then, they use a database to retrieve product details, such as name, price, and special offers [42; 75; 76]. Other solutions use these tags for navigation and giving the shortest route to products [77; 78]. Finally, tactile or voice commands are used for warnings and providing direction commands to the PVI [79].  
With non-tag based systems, developers do not attach tags to objects. They use CV techniques to analyze the images and identify objects [80; 81; 82]. Non-tag systems require extensive computational power to analyze images and give accurate results [44]. For example, [PERSON] et al. used smartphones to accurately locate and identify objects using CV techniques [41; 83]. They also used a glove with a camera, which guides hand movements to point and grasp things. [PERSON] and [PERSON] used a color recognition module with a convolution neural network to recognize objects and their colors [84; 85]. [PERSON] and co-workers processed the depth and motion tracking data obtained via a Google Tango Tablet to create and update a 3D box around the PVI to detect and avoid obstacles [43; 86]. [PERSON] and co-workers utilized color images, depth images, and accelerometer information from a mobile Kinect and transferred them to laptop for processing and detecting obstacles [87]. Concerning the obstacle warning module, a tactile-visual substitution system uses voice commands to warn the PVI to avoid obstacles.
## 2 Materials and Methods
### Mathematical Model
#### 2.2.3 Mathematical Description of Thermal and Electromagnetic Phenomena in Thin Films  
Four thin-layer objects were in the model: TE thin films of \(Sb\) and \(Bi_{88}Sb_{12}\), and two copper films. The thickness of all films was 150 nm. In order to reduce the model size and computational time, analytical expressions were used to described thin objects.  
In electromagnetic interfaces, a dielectric loss model is required. For this purpose, a transition boundary condition was used:  
\[\mathrm{J}_{\mathrm{s,up}}=\frac{Z_{s}\mathrm{E}_{t.up}-Z_{t}\mathrm{E}_{t. down}}{Z_{s}^{2}-Z_{t}^{2}}, \tag{8}\]\[\mathrm{J_{s.down}}=\frac{Z_{s}\mathrm{E_{\mathit{t.down}}}-Z_{t}\mathrm{E_{ \mathit{t.up}}}}{Z_{s}^{2}-Z_{t}^{2}}, \tag{9}\]  
\[Z_{s}=\frac{-i\omega\mathrm{\mu}}{k}\frac{1}{tan(kd)}, \tag{10}\]  
\[Z_{t}=\frac{-i\omega\mathrm{\mu}}{k}\frac{1}{sin(kd)}, \tag{11}\]  
\[k=\omega\sqrt{\epsilon_{r}+(\sigma/(i\omega)))\mathrm{\mu_{r}}}, \tag{12}\]  
where indices \"\(up\)\" and \"\(down\)\" correspond to upsides and downsides of the layer, \(Z_{s}\)--surface impedance, \(Z_{t}\)--tangential impedance, \(J_{s}\)--surface current density, and \(\mathrm{E_{\mathit{t}}}\)--tangential electric field.  
In the heat transfer interface, a thin layer description was used in order to describe heat transfer in thin objects:  
\[\
abla_{t}\mathrm{q_{s}}=d_{s}Q_{s}+q_{0}, \tag{13}\]  
\[\mathrm{q_{s}}=-d_{s}\kappa\
abla_{t}T, \tag{14}\]  
where \(d_{s}\) is film thickness, \(Q_{s}\) is a density-distributed heat source, \(q_{0}\)--heat flux received out-of-plane, \(q_{s}\)--heat flux in the film, and \(\
abla_{t}\) stands for tangential derivative.  
Thermally thin approximation model was used for the layer description. This model is valid for films with high thermal conductivity, or conductivity which is higher than the surroundings.