esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 3 Identification of CSC
### Marker-Based Isolation of CSCs  
#### 3.1.1 CD44  
High expression of the hyaluronic acid receptor CD44 is one of the most common markers for the isolation and enrichment of CSCs in solid cancers, including HNSCC (Figure 2) [61]. The CD44 receptor is a type I transmembrane glycoprotein with a critical role in cell adhesion, migration, proliferation, and angiogenesis [62]. The extracellular segment of CD44 contains a ligand-binding site that primarily binds hyaluronic acid (HA), but it can engage with various other constituents of the extracellular matrix such as osteepontin, integrin, fibronectin, matrix metalloproteinases, and laminin. By working as a signaling platform, CD44 facilitates the activation of multiple cancer-related signaling pathways involved in cell proliferation and survival, such as the epidermal growth factor receptor (EGFR), Src/focal adhesion kinase (FAK), and the hepatocyte growth factor receptor (MET) [62].  
Increasing evidence suggests that overexpression of CD44 is associated with various aspects of HNSCC tumorigenesis, including proliferation, migration, angiogenesis, epithelial-mesenchymal transition (EMT), and therapy resistance [63; 64; 65; 66]. In addition, several studies demonstrated that CD44 expression is associated with poorly differentiated tumors, regional lymph node metastasis, local recurrence, and a reduced overall survival rate in oral squamous cell carcinoma (OSCC), emphasizing its crucial role in tumor recurrence and metastasis in HNSCC tumors [67].  
Accumulating data indicate that CD44 has a key role in the regulation of CSC features such as tumor initiation, self-renewal, and resistance to chemo- and radiotherapy [68]. [PERSON] and colleagues showed that in OSCC, CSC subpopulations expressing high levels of CD44 exhibit increased phenotypic plasticity that allows a sequential shift between EMT and mesenchymal-epithelial transition (MET) activities and ultimately underlies enhanced therapeutic resistance [69; 70]. However, the _CD44_ gene is regularly subjected to alternative splicing which produces both the standard isoform (CD44s) and several variant isoforms (CD44v), which might play a distinct biological role [71]. For instance, the CD44v3 isoform is highly expressed in CSCs and correlates with HNSCC progression [72; 73]. Furthermore, [PERSON] et al. demonstrated that CD44 plays a pivotal role in promoting stemness and the development of HNSCC CSCs. By using HNSCC cell lines, a xenograft model, and patient tumor samples, they showed that the ERK1/2-Nanog signaling pathway is critical for the maintenance of CSC stemness and tumorigenic capacity by upregulating CD44 expression, and therefore, targeting this pathway may prevent or reverse CSC phenotypes that drive tumor progression and metastasis in HNSCC [46].
## 3 Y-Branch Coupler  
Using the Y-branch coupler is an effective way to improve the coupling efficiency between the single-mode fiber (SMF) and waveguide. In this section, firstly, structure of the Y-branch coupler is introduced and its key parameters are optimized to get the maximum coupling efficiency. Secondly,  
Figure 1: Structure of the waveguide reference wavefront source (RWS). Part A\({}_{1}\) or A\({}_{2}\) is a fiber-to-waveguide coupler. Part B and D are straight waveguides. Part C is a bent waveguide.  
Figure 2: Accuracy of the reference wavefront generated by waveguide RWS when numerical aperture (NA) is (**a**) 0.3 or (**b**) 0.5.  
to quantitatively evaluate the fabrication difficulty and alignment difficulty of the coupler, 3 dB fabrication tolerance, 3 dB alignment tolerance, and polarization-dependent loss are analyzed, respectively.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 5 Conclusions  
Our study aimed to build a framework to support queue simulation and performance testing of systems processing EDI messages within a supply chain organisation. Our framework allows for a methodical approach to model queueing systems. Developers and performance engineers can use our framework to identify message elements that contribute to bursty and non-bursty messages by looking at different EDI parameters, which will help determine the impact of such burst on a queueing system. Implementors deploying an AmI service within smart spaces can use our framework to determine under or over-provisioning of resources based on the results of the applied model selection tests. Data scientists and researchers can apply our framework to model parametric and non-parametric distributions. Companies can use our research to help inter-twine existing technology with smart modelling. Our framework provides a helping hand when starting to model these EDI messages. Applying context awareness to our EDI feature selection improves knowledge sharing between Supply Chain processes and their impact on the network and within a Supply Chain organisation. We note, in particular, that our data suffered from quantisation noise, which we believe may be a common challenge in the modelling of queueing data with short-lived jobs.  
A number of areas remain open for further study. Further exploration of correlation between messages could be investigated. This might be advanced by looking at other elements of EDI message attributes. This might also improve the modelling of the head of the distributions, though resolving issues around quantised data may also help significantly. In subsequent work, we will investigate the effects of quantisation and correlation that were observed in some parts of this dataset.  
**Author Contributions:** Investigation, Analysis and Writing, S.L.; Supervision, Analysis and Writing, J.D. and D.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare on conflict of interest.
# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 3 Deep Neural Network and Entropy to Multiple PUE Detection in Cooperative MCRN
### General Proposal for PUE Attack Detection  
Our proposal starts with the entropy calculation of the energy detector results, which achieves better results for low SNR values and energy by itself on each SU. After that, a DNN is used and divided into two parts. In the first one, the PUE attacker is placed at different points of a mesh around the SU of the network; the collected data is used for the learning process of the DNN algorithm, which will be explained in the following subsection. Each SU has the results of the learning stage in its programming. In the second part, the PUE is positioned anywhere, and the decision results of each SU are sent to the FC, which makes the global decision if there is a _PUE attack_ presence based on an OR rule. There are other rules, such as the AND or MAJORITY rule, but depending on the position of the SU and the PUE attacker, there is a high probability that only one SU will be able to detect the attack. The rule that allows better detection is the OR rule; if any of the SU detects the _PUE attack_, it will be transmitted to the entire network. An example will be shown in Section 5.2. The global decision is sent in a broadcast message to all the SU in the CSS scheme. The general model for the proposed PUE detection can be seen in Figure 3.  
The model consists of a malicious _PUE attack_ generated in the MCRN environment and some SUs that use energy and entropy methods to detect it, distributed on the MCRN. Depending on the position of a SU, it can detect or not the _PUE attack_; that is why the CSS needs to have a vision of the MCRN within its coverage limits. The malicious _PUE attack_ does not send signals in uplink frequency (UL), only in downlink (DL) in the MCRN.  
The first stage of the detection system is to analyze if a PU, a SU, or a MU is detected in the radio environment to protect the PU. However, to detect if there is a PU or a MU, the UL and DL signals are estimated. If there is a PU phone call, the two signals must be active, but if there is a MU, only a DL/UL signal will be detected. For the test, we use the absolute radio frequency channel number (ARFCN) and record the combination of UL/DL frequency pairs available for the phone call. If a DL signal is detected, the system detects the UL signal. If only the DL is detected by the system, it will be marked as a MU.
## Appendix B  
As shown in Figure 6, the gas-ejection of the ejection point is along normal line of the material trajectory, where the elevation angle of gas-ejection is \(\theta\), and the line speed of belt conveyor is \(v\). Supposing that \(t_{e}\) is the time from material off the belt to ejection point, \(P_{e}\) (\(y_{e}\), \(z_{e}\)) is the ejection point and its coordinates, \(P_{0}\) (\(y_{0}\), \(H_{b2l}\)) is the free parabolic falling point and its coordinates, \(P_{n}\) (\(y_{n}\), \(H_{b2l}\)) is the preset falling point and its coordinates, and \(g\) is acceleration of gravity, then there are:  
\[y_{e}=vt_{e},z_{e}=\frac{gt_{e}^{2}}{2}\] (A13)  
\[\tan\theta=\frac{v}{gt_{e}}\Rightarrow t_{e}=\frac{v}{g\tan\theta}\] (A14)  
\[\cos\theta=\frac{1}{\sqrt{1+\tan^{2}\theta}}=\frac{gt_{e}}{\sqrt{g^{2}t_{e}^{ 2}+v^{2}}}\] (A15)  
\[\sin\theta=\cos\theta\tan\theta=\frac{v}{\sqrt{g^{2}t_{e}^{2}+v^{2}}}\] (A16)  
where \(H_{b2l}\) is the vertical height of belt to falling point, \(t_{0}\) is the free parabolic falling time. Then there are:  
\[H_{b2l}=\frac{1}{2}gt_{0}^{2}\Rightarrow t_{0}=\sqrt{\frac{2H_{b2l}}{g}}\] (A17)  
\[y_{0}=vt_{0}=v\sqrt{\frac{2H_{b2l}}{g}}\] (A18)  
The velocity \(v_{ey}\) in Y-direction and the velocity \(v_{ez}\) in Z-direction before the gas ejection at gas-ejection point have the following equation:  
\[v_{ey}=v,v_{ez}=gt_{e}\] (A19)  
If the gas-ejection impulse \(I_{e}=F_{eM}\times\Delta t\), \(F_{eM}\) is the average force of gas-ejection, \(\Delta t\) is the action time of gas-ejection and \(m\) is mass of the material, then, according to the momentum theorem, the normal velocity after ejection at the gas-ejection point is:  
\[I_{e}=F_{eM}\Delta t=mv_{en}\Rightarrow v_{en}=\frac{I_{e}}{m}\] (A20)  
The velocity \(v_{pyy}\) in Y-direction and the velocity \(v_{pez}\) in Z-direction after the gas ejection at gas-ejection point have the following equations:  
\[\left\{ \begin{array}{l}v_{pyy}=v_{en}\cos\theta+v_{ey}\\ v_{pez}=v_{ez}-v_{en}\sin\theta\end{array} \right.\] (A21)  
Placing Equations (A14)\(-\)(A16), (A19) and (A20) into Equation (A21), we obtain  
\[\left\{ \begin{array}{l}v_{pyy}=\frac{gt_{e}I_{e}}{m\sqrt{g^{2}t_{e}^{2}+v^{ 2}}}+v\\ v_{pez}=gt_{e}-\frac{gt_{e}}{m\sqrt{g^{2}t_{e}^{2}+v^{2}}}\end{array} \right.\] (A22)  
Supposing that the time from the material off the belt to landing (falling point) is \(t_{n}\), then there are trajectory equations:  
\[\left\{ \begin{array}{l}y=vt\\ z=\frac{1}{2}gt_{2}\end{array} \right.,t\in[0,t_{e}]\] (A23)  
\[\left\{ \begin{array}{l}y=v_{pyy}(t-t_{e})+y_{e}\\ z=v_{pez}(t-t_{e})+\frac{1}{2}g(t-t_{e})2+z_{e}\end{array} \right.,t\in[t_{e},t _{n}]\] (A24)Placing Equation (A22) into Equation (A24), we obtain:  
\[\left\{ \begin{array}{l}y=(\frac{gt_{e}I_{e}}{m\sqrt{g^{2}t_{e}^{2}+v^{2}}}+v)(t -t_{e})+vt_{e}\\ z=(gt_{e}-\frac{vt_{e}}{m\sqrt{g^{2}t_{e}^{2}+v^{2}}})(t-t_{e})+\frac{1}{2}g(t -t_{e})2+\frac{gt_{e}^{2}}{2}\end{array} ,t\in[t_{e},t_{n}]\right.\] (A25)  
Equations (A23) and (A25) constitute the trajectory equations of material off belt to falling point.
# Medical Devices for Tremor Suppression: Current Status and Future Directions
## 4 Wearable Orthoses  
The first reported mechanical solution for the suppression of hand tremors was focused on clasping the patient's arm to prevent involuntary spasms, patented by [PERSON] and [PERSON] in 1980 [87]. However, this approach was not developed further. In 1998, the Viscous Beam orthosis [88] was developed based on previously established principles of energy dissipation [45; 46; 48] to suppress tremor along the wrist flexion/extension. This device showed success, demonstrating that energy dissipation could be employed in an orthosis. However, it was limited by the fixed damping rate, leading to inconsistent tremor suppression [88].  
Tremor suppression orthoses for the upper limbs, wrist, and elbow joints are classified into active, semi-active, or passive. Active orthoses work by generating an active force that counteracts the involuntary motions while supporting the voluntary motions in patients with tremors. In contrast, semi-active and passive orthoses leverage energy dissipation or absorption to suppress involuntary movements. Unlike passive orthoses, the damping magnitude of semi-active orthoses can be adjusted by an active controller. Tremelo (Five Microns, Fresno, CA, USA), Steadi-One (Steadiwear, Toronto, ON, Canada), and Readi-Steadi (Readi-Steadi, Gonzales, LA, USA) are the three passive orthoses currently available for use in patients with tremor. Both Tremelo and Steadi-One are FDA-registered, while Readi-Steadi is FDA-exempted. Active and semi-active orthoses are currently being researched but are not clinically available.
# Research and Evaluation Method for Echelon Usage of Double-Life Equipment Considering Calendar Life
## 3 Analysis and Solution
### When the Number of Units Used Each Time Is Less Than Total: \(M<n\)
#### 3.2.3 Calculate the Optimal Distribution of Working Life  
There are many kinds of equipment life distributions that meet the two conditions of no working life waste and enough equipment use at the same time. However, under the same service conditions, the changes of the sum of the working life of all of the equipment are the same, and the optimal distribution curve of the working life is in these conditions.  
By analyzing how the maximum life curve of the working life is affected by the monthly usage (_Ty_) and the number of units used (_M_), it can be seen that when the monthly usage (_Ty_) decreases or the number of units used (_M_) increases, the controllable range becomes smaller, which will cause the equipment to move beyond the controllable range. The controllable range changes are shown in Figure 4.  
The maximum sum of the working life chart affected by monthly usage and number of units can be drawn as in Figure 4.  
By observing Figure 5, it can be seen that under the same working life and conditions, the sum of working life at any time should be lower than the value of \(M=N\) curve, and number of units used \(M=N\) also includes another requirement, \(\frac{\textit{Twl}_{i}}{\textit{Tcl}_{i}}\geq\frac{\textit{Twl}_{i-1}}{\textit {Tcl}_{i-1}}\). Therefore, the minimum working life affected by external indicators will meet:  
\[\frac{\textit{Twl}_{i}}{\textit{Tcl}_{i}}\geq\frac{\textit{Twl}_{i-1}}{\textit {Tcl}_{i-1}} \tag{3}\]  
Figure 4: Change in monthly usage or the number of units used.  
Figure 5: Maximum sum of working life.  
Enough equipment should be used in the month to meet the following conditions:  
\[AVTwl_{1}+NVTwl_{1}\geq J^{1}\times Ty\]  
It can be seen that under the same working life and conditions, the greater the number of units used, the more limited they become. So, set \(M_{0}=N\), \(J^{1}=N\). \(NVTwl_{j^{1}}\) are taken as fixed values, and the following formula is obtained:  
\[\left\{\begin{array}{l}AVTwl_{j^{1}}=\sum\limits_{i=1}^{N}Min(Tcl_{i}\times \frac{Ty}{N},Twl_{i})\\ \sum\limits_{i=1}^{N}Tcl_{i}\times\frac{Ty}{N}=N\times Ty-NVTwl_{N}\end{array}\right.\]  
Further, the following formula can be obtained:  
\[Tcl_{i}\times\frac{Ty}{N}\leq Twl_{i}\]  
Different values can be taken for the monthly usage, such as \(Ty_{1}\leq Ty_{2}\leq Ty_{\max}\). Set \(Ty=Ty_{\max}\):  
\[Tcl_{i}\times\frac{Ty_{\max}}{N}=Twl_{i}\]  
Further, we can obtain the formula:  
\[\frac{Twl_{i}}{Tcl_{i}}=\frac{Twl_{i-1}}{Tcl_{i-1}}=\frac{Ty_{\max}}{N} \tag{4}\]  
So, the result of meeting the two formulas (3) and (4) under the same sum of working life is:  
\[\frac{Twl_{i}}{Tcl_{i}}=\frac{Twl_{i-1}}{Tcl_{i-1}}\]  
Thus, the slope of the optimal working life distribution curve, which has the same sum of working life, can be obtained as follows:  
\[k=\frac{\sum\limits_{i=1}^{N}Twl_{i}}{\sum\limits_{i=1}^{N}Tcl_{i}} \tag{5}\]
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 1 Introduction
Spatial modulation (SM) not only improves the bit error rate (BER) performance, but also reduces the detection complexity of the proposed system [20; 21]. Results show that the proposed system generates the desired interference-free signals at the mobile station (MS) with diversity gains. Moreover, the proposed system clearly outperforms a conventional MU-MIMO system based on spatial multiplexing (SMux) and quadrature amplitude modulation (QAM) [22], and the amplify and forward (AF) relay-assisted MU-DQSM system [23] used as references. To the best of the author's knowledge, the proposed system has not been addressed in previous works. The remainder of this work is organized as follows. In Section 2, the system model of the RIS-MU-DQSM system is introduced. In Section 3, the channel model is presented. Section 4, discusses the interference cancellation of the system, whereas Section 5 deals with the optimal detection. Section 6 shows the results of the BER performance and the detection complexity of the proposed systems. Also, this section discusses the obtained results. Finally, conclusions are provided in Section 7. _Notation_: Uppercase boldface letters denote matrices whereas lowercase boldface letters denote vectors. The transpose, Hermitian transpose, complex conjugate, and Frobenius norm of \(\mathbf{A}\) are denoted by \(\mathbf{A}^{T}\), \(\mathbf{A}^{H}\), \(\mathbf{A}^{*}\), and \(\|\mathbf{A}\|_{F}\), respectively. The statistical expectation is represented by \(E[\cdot]\), \(\mathbf{0}\) denotes an all-zero matrix (or vector) whose dimensions are specified according to the context. Finally, \(\mathcal{CN}(0,\sigma^{2})\) is used to represent the circularly symmetric complex Gaussian distribution with mean zero and variance \(\sigma^{2}\).
# Research on a Reductive Deep Chlorine Removal Process for Breaking Through the Solid Film Barrier
## 2 Materials and Methods
### Calculations Related to Thermodynamic Parameters
#### 2.4.1 Ascorbic Acid Dechlorination  
Ascorbic acid dechlorination uses ascorbic acid as a reducing agent, which undergoes a redox reaction with copper ions in the solution to generate cuprous ions for dechlorination. The reaction equation is shown in Equation (1).  
\[2\text{Cu}^{2+}+\text{ C}_{6}\text{H}_{8}\text{O}_{6}+2\text{Cl}^{-}\to 2 \text{CuCl}+\text{C}_{6}\text{H}_{6}\text{O}_{6}+\text{ 2H}^{+} \tag{1}\]  
The reaction of ascorbic acid with Cu\({}^{2+}\) involves the transfer of electrons from the reducing agent to Cu\({}^{2+}\). In the redox process, two half-reactions take place: one in which Cu\({}^{2+}\) gains electrons to reduce to Cu\({}^{+}\), which then combines with Cl\({}^{-}\) to form CuCl, and another in which the reducing agent (ascorbic acid) loses electrons and is oxidized to dehydroacorbic acid [32]. The electric potential \(\Delta\)E, the standard Gibbs free energy \(\Delta\)G\({}^{0}\)  
Figure 2: Experimental set-up for dechlorination of ascorbic acid (**a**) and experimental flow chart (**b**).  
and the standard equilibrium constant K were calculated for this reaction from the data in Table 1, using Equations (2), (3) and (5).  
\[\Delta\mathrm{E_{1}}=\mathrm{E_{Cu^{2+}/CuCl}}-\mathrm{E_{C_{6}H_{8}O_{6}/C_{6}H_{ 6}O_{6}}}=0.57-0.08=0.49\;\mathrm{V} \tag{2}\]  
\[\Delta\mathrm{G_{1}}^{\theta}=-z\mathrm{FE}^{\theta}=-2\;\times\;96,484.5\; \times\;0.49=-94,554.8\;\mathrm{J/mol}=-94.6\;\mathrm{KJ/mol} \tag{3}\]  
The calculations were performed according to isothermal Equation (4):  
\[\Delta\mathrm{G}^{\theta}=-\mathrm{RTlnK} \tag{4}\]  
\[\mathrm{lgK_{1}}=\frac{z\mathrm{FE}^{\theta}}{2.303\mathrm{RT}}=\frac{94,554. 8}{2.303\;\times\;8.314\;\times\;298}=16.572 \tag{5}\]  
\[\mathrm{K_{1}}=3.733\;\times\;10^{16}\]
## 5 Experiments and Results
### Key-Based Security  
In this section, the effectiveness of both additive and multiplicative strategies is evaluated in terms of Bit Error Rate (BER). It is well-known that the most effective security mechanism will produce BER values equal to 0.5 when an incorrect key is used. According to Information Theory, no practical Error Correcting Codes exist for BER = 0.5, as Repetition Codes become effective for that BER for infinite codeword lengths. Therefore, a key-based security strategy producing BER = 0.5 is mandatory.
## 2 Materials and Methods
### Study Area  
Across western North America, all forested areas were delineated using MODIS-derived land cover information [29]. In temperature and precipitation, the mountainous terrain enhances local variation in both climate and soils. As a result, the study area includes portions of 51, Level III, US Environmental Protection Agency's defined ecoregions [30], within which there are four major subregions. The latter are described below based on information extracted from several sources [13, 14, 23, 24].  
The Marine West Coast Forest zone, which extends from Alaska in a progressively narrowing band to the San Francisco Bay area, is the most productive of the four subregions, with the highest percentage of forested land (82%). Western hemlock (_Tsuga heterophylla_) and Sitka spruce (_Picea stitensis_) are present throughout most of this maritime-affected zone, with Douglas-fir (_Pseudotsuga menziesii_), coastal redwood (_Sequoia sempervirens_), Alaska yellow cedar (_Chamaecyparis nootkatensis_), western red cedar (_Thuja placata_) and grand fir (_Abies grandis_) well represented in some areas. Maximum values of LAI generally range from 8 to 12 m\({}^{2}\) m\({}^{-2}\).  
The Northwest Forested Mountains is the second most productive subregion in the study area and is 55% forested. Douglas-fir and western hemlock are abundant in mixtures with Pacific silver fir (_Abies amabilis_), noble fir (_Abies procera_) and western larch (_Larix occidentalis_). At higher elevations and latitudes, lodgepole pine (_Pinus contorta_), whitebank pine (_Pinus alicaulis_), mountain hemlock (_Tsuga mertenisana_), subalpine fir (_Abies lasiocarpa_) and Engelmann spruce (_Picea engelmannii_) are characteristic species. Maximum LAI varies from 3.0 at some high elevation subalpine forests to 10.0 m\({}^{2}\) m\({}^{-2}\) for the most productive Douglas-fir and hemlock stands [13, 14].  
On drier sites, ponderosa pine (_Pinus ponderosa_) and incense cedar (_Calocedrus decurrens_) are usually present. Ponderosa pine extends its range southward through the Temperate Sierra Ecoregion (20% forested) into the fringe of the North American Desert Ecoregion, with further reductions in productivity. In Oregon and Northern California, in the rain shadow of the Cascade and Sierra Mountains, open pine forests grade into western juniper (_Juniperus occidentalis_) woodlands and, finally, to sagebrush steppe [13, 31]. Some species, such as Jeffrey pine (_Pinus jeffreyi_), grow predominantly on soils derived from ultramafic parent materials in the northern extent of its range [32]. Maximum LAI ranges between 0.5 and 2.5 m\({}^{2}\) m\({}^{-2}\).  
In the North American Desert of the Southwestern U.S., tree species make up only 2% of the flora, of which pinyon pines (mainly _Pinus edulis_ and _P. monophylla_) and junipers (_Juniperus spp._) are the most common [33], with LAI\({}_{\text{max}}\) generally less than 1.5 m\({}^{2}\) m\({}^{-2}\)[19].