esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Application Fields of Microwave Radiation in Medicine  
The use of microwave radiation for various applications in medicine has a history of more than 80 years (see [33] and references therein). Figure 1 demonstrates the areas of this kind of application for this electromagnetic range.  
In addition to diagnosing various diseases, we will point out applications in the field of sports, fitness, and general healthcare monitoring. The development of microwave ablation methods as a clinical tool for oncology treatment has great prospects [17; 27; 28]. A related use is associated with radiofrequency vein ablation for surgical purposes. A high therapeutic effect is given by physiotherapeutic practice based on microwave to create anti-inflammatory, anti-allergic, immunostimulating, and antispasmodic effects. Microwave technology allows one to eliminate cosmetic skin defects, such as kelol scars. Efficacy of microwave sterilization in dentistry are confirmed [20]. The properties of passive microwave radiation are determined by complex biochemical and biophysical processes inside proteins, cells, and organs as a whole with the release of energy, which makes MWR one of the tools in pharmacology at the stages of both preclinical and clinical studies [29].  
Microwave radiometry has certain advantages for rapid mass diagnostics in comparison with magnetic resonance imaging (MRI) [21], mammographic screening [34], ultrasound [35], or thermoacoustic tomography [36]. Magnetic resonance elastography (MRE) of the breast provides new opportunities for further development of ultrasound diagnostics to determine the biomechanical properties of tissues [37]. Machine learning algorithms can improve both the accuracy and reliability of MRE for cancer diagnosis (see review [38] and references therein). A separate approach to cancer diagnosis is tumor markers (biomarkers), which, as active substances, indicate cancer presence [39; 40]. The use of biosensors greatly facilitates the detection of relevant biomarkers compared to more traditional methods of searching for such breast tumor markers based on immunoassay, proteomics, and other  
Figure 1: Some applications of microwave radiation in medicine.  
methods of molecular biology [40]. Above we indicated a number of advantages of MWR for diagnostics (non-invasiveness, low cost and short duration of the procedure, the absence of contraindications and restrictions on the measurements frequency, etc.). Additionally, it should be noted that MWR gives sufficiently high sensitivity and specificity at the same time. Therefore, MWR can be a tool for initial diagnosis of breast cancer, for which the earliest possible detection is a key factor in successful treatment.
## 9 Future Research Directions and Challenges  
Most of the research on the use of 5G for localisation approaches the problem from the pure communication point of view, while its use in various robotic applications is still in its infancy. In this section, we shed light on future opportunities, research gaps, and challenges that will be provided by 5G for UAV and UGV applications.
# Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network
## 5 Scheme Feasibility Analysis
### Scalability Analysis  
Mission-oriented UAV networks use scale, node redundancy to guarantee mission completion rates in complex environments. Thus the scalability of the network is important. There are many factors that affect the scalability of the network, such as routing overhead, transmission delay, DOS attacks, etc. Storage resources also limit the scalability of the network when using blockchain as the management platform.  
First, the routing overhead of establishing and maintaining is too large, which not only occupies too many computing resources, but also squeezes the bandwidth space for normal business data. Moreover, the large network range and many network nodes lead to excessive network transmission delay, which cannot meet the requirements of UAV network timeliness. Thus, the reconfiguration methods of UAV networks with planar architecture, such as OLSR, AODV, etc., do not have good scalability. The recommended scheme is based on ZRP routing protocol improvement, which combines intra-zone routing with a small delay, and inter-zone routing with low routing overhead to build a blockchain-managed hierarchical network architecture with good scalability.  
Second, DOS attacks make the network process a large number of invalid requests, which seriously affects the availability of UAV networks, and the larger the network size, the more serious the impact. Thus, it is also a key factor limiting the scalability. BC_TRZP scheme builds the authentication environment of the UAV network, for which the requested nodes can be verified quickly with \(O(1)\) complexity by identity vector commitment to stop DOS attacks and improve the scalability of the network.  
Finally, the blockchain management platform can effectively provide node trust management, but the continuously growing blockchain ledger is also a challenge to the storage capacity of nodes. Traditional blockchain uses timestamp and hash chain to pack all transactions into blocks, which are uploaded to the chain after consensus. The transactions of the drone network are the nodes' own state data, and the amount of data is generated all the time and keeps growing. The limited storage capacity of nodes also causes the network to be unscalable, because the larger the network size is, the more transactions there are. This scheme adopts a two-stage consensus approach, where the state data of the cycle is consensual and then processed for decision making. The decision result is only the node trustworthiness of very small size and configuration information such as proxy. This configuration information is used to build new blocks, while a large amount of local state data is discarded after decision consensus, thus guaranteeing the scalability of the UAV network.
## 2 Materials and Methods  
### Materials  
The DNA phosphoramidite monomers and controlled pore glass (CPG) (nominal particle size 110 \(\upmu\)m, average pore size = 1000 A) were purchased from Link Technologies Ltd. (Scotland, UK). Superdehydrated acetonitrile (water < 0.001%, 10 ppm) was purchased from Wako Pure Chemical Industries Ltd. (Osaka, Japan). A 25-30% NH\({}_{4}\)OH aqueous solution was purchased from Fluka Analytical (Munich, Germany). The (15)-(+)-(10-camphorsulfonyl) oxaziridine (CSO) oxidizer, 5-(ethylthio)-1\(H\)-tetrazole (ETT activator, 0.25 mol/L solution in anhydrous acetonitrile), 5-(benzylthio)-1\(H\)-tetrazole (BTT activator, 0.3 mol/L solution in anhydrous acetonitrile), and anhydrous acetonitrile (MeCN) were purchased from Glen Research (Sterling, VA, USA). The SYBR gold nucleic acid gel stain was purchased from Invitrogen (Carlsbad, CA, USA). Tris-EDTA buffer (pH = 7.5) was purchased from Integrated DNA Technologies (IDT; Coralville, IA, USA). All the DNA oligonucleotides, except the ones that we synthesized from the microfluidic chips, were ordered from IDT and were purified using high-performance liquid chromatography (HPLC). Other chemicals, including tris (hydroxymethyl) aminomethane (Tris), Tris-borate-EDTA (TBE) buffer 10x, poly (vinylpyrrolidone), and TFA, were purchased from Sigma-Aldrich (Tokyo, Japan).
## 1 Introduction
In those cases usually, the information about turns is obtained (a) from the steering sensor mounted on the steering wheel, which is not available in our case, (b) is read from GPS, which does not work in an underground mine, or (c) in addition to a gyroscope, by utilizing magnetometer data (to reduce the drift problem), which readings are unreliable in mines. Therefore, there is a need to develop a separate, reliable turn-detection method, possible to be used in the difficult conditions of an underground mine.
## 4 Conclusions  
This paper explored the impact of air pressure on the laser spectral data of intumescent fire-retardant coatings using LIBS, and delved into the mechanisms driving these influences. Moreover, it examined how LIBS technology can be utilized to measure the thickness of fire-retardant coatings under varying air pressures, concluding with the following insights:  
1. As atmospheric pressure decreases, the intensity of spectral lines for titanium elements in the fireproof coating similarly diminishes. This trend is attributed to the reduced constraining effects of environmental gases at lower pressures.
2. There is a strong linear relationship between the number of laser pulses in fire-retardant coating samples and their ablation depth, with a goodness-of-fit of 0.963 for the linear model at 101 kPa. The Zn spectral line, characteristic of galvanized steel materials, can be used to determine the thickness of the fire-retardant coating by identifying the specific number of laser pulses required for the Zn spectral line to appear.
3. A decrease in atmospheric pressure leads to an increase in the ablation depth of fireproof coating samples due to the weakening of the plasma shielding effect. By establishing a linear model of ablation depth versus laser pulses at different pressure levels, it is possible to mitigate the influence of pressure on the thickness measurement of fireproof coatings.  
This study offers preliminary insights into how air pressure affects the laser spectral data of intumescent fire-retardant coatings and the underlying mechanisms involved. It also explores the application of LIBS for measuring coating thickness under different pressure conditions, providing a reference for assessing the fire performance of these coatings. Future research could advance the on-site characterization of the fire performance of fire-retardant coatings by incorporating technological innovations such as equipment miniaturization and integration with drone technology.  
Conceptualization, [PERSON] and H.J.; methodology, H.J.; software, S.W.; validation, F.Z. and S.W.; formal analysis, J.W. and H.J.; investigation, S.W.; resources, X.W.; data curation, F.Z.; writing--original draft preparation, H.J. and J.W.; writing--review and editing, X.W.; visualization, S.W. and F.Z.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Variation of Zn spectral line intensity with the number of laser pulses under different environmental conditions.  
**Funding:** This research was funded by the China Electric Institute State Key Laboratory of Environmental Adaptability for Industrial Products, grant number 2024 EASKJ-005.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Xylitol Fermentation from EOP Acid Hydrolysate
[39], when acetic acid concentrations are not high (<3-3.5 g/L), microorganisms are able to consume this compound as a nutrient. _C. boidinii_ was also able to consume acetic acid contained in the hemicellulose hydrolysate of corn fibre during the fermentation of this hydrolysate to xylitol [35]. On the other hand, the xylitol yield and productivities obtained from ion-exchange resin detoxified hydrolysate (considered as the best attained in this work for EOP hemicellulose hydrolysate) can be compared with those achieved in the SM1 synthetic medium (Table 2), which were regarded as the maximum values that could be obtained if toxic compounds were not present in the hydrolysate. As described in Section 3.3, the xylitol production with _C. boidinii_ is not only inhibited by toxic compounds, but it also is negatively affected by a considerable presence of glucose (glucose/xylose ratios > 10%). Then, as seen in Table 2, a decrease in the xylitol yield occurred because of the presence of residual toxic compounds in the EOP ion-exchange resin detoxified hydrolysate (0.43 vs. 0.52 g/g for EOP ion-exchange resin detoxified hydrolysate and SM1, respectively), reaching 83% of the maximum possible xylitol yield in SM1. In addition, fermentation of EOP ion-exchange resin detoxified hydrolysate was slower than for SM1 due to the presence of inhibitors, with its productivity threefold lower compared to SM1 (0.07 versus 0.22 g/L/h, respectively). However, similar cell yields were obtained in both cases (0.04-0.05 g/g). In summary, taking into account that a previous detoxification method (for instance, ion-exchange resin) is essential to reduce the toxic compounds, EOP hemicellulose hydrolysate can be successfully employed as fermentation medium to generate xylitol. Xylitol yields reported in the literature for the fermentation of diverse hemicellulose hydrolysates from different lignocellulosic biomass and by different xylitol producing microorganisms are compared in Table 3. As seen, in all examples the different raw materials were pretreated using H\({}_{2}\)SO\({}_{4}\), mainly in low concentrations. In most studies, a previous detoxification step, prior to fermentation, was necessary, with the most employed methods being chemical methods, like activated charcoal, ion-exchange resin, or ethyl acetate extraction. Regarding xylitol production, yields ranging between 0.11 and 0.55 g/g were reported, which were comparable to the results achieved in this work for EOP hydrolysates detoxified with both activated charcoal and ion-exchange resin. Moreover, the results achieved in this work were favourable compared to those attained by [PERSON] et al. (2019) with the same raw material. These authors reached a yield of 0.26 g/g in the xylitol fermentation with _Debaryomyces hansenii_ of hemicellulose hydrolysate from EOP obtained by pretreating with sulfuric acid (130 \({}^{\circ}\)C, 130 min, 3.5% H\({}_{2}\)SO\({}_{4}\), 33.3% solids), but a previous detoxification step by Giananofiltration was also necessary.
# Offloading of Atomic Tasks in Satellite Networks:
## 4 Method
### Algorithm Design
#### 4.2.2 Optimal Task Offloading  
Through the above calculation, we can get the maximum number of tasks that can be offloaded on the satellite, so the Formula (7) needs to meet the constraint of the maximum task offload. From the task offloading optimization problem proved in the literature [41], the task offloading problem can be transformed into a convex optimization problem.  
Given \(\rho=\rho^{0}\), the Formula (7) can be transformed into a convex optimization problem. It can be regarded as a function of \(\rho\) and redefined as a function of \(f(\varphi_{s})\), which denotes as follows,  
\[f(\varphi_{s})=\varphi_{s}T_{s,q}^{lm}+(1-\varphi_{s})\Big{(}1- \rho^{0}\Big{)}T_{s,q}^{h} \tag{9}\] \[s.t.\] \[C1:T_{s,q}^{lm}\leq D_{s},\forall s\in V,q\in Q,\] \[C2:\varphi_{s}\in\{0,1\},\;\forall s\in V,\] \[C3:\sum_{q=1}^{Q}\varphi_{s}^{q}\leq O(s),\;\forall s\in V,q\in Q,\]As shown in Equation (9), \(f(\varphi_{s})\) denotes the task offloading decision objective function with \(\varphi\) as the variable, where \(C1\) indicates that the task execution cannot exceed the time threshold. \(C2\) indicates that the value range of the decision variable is 0 or 1, and C3 indicates the task of satellite offloading. The quantity cannot exceed the amount of data that can be accommodated by the transmission link's maximum bandwidth and cannot exceed the amount of task data transferred by the maximum stream scheme.  
As shown in Algorithm 2, according to the calculation result of the maximum flow calculation, obtain the upper limit of the task that the satellite can unload at the current moment (line 5). The number of buffers for a mission is constant, so a constant value is assigned to the number of buffers available for each satellite (line 6). According to the Formula (9), use the Lagrangian expansion to solve and return the result of task offloading quantity (lines 7-11).  
```
1:Given a directed graph \(G=(V,E,T)\), Given time range \(\tau\), Given the atomic task request queue \(v_{q}\) on each satellite. Output: An array of offloading tasks \(arr(V)\)
2:set \(timeRange=\tau\)
3:set \(G^{{}^{\prime}}=G(V,E)\)
4:repeat
5: Calculate the maximum number of tasks that can be uninstalled \(O(V)\)
6:set \(\rho_{v}=cache(v)\)
7:set \(\varphi(v)=Lagrange(v)\)
8:set \(arr(v).add(\varphi(v))\)
9:until\(V\)
10:until\(timeRange\)
11:return\(arr(V)\)
```  
**Algorithm 2** Satellite mission offloading optimization calculation
# Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging
## 4 Results and Discussion
### Comparison with Related Works  
The proposed method was benchmarked against four models that introduced notable approaches to predicting drug response:  
1. Researchers utilized Weighted Graph Regularized Matrix Factorization (WGRMF) [92] to predict the responses of cell lines to anti-cancer drugs. This model used the CCLE, which has 491 cell lines and 23 drugs with 10,870 known responses. WGRMF employed gene expression and drug fingerprints as inputs for the model.
2. EBSRMF [81]: Researchers proposed Ensemble-based Similarity-Regularized Matrix Factorization, a bagging-based technique to enhance drug response prediction accuracy on the CCLE dataset. The dataset comprises 24 drugs and 363 types of cell lines. It utilized gene expression profiles and chemical structure.
3. DeepDSC [80]: Gene expression data were utilized to extract features of cell lines by a stacked deep autoencoder. Subsequently, the gene expression data were combined with chemical structure information to forecast drug response. DeepDSC utilized the Cancer Cell Line Encyclopedia (CCLE), which has 491 cell lines and 23 drugs, along with 10,870 documented responses.
4. SRMF [93]: Drug response prediction was accomplished by combining gene expression data with chemical structures using a Similarity-Regularized Matrix Factorization model. The CCLE dataset has 10,870 known responses, encompassing 491 distinct cell lines and 23 drugs.  
Table 7 compares the BRR-ISB using 200 features as a bag size and ten iterations with the four models using the CCLE dataset.  
Figure 5: The loss value between training and validation.  
The proposed model demonstrated improvements by achieving the lowest RMSE compared with the other models. Additionally, BRR-ISB achieved the highest PCC compared with the other methods. DeepDSC showed the highest R\({}^{2}\) score compared with BRR-ISB.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 4 Interference Alignment in Cognitive Radio: Second Paradigm
### Multi User MIMO SU Link
By allowing the PU-Rx to perform interference suppression and also checking that the number of variables and the number of equations match the feasibility condition of DoF allocation of IA [121], it is shown that the SUs achieve considerable performance gain in terms of their transmission rates.
# Thin Film Encapsulation for RF MEMS in 5G and Modern Telecommunication Systems
## 6 RF Modeling  
A full 3D FEM model was developed to simulate the RF behavior of 400 \(\upmu\)m long CPWs in gold on low (\(\rho\) = 20 Ohm-cm) and high (\(\rho\) = 5 kOhm-cm) resistivity silicon substrates. Simulations of CPWs  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Barrel Parameters** & **Cap Release** \\ \hline Test 1 & P = 600 W, t = 8 min & complete \\ Test 2 & P = 400 W, t = 8 min & complete \\ Test 3 & P = 300 W, t = 8 min & incomplete \\ Test 4 & P = 300 W, t = 20 min & complete \\ Test 5 & P = 200 W, t = 20 min & incomplete \\ Test 6 & P = 200 W, t = 80 min & complete \\ Test 7 & P = 100 W, t = 80 min & complete \\ \hline \hline \end{tabular}
\end{table}
Table 3: Barrel parameters for the etching of the sacrificial layer and consequent results in terms of release for capping layers with A = 57%.  
uncapped and encapsulated with the fabricated caps having A = 26%, 50%, and 57% were performed in the frequency range of 0-40 GHz.  
For low resistivity substrate, the reflection loss was predicted to decrease with increasing the frequency from around -21 dB to -28 dB (Figure 4a) for uncapped and capped CPWs. No significant variation was observed as a function of the cap, likely due to the fact that substrate losses and characteristic impedance mismatch between CPW and RF terminations (i.e., input/output ports in the simulated model) dominate.  
For a high resistivity substrate, the reflection loss of the uncapped CPW was significantly smaller than that on the low resistivity substrate, due to a better match of the CPW characteristic impedance, with values that increased with frequency from around -38 dB to -30 dB. In the presence of a cap, the reflection loss of the CPW on the high resistivity substrate further decreased, especially for frequencies lower than 6 GHz. In particular, the reflection loss reduced by a factor of -0.87 for almost all frequencies for the capped CPW with A = 50% and decreased for frequencies lower than 15 GHz by a factor up to 1.8 for capped CPWs with A = 26% and 57%. The reduction of reflection loss observed in the presence of a cap can be ascribed to the fact that the CPW geometry was not previously optimized for any specific frequency so that it is possible that the CPW characteristic impedance gets closer to 50 Ohm thanks to the interaction of the electromagnetic field with the cap.  
Figure 4: Magnitude of simulated reflection (**a**) and insertion (**b**) losses for gold coplanar waveguides (CPWs) uncapped and encapsulated with the fabricated caps having A = 26%, 50%, and 57% on low and high resistivity silicon substrates.  
The insertion loss of the uncapped CPW on a low resistivity substrate decreased from around \(-\)0.8 dB to \(-\)1.2 dB with frequency (Figure 4b). When the high resistivity substrate was used, the insertion loss of the uncapped CPW improved, reducing from nearly zero to around \(-\)0.45 dB. For both substrates, no appreciable effects on the insertion loss were predicted to be induced by the presence of caps.