esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated DNA Extraction Monitoring System Based on MTConnect Technology
## 4 Discussion  
POCT is used in various fields, such as medicine, quarantine, agriculture (breeding), and food. In particular, its use in the medical field is very important because it takes a short time from the start of a test to diagnosis and is not restricted by location and time. The reason why this is receiving such attention from the industry is that the patient's condition changes from moment to moment, and the appropriate response at the beginning is effective for treatment. In addition, the necessity of being able to monitor POCT equipment and manage it remotely is particularly important for the specialty of remote medical treatment.  
This study demonstrated the possibility of the remote monitoring of automated diagnosis processes using POCT devices and the MTConnect standard technology, in the field of in vitro diagnostic testing. Previous studies on the MTConnect standard technology were limited in application to equipment monitoring in the manufacturing industry. This study has demonstrated that MTConnect standard technology can be applied to monitoring the status of medical equipment in multiple remote locations in real time.  
The average response time of the system implemented in this study was longer (11.39 ms, 9.62 ms, and 10.55 ms for sample, probe, and current commands, respectively) than that of the system described in a previous MTConnect study (4.80 ms, 4.81 ms, and 4.50 ms for sample, probe, and current commands, respectively) [21]. The reason for the longer response time of this study seems to be that the proposed system handles not only the MTConnect function but also the DNA extraction process. In this study, it was expected that the optimization of each program implemented on a single-board computer and running at the same time was necessary, along with optimization of the interlocking parts.  
In a follow-up study, the adaptability and performance of the currently implemented MTConnect monitoring solution to other medical devices must be tested. This will require efforts to improve the performance of the MTConnect system and expand is application to other companies' equipment in the manufacturing facility industry.
## 3 Results and Analysis
### Analysis of Effect of Threshold Model Testing  
We first examined whether the threshold effect existed, and then determined the number of threshold values based on the test results. According to the analysis of the test results in Table 3, the F statistic and its significance level verified the existence of the threshold effect. The F statistics of the single-threshold model and the dual-threshold model were 78.20 and 90.44, respectively, and both passed the 1% significance level test. The \(p\) value indicated that both a single threshold and a double threshold could be selected, and the width of the confidence interval was small. However, a small sample size in the interval would lead to inaccurate estimation results. Therefore, considering the sample size of each interval, the research should select a single threshold model. When the corresponding likelihood ratio statistic LR was 0, the threshold parameter estimated value was \(\gamma\) = 5,327,211.87.
## 2 Instrument Design  
A microwave radiometer is a receiver for electromagnetic radiation with sub-millimeter to centimeter wavelengths, corresponding to the frequency range of 1-1,000 GHz. The L-band, ranging from 1-2 GHz, has many commercial and military applications. It also contains the hydrogen line at 1,420.41 MHz, originating from the hyperfine transition of neutral hydrogen. For the imaging of neutral atomic hydrogen in interstellar space, passive measurements at this frequency are of great astronomical interest. As a consequence, the 27 MHz frequency band ranging from 1,400 to 1,427 MHz has become a protected radio astronomy allocation world-wide, in which it is forbidden to transmit any kind of electromagnetic radiance. Likewise, an RFI-free environment is mandatory to measure microwave brightness temperatures emitted from terrestrial surfaces. The frequency transfer function of an L-band radiometer to be used for retrieving geophysical properties must therefore be narrow and within this protected band. This implies, however, that the power level \(P\) received by such a radiometer with bandwidth \(B=27\) MHz is very low. For a noise source at the physical temperature \(T\), and with emissivity equal to unity (e.g., a perfectly matched resistor), the noise power received is:  
\[P=kTB \tag{2}\]  
with \(k=1.380658\cdot 10^{-23}\) J K-1 being the Boltzmann constant. The same expression holds true when a scene at the physical temperature \(T\) and with the emissivity 1 is observed with a radiometer. For \(T=300\) K, this gives \(P\approx 0.11\cdot 10^{-12}\) W (\(\approx\)-99.5 dBm) received with the radiometer antenna. To detect such an extremely low power, the radiometer (RM) must have the lowest possible residual noise \(T_{\mathrm{RM}\,0}\) and any instrument internal RFI disturbances must be rigorously mitigated. To allocate an absolute value to the noise power received with the antenna, the noise power must be compared with the power of at least two instrument internal calibration sources with known noise temperatures. Provided that the linearity of the receiver is sufficient and the gain is stable between several calibration cycles, this allows a certain brightness temperature to be assigned to the radiance entering the antenna. These requirements are important for the design of the ELBARA II electronics described below.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Dual Graph Construction  
In this section, we present a construction that is asymptotically much worse than Definition 4; however, it turns out to be quite interesting for a small number of elements. Specifically, it creates a separating family with \(k\) sets for at most \(\frac{k(k-1)}{2}\) elements. The construction is presented below (see Figure 5 as an illustration of the result).  
**Definition 5**.: _1. Create a graph \(G\) with \(k\) vertices labeled by \(F_{1},\ldots,F_{k}\) and with no edges;_  
_2. For \(i=1,\ldots,r\):_  
_(a) find two vertices \(F_{x},F_{y}\) not yet connected in \(G\),_  
_(b) connect \(F_{x},F_{y}\) by an edge labeled by \(i\)._  
_(3) For \(i=1,\ldots,k\):_  
_- define set \(F_{i}\) as the set of all labels of the edges incident to the vertex labeled by \(F_{i}\)._  
Of course, the construction from Definition 5 works correctly only for \(r\leq\frac{k(k-1)}{2}\). It views the network nodes as edges of a graph \(G\) and the separating family as the set of vertices of \(G\).  
Table 4 compares the number of sets in the separating families obtained by the constructions from Definitions 4 and 5.  
Finally, we can check that Definition 5 provides correct results. Consider \(i<r\), which corresponds to an edge with endpoints, say, \(F_{a}\) and \(F_{b}\). If we want to separate \(i\) from an element \(j\), then we have to choose either \(F_{a}\) or \(F_{b}\). Assuming that none of them can be used, that is, \(j\in F_{a},F_{b}\), then the endpoints of the edge labeled by \(j\) are \(F_{a}\) and \(F_{b}\), just as in the case of \(i\). This is a contradiction, as \(G\) does not contain multiple edges.
## 3 Observed Damage Mechanisms in Eastern T\(\ddot{\text{u}}\)rkiye's Masonry Buildings
### Damage Modes Attributable to Poor-Quality Units and Mortar
#### 3.1.2. Stone Blocks of Variable Quality  
In many regions of eastern T\(\ddot{\text{u}}\)rkiye, stone masonry has traditionally been the predominant building method owing to the natural availability of basalt, limestone, tuff, and other sedimentary and volcanic rock resources. Although stone walls may demonstrate considerable compressive strength when properly cut, dressed, and erected, many regional constructions use irregularly shaped or inferior stone blocks that significantly undermine seismic performance [41]. Deficiencies in stone quality, cutting techniques, and assembly processes often result in brittle, poorly bonded walls that are susceptible to various collapse mechanisms during moderate-to-intense seismic activity.  
A prominent issue is that several stones are used in their \"rubble\" state, indicating they are sourced from local quarries or riverbeds and incorporated into walls with less or no modification. Consequently, individual blocks exhibit irregular geometry and fail to create  
Figure 6: Damage due to use of adobe bricks.  
cohesive, interconnecting surfaces. This results in substantial mortar-filled voids between stones, creating a discontinuous load path inside the wall section. Cyclic lateral stresses from seismic waves induce stress concentrations at these gaps, leading to premature mortar cracking and potential delamination of exterior stone veneers. Post-earthquake reconnaissance after the 2011 Van earthquakes and 2023 Kahramanmaras earthquakes revealed whole wall portions removed from corners or mid-lengths, mostly due to inadequate fitting between neighboring stones.  
A further issue emerges from the variety of rock types used in the same wall. Stone blocks may vary not only in form but also in geological composition; some may exhibit more brittleness, while others may have enhanced elasticity or superior strength. This heterogeneity induces inconsistent stiffness along the wall's length and height, resulting in varying deformations under lateral stimulation. Consequently, weaker or more brittle stones may serve as \"weak links\" where fractures originate, ultimately propagating throughout the wall. Post-earthquake damage assessments in rural regions following the earthquakes indicated that structures composed of mixed stone types often exhibited intricate cracking patterns accompanied by partial out-of-plane failures at the junctions of stronger and weaker stone components [44]. Field observations for this type of application are given in Figure 7.
## 4 The Proposed HMPS Technique
```
1:\(B,Y,c,s,k,y,g,p\)
2:functionHSig
3:\(A,m\gets Dec(c,s)\)
4:\(h\gets Hash(m)\)
5:\(h^{\prime}\gets Hash(h)\)
6:if\(Y^{h}\mod p=g^{A}\times B^{ll^{\prime}}\mod p\)then\(\triangleright\) accept previous signatures
7:\(a,b\gets IndS(m,y,k,g,p)\)
8:\(A\gets A+a\mod(p-1)\)
9:\(B\gets B\times b\mod p\)
10:\(Y\gets Y\times y^{b}\mod p\)
11:\(c\gets Enc(s,m,A)\)
12:return \(c,B,Y\)
13:else
14:return \(\bot err\)
15:endfunction
```  
**Algorithm 5** Hierarchic signature. Finally, if the entity is the last verifier, Algorithm 6 illustrates its process. ```
1:\(B,Y,c,s,k,y,g,p\)
2:functionLVer
3:\(A,m\gets Dec(c,s)\)
4:\(h\gets Hash(m)\)
5:\(h^{\prime}\gets Hash(h)\)
6:if\(Y^{h}\mod p=g^{A}\times B^{ll^{\prime}}\mod p\)then\(\triangleright\) accept previous signatures
7:return \(1\)
8:else
9:return \(err\)
10:endif
11:endfunction
```  
**Algorithm 6** last verification  
The system works as follows: One of the nodes decides to create a message \(m\), then signs and encrypts it (FEnc function), and sends the ciphertext to node number one in the group in order to start a collective signing process from the first node to the last nodes. Each node that receives this encrypted message extracts the original text \(m\) and the first part of the signature \(A\), which is included there (Dec function). The next step for the message receiver \(j\) is to verify that all the nodes before it (\(i\) from \(1\) to \(j-1\)) have signed the message by using a single verification process. Then, the receiver \(j\) updates the signature, \(\sigma\), by adding its own signature on the extracted signature (signature of signature), then they encrypt the message again using the new computed signature (HSig) and send the new data (ciphertext and signature \(c,B,Y\)) to the next node. Following Algorithm 5 (hierarchical signature), the node extracts the message, \(m\), by the \(Dec(c,s)\) function--line 2; then, it verifies the signature validity and signs the same \(m\) by using their individual signature (line 6, \(a,b\gets IndS(m,y,k,g,p)\)). After updating the aggregate signature \(A\) (line 7) and the aggregate public keys \(B\) and \(Y\) (lines 8 and 9), they encrypt the same message, \(m\) (line 10, \(c\gets Enc(s,m,A)\)). The next node will extract the same message, \(m\), from \(c\) by using \(Dec(c,s)\)--line 2. This continues on. For a hierarchical model and respecting the order of signatures, we can replace the hash \(h\) by \(h=Hash(m\parallel ID_{0}\parallel ID_{1}\cdots\parallel ID_{j})\). Algorithm 7 displays the entire process flow for the proposed scheme. ```
1:function SP
2:\(c_{0},B_{0},Y_{0}\gets FEnc(s,m,y_{0},k_{0},g,p)\)
3:\(c_{1},B_{1},Y_{1}\gets HS(B_{0},Y_{0},c_{0},s,k_{1},y_{1},g,p)\)
4:\(c_{2},B_{2},Y_{2}\gets HS(B_{1},Y_{1},c_{1},s,k_{2},y_{2},g,p)\)
5:...
6:\(x\gets LastV(B_{j},Y_{j},c_{j},s,g,p)\)\(\triangleright\)\(x\) is 1 or err
7:endfunction
```  
**Algorithm 7** System progress.
## 4 Case Study
### IPC-Based Coordinate System and Opportunity Identification
The functions of aided shooting and aided editing, as well as planning video content in advance can be attached to UAVs. These functions will enable both professionals and amateurs to efficiently create professional videos using UAVs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Patent Number** & **Application Date** & **Title** & **Description** \\ \hline \multirow{4}{*}{CN103935508B} & \multirow{4}{*}{9 April 2014} & Auxiliary control method and auxiliary control system for descending of unmanned aerial vehicle & A method to improve the descending stability of UAV, by adjusting throttle outputs of the engine based on the difference between the virtual and actual altitude \\ \cline{3-4}  & & Flight auxiliary method and system of unmanned aerial vehicle, unmanned aerial vehicle, and mobile terminal & A method for long-range flight control, by defining the forward direction of a UAV based on the positions of the targeted destination and UAV \\ \hline \multirow{4}{*}{CN106104203B} & \multirow{4}{*}{13 July 2015} & A distance detecting method and a device for a moving object and an aircraft & A method for precise control of a flight position, based on the actual distance between the moving UAV and the targeted position and on the virtual distance and angle between them \\ \hline \multirow{4}{*}{CN106170676B} & \multirow{4}{*}{14 July 2015} & Method, device, and system for determining movement of mobile platform & A method for precise hover control of a UAV, by estimating the position error based on the correlation between the error and its contribution \\ \hline \multirow{4}{*}{CN107000830B} & \multirow{4}{*}{10 November 2016} & \multirow{4}{*}{UAV descent controlling method and device and UAV} & A method for smooth descent of a UAV, to mitigate the horizontal airflow by adjusting the horizontal speed of UAV, so that the drone can obtain a large descent speed without sway \\ \cline{1-1} \cline{3-4}  & & Unmanned aerial vehicle and control method for same & Converting tasks into control instructions to achieve autonomous cruising. \\ \hline \multirow{4}{*}{CN108780322B} & \multirow{4}{*}{24 May 2017} & Method, device, and system for controlling UAV & An efficient UAV launching process and its control operation, based on the throwing action \\ \hline \multirow{4}{*}{CN109074168B} & \multirow{4}{*}{23 January 2018} & Control method, device and UAV & Control the flight of the drone based on the position information of the user’s hand in the captured image \\ \cline{1-1} \hline \hline \end{tabular}
\end{table} 
Table 5: Patents with radical invention features for the control system of UAVs.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## Abbreviations  
The following abbreviations are used in this manuscript:  
ADC Analog-to-Digital Converter.  
ASIC Application-Specific Integrated Circuit.  
AxC Approximate Computing.  
AXI Advanced eKensible Interface.  
BRAM Block RAM.  
CPU Central Processing Unit.  
DGNSS Differential GNSS.  
DMA Direct Memory Access.  
DSP Digital Signal Processing.  
FFT Fast Fourier Transform.  
FPGA Field-Programmable Gate Array.  
GNSS Global Navigation Satellite System.  
HDL Hardware Description Language.  
HLS High-Level Synthesis.  
IC Integrated Circuit.  
IP Internet Protocol.  
KPI Key Performance Indicator.  
LUT Lookup Table.  
ML Machine Learning.  
NoC Network on Chip.  
PL Programmable Logic.  
PP Precise Point Positioning.  
PRN Pseudo-Random Noise.  
PS Processing System.  
PU Processing Unit.  
RAM Random Access Memory.  
RF Radio Frequency.  
SDR Software-Defined Radio.  
SNR Signal to Noise Ratio.  
SPP Standard Point Positioning.  
TOW Time of Week.  
USRP Universal Software Radio Peripheral.  
VHDL Very-high Speed Integrated Circuit Hardware Description LanguageAppendix A Snippet of Python code illustrating how to use PYNQ to interact with a design in the FPGA on the Zynq platform. The example code programs the FPGA and enables the design before transferring an array of 5000 64-bit integers to it and receiving six 64-bit integers in return. Our Jupyter notebook used for initial experiments with Hard SyDR extends upon this code by looping over the runKernel function till a pre-determined number of milliseconds of RF data have been processed.
# Intercalating Sn/Fe Nanoparticles in Compact Carbon Monolith for Enhanced Lithium Ion Storage
###### Abstract  
Given its high-capacity of multielectron (de-)lithiation, SnO\({}_{2}\) is deemed as a competitive anode substance to tackle energy density restrictions of low-theoretical-capacity traditional graphite. However, its pragmatic adhibition seriously encounters poor initial coulombic efficiency from irreversible Li\({}_{2}\)O formation and drastic volume change during repeated charge/discharge. Here, an applicable gel pyrolysis methodology establishes a SnO\({}_{2}\)/Fe\({}_{2}\)O\({}_{3}\) intercalated carbon monolith as superior anode materials for Li ion batteries to effectively surmount problems of SnO\({}_{2}\). Its bulk-like, micron-sized, compact, and non-porous structures with low area surfaces (14.2 m\({}^{2}\) g\({}^{-1}\)) obviously increase the tap density without compromising the transport kinetics, distinct from myriad hierarchically holey metal/carbon materials recorded till date. During the long-term Li\({}^{+}\) insertion/extraction, the carbon matrix not only functions as a stress management framework to alleviate the stress intensification on surface layers, enabling the electrode to retain its morphological/mechanic integrity and yielding a steady solid electrolyte interphase film, but also imparts very robust connection to stop SnO\({}_{2}\) from coarsening/losing electric contact, facilitating fast electrolyte infiltration and ion/electron transfer. Besides, the closely contacted and evenly distributed Fe\({}_{2}\)O\({}_{3}\)/SnO\({}_{2}\) nanoparticles supply additional charge-transfer driving force, thanks to a built-in electric field. Benefiting from such virtues, the embedment of binary metal oxides in the dense carbons enhances initial Coulombic efficiency up to 67.3%, with an elevated reversible capacity of 726 mAh/g at 0.2 A/g, a high capacity retention of 84% after 100 cycles, a boosted rate capability between 0.2 and 3.2 A g\({}^{-1}\), and a stable cycle life of 466 mAh/g over 200 cycles at 1 A g\({}^{-1}\). Our scenario based upon this unique binary metal-in-carbon sandwich compact construction to achieve the stress regulation and the so-called synergistic effect between metals or metal oxides and carbons is economically effective and tractable enough to scale up the preparation and can be rifely employed to other oxide anodes for ameliorating their electrochemical properties.  
a mode; lithium ion battery; tin oxide; carbon materials +
Footnote †: journal: Applied Soft. 2020, 10, 2220
# Collagen Nanoparticles in Drug Delivery Systems and Tissue Engineering
## 2 Methods of Collagen Nanoparticle Preparation
### Complex Coaervation/Polyelectrolyte Complexation  
Complex coaervation or polyelectrolyte complexation is a process where oppositely charged polyelectrolytes in an aqueous solution undergo a liquid -liquid phase separation (LLPS) for the formation of a polymer-dilute and polymer-dense (coaervate) phase. The coaervate's properties can be adjusted by the chirality of the polyelectrolytes, salt concentration, pH, charge density, temperature and ionic strength, enabling the understanding of LLPS from changes in the physical environment [38]. It is a chemical nanoparticle preparation method and involves the incorporation of natural salt or alcohol to a solution of collagen which changes the collagen structure, to which a crosslinking material is added, leading to nanoparticle formation, as explained in Figure 2. This preparation method is  
Figure 1: Emulsification/Solvent Extraction—The drug is dissolved in the polymer solution and an emulsion is formed by mechanical agitation. The polymer and drug are precipitated in droplets and NPs are fabricated by solvent extraction by evaporation.  
comparable to the desolvation method, only differing in parameters such as pH, rate of addition of solvent, temperature, homogenizer speed, crosslinking agent concentration, molar ratios of protein and organic solvent [36]. Due to the amphoteric nature of proteins which consist of multiple charged functional groups, they can be changed to either anionic or cationic just by modifying parameters such as pH. The protein which is charged can associate electrostatically with different polymeric electrolytes [31]. This method is useful in applications such as entrapment of DNA for gene therapy [39]. A study conducted by [PERSON] et al. explores the potential of DNA NPs prepared with native collagen (NC) and methylated collagen (MC) to efficiently deliver genes into cells. The transfection abilities and physicochemical properties of these two types of NPs were studied in parallel. NC/DNA and MC/DNA NPs were prepared using the complex coacervation method. It was observed that NC formed complexes with the DNA at a low pH value. This complex accumulated promptly at neutral pH and did not give out significant protection to DNA because of its poor stability in serum. MC carries a positive charge at neutral pH and thus has higher stability under physiological conditions and strong DNA binding ability. It also showed that MC/DNA NPs were smaller and more stable than NC/DNA particles, which released DNA in a prolonged manner for up to 3 weeks [38; 40].
## 4 Predicting the Porosity in Selective Laser Melting Parts  
Using the artificial images created in Section 3, this section will show three methods for predicting the percentage of porosity in SLM parts. The first one is the existing image binarization method, which is one of the main methods for measuring porosity along with the Archimedes method [22]. Archimedes' principle might be used in the case of producing real SLM parts, and this paper uses artificial porosity images, so the image binarization method will be conducted and explained in Section 4.1. The second and third methods are Regression Convolutional Neural Network (RCNN) and hybrid Bees Regression Convolutional Neural Network (BA-RCNN), which will be explained in detail in Section 4.2.