esa-sceva/satcom-chunk-collection huggingface dataset:
# An Anchor-Free Location Algorithm Based on Transition Coordinates
###### Abstract  
In some location scenarios where the location information of nodes cannot be mastered in advance, the anchor-free location technology is particularly important. In order to reduce the complicated calculation and eliminate the accumulated error in the traditional anchor-free location algorithm, a new anchor-free location algorithm based on transition coordinates is proposed in this paper. This algorithm is different from the traditional methods such as minimum cost function or inverse matrix. Instead, \(N\) initial coordinates are randomly generated as the starting position of the transition coordinates, and the position increment between the transition coordinates and the real coordinates of the node is constantly modified. After \(K\) iterations, the convergent position coordinates are finally infinitely close to the real position coordinates of \(N\) nodes, and the computational complexity is less than most existing algorithms. As follows, the factors that affect the performance of the algorithm are investigated in the simulation experiment, including the topology structure, positioning accuracy and the total number of nodes, etc. The results show great advantages compared with the traditional anchor-free positioning algorithm. When the topology structure of the initial coordinates changes from a square to a random graph, the number of iterations increases by 15.79%. When the positioning accuracy increased from 1% to 1%, the number of iterations increased by 36.84%. When the number of nodes \(N\) is reduced from 9 to 4, the number of iterations is reduced by 63.16%. In addition, the algorithm can also be extended to the field of moving coordinates or three-dimensional spatial positioning, which has broad application prospects.  
anchor-free location; transition coordinates; position increment; topology structure +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Throughput Comparison  
In this study, we analyzed the impact of varying traffic generation rates on the throughput performance of four routing protocols: CM-QOS-AODV, CM-GPSR, QOS-AODV, and GPSR. The evaluation focused on the influence of two optimization profiles: the Throughput Optimized Method (TOM) and the Delay and PDR Enhanced Method (DEM). As illustrated in Figure 5, the throughput of all protocols increased proportionally with higher traffic generation rates, which aligns with expectations since greater traffic loads tend to better utilize available network capacity [11]. Figure 5a demonstrates that both QOS-AODV and GPSR protocols equipped with TOM achieved superior throughput compared to their DEM counterparts and their respective baseline versions. Specifically, QOS-AODV with TOM achieved a peak throughput exceeding 0.45 Mbps, whereas QOS-AODV with DEM showed  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Experiment** & **1** & **2** & **3** & **4** & **5** \\ \hline Time & & & & & \\ Interval (s) & 0.11 & 0.16 & 0.21 & 0.26 & 0.31 \\ \hline Traffic (bps) & 74,472.73 & 51,200 & 39,009.52 & 31,507.69 & 26,425.81 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Traffic Generation Values in Time Interval and Data per Node per Second: The table details the configured traffic generation parameters, including time intervals and the amount of data generated per node per second, which were used to simulate network load conditions during performance evaluation experiments.  
slightly lower performance, remaining just under 0.4 Mbps [23]. This performance gap highlights TOM's effectiveness in maximizing throughput, while also indicating that DEM's additional focus on delay and packet delivery ratio (PDR) can introduce trade-offs that marginally hinder throughput. Similarly, Figure 5b shows that GPSR with TOM achieved the highest throughput relative to both DEM-based GPSR and the baseline GPSR. Notably, TOM's impact was more pronounced on GPSR than on QOS-AODV [20]. The throughput improvement for QOS-AODV with TOM was approximately 0.08 Mbps, whereas GPSR with TOM outperformed the baseline GPSR by 0.17 Mbps at a 0.11 s interval. This disparity suggests that the GPSR protocol may be inherently more responsive to throughput-specific enhancements introduced by TOM. These findings are consistent with earlier studies that emphasize the importance of optimization strategies in improving protocol throughput under high-traffic conditions [27]. Overall, our results underscore the effectiveness of TOM in enhancing throughput in Vehicular Communication Networks (VCNs), particularly in scenarios where real-time performance is critical. On the other hand, while DEM offers benefits in reducing delay and improving PDR, it entails a trade-off in throughput performance. This analysis contributes to a deeper understanding of the balance between throughput, delay, and reliability in VCN routing and supports the adoption of adaptive optimization strategies based on specific application requirements.
# Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures
## 3 IP Address Lookup Using a Vectored-Bloom Filter
### VBF Querying  
Algorithm 2 describes the querying procedure of the VBF in detail. For a given input key, \(k_{v}\) indexes are first obtained and a bit-wise AND operation is then performed for \(k_{v}\) vectors obtained from the VBF in order to obtain a \(l\)-bit result vector, _check_. The querying has three possible results: _negative_, _positive_ or _indeterminable_. If all of the bits in the _check_ are 0, the input was definitively not programmed to the VBF and the result is termed a _negative_. If the _check_ has a single set bit, the input is considered a member of the routing set and the location of the set bit is returned as the matching output port; the result is then termed a _positive_. If the _check_ has plural set bits, the VBF cannot return a value and the result is termed an _indeterminable_. In Algorithm 2, outPort is the matching output port and _counter_ is the number of set bits in the _check_. Therefore, if _counter_ is 1, it means a _positive_ and outPort is returned as the matching port. If _counter_ is larger than 1, it means an _indeterminable_ and a matching port cannot be returned from the VBF.  
```
FunctionqueryVBF(\(y\)) check = BF[\(h_{0}(y)\)]&&BF[\(h_{1}(y)\)]&&&&BF[\(h_{k_{v}-1}(y)\)]; counter = 0; for(\(i=0\) to \(l-1\))do if( check[\(i\)] == true ) then outPort = \(i+1\); counter = counter + 1; if( counter >1)then break;  end if  end for if( counter == 0)then return 0; // negative elseif( counter == 1)then return outPort; // positive else return -1; // indeterminable  end if  end for
```  
**Algorithm 2**Querying Procedure of VBF
## 4 Case Study
### Results and Discussion  
#### 4.3.1 Weights of Flight Routes  
After applying Min-Max normalization to the data for average daily flight volume, flight distance, and airport traffic stability (RAI), as shown in Figure 9, it can be observed that the higher the average daily flight volume on a route, the greater its impact on the airport's traffic stability. On the other hand, flight distance is influenced by the geographical locations of the origin and destination. For example, routes with the longest flight distance (Aksu, AKU, 3159.45 km) and the shortest flight distance (Ankang, AKA, 62.52 km) both have relatively low average daily flight volumes and little impact on airport traffic stability, which indicates a lower correlation between flight distance and variations in other indicators.  
Figure 9: Average daily flight volume, flight distance, and RAI of each route. (**a**) Average daily flight volume. (**b**) RAI values. (**c**) Flight distance.  
This relationship is verified through correlation analysis, as shown in Table 3. The correlation between average daily flight volume and airport traffic stability (RAI) is very high (0.94), indicating that routes with higher average daily flight volumes significantly impact airport traffic stability. The correlation between flight distance and average daily flight volume is low (\(-0.16\)), showing no significant relationship between flight distance and daily flight volume. Similarly, the correlation between flight distance and airport traffic stability (RAI) is also low (\(-0.18\)), indicating no significant relationship between flight distance and airport traffic stability.  
The route weights of each airport are shown in Figure 10. To analyze the regional distribution of the route weights, representative airports in different regions are listed in Table 4.  
The Grey Relational Degree calculation results show significant differences in the total weight of different routes across the average daily flight volume, flight distance, and airport traffic stability (RAI). Routes in economically developed regions (such as Shanghai and Guangzhou) with higher Grey Relational Degrees indicate a more significant impact on  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & **Route OD** & **Route Weight** \\ \hline Eastern Coastal Area & Guangzhou Baiyun Airport & 0.9 \\ Eastern Coastal Area & Shanghai Pudong Airport & 0.85 \\ Central Area & Wuhan Tianne Airport & 0.75 \\ Western Area & Chengdu Shuangliu Airport & 0.7 \\ Western Area & Urumqi Airport & 0.5 \\ Northeastern Area & Harbin Airport & 0.65 \\ Remote Western Area & Lhasa Gonggar Airport & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Weights of representative routes in different regions.  
Figure 10: Route weights of airports linked with Xi’an Xianyang airport.  
airport traffic stability. In contrast, routes in remote areas (such as Lhasa and Aksu) with lower Grey Relational Degrees confirm that flight distance has a lower correlation with other indicator variations.
## 4 Power Control For Multiple Sink Cluster WSNs (MSCWSNs-PC)
### Border Constraint Problem (BCP)  
Although the most significant bit sink radius correction (MSBSRC) scheme is provided, when only a few sink nodes were deployed, the sink border group is absent [see Figure 8(a)] which would result in a communication void. In our opinion, if one or more of the sink groups in _Topside_, _Underside_, _Lefside_,  
Figure 7: Illustration of border communication void problem.  
or _Rightsside_ was empty, this situation will easily produce a sink communication void problem. In addition, the power allocation algorithm is only focused on the borders of the objective area, and the center communication void problem also needs to be considered [see Figure 8(b)]. We denote all of these conditions collectively as the border constraint problem.  
For the sink group absence problem, if a sink found that any sink border group was empty, it would chose the distance between the farthest vertex of the border and itself as current radius. In order to solve the center communication void problem, the sink, nearest to the center point of the rectangle in each border group checks if the point has been covered by themselves. If not, they adjust their radio radius equal to the distance between the center point and itself. After these measures, the border constraint problem will be improved greatly.  
Let the vertices of the rectangle work area _vert\({}_{i}\)_, \(i\) {_I,II,III,IV_}, _I,II,III,IV_ denote the quadrants.  
```
01://(a) sink border group absence
02:ifi==\(\varnothing\)then
03:for all\(w\), \(w\in S_{i}\)
04://i \(\in\) {Topside, Underside, Lefside, Rightside}
05: find out the sink j which nearest to the corresponding vertex k then
06:if\(r\)/distance(j,k)then
07:\(r\)/=distance(j,k)
08:endif
09:endfor
100:endif
11://(b) center void
```  
**Algorithm 3** sink \(w\) decides its radius considering border constraint problem (Anticlockwise orientation mode)  
Figure 8: Border constraint problem resulting of sink communication void. (a) sink border group absence. (b) center void.  
12. **for all**\(w\), \(w\in S_{i}\)
13. **find out** the sink \(m\) which nearest to the center vertex \(O\) then
14. \(//\)\(m\in\{\)_Topside_, _Underside_, _Lefside_, _Rightside_\(\}\)
15. **if**\(r_{m}\)\(<\)_distance_(\(m\),\(O\)) **then**
16. \(r_{m}\)\(=\)_distance_(\(m\),\(O\))
17. **Endif**
18. **endfor**
## 3 Implementation
### Discretization Using Isoparametric and High-Order Spectral Elements in the SBFEM
The \(\eta\) roots of the Lobatto polynomial corresponding to the location of the nodes were found by using GLL quadrature points, and these points are included in Table 1. For an element of order \(p\), the \(p+1\) GLL quadrature points are the roots of the polynomial (\(1-\eta)2L^{\prime}p(\eta)\), where \(Lp(\eta)\) is the Legendre polynomial of order \(p\). For another order, the same procedure would be repeated. Figure 2: Lagrange shape functions for \(GLL\) quadrature points. The shape functions' order of interpolation for spectral elements is evaluated by comparing the computational time and effectiveness (relative error with respect to analytical solution) for equivalent or better results to those obtained with isoparametric elements of order two. Table 2 shows the shape functions of one spectral element with four nodes (\(p=3\)), and Figure 2 shows an example of Lagrange interpolation for order three (\(n=4\)) and order six (\(n=7\)), based on the GLL approach. The shape functions can be determined for the desired number of nodes, discretizing the entire thickness with a single element. If the order of one element is increased by one, a new additional node should be inserted, and all internal nodes must be repositioned. To calculate \(N\), \(\eta\) with the transformation of coordinates for each element, we use the Jacobian matrix described in Equation (17),  
\[|J|= \begin{vmatrix}\partial_{\eta}y&\partial_{\eta}z\\ \partial_{\xi}y&\partial_{\xi}z\\ \end{vmatrix} = \begin{vmatrix}\partial_{\eta}y&\partial_{\eta}z\\ \partial_{z}y&\partial_{z}z\\ \end{vmatrix} = \begin{vmatrix}y_{\eta}&0\\ 0&1\\ \end{vmatrix} =y_{\eta}=\frac{l}{2}. \tag{17}\]  
In the case of the plate, the transformation of coordinates for an element is performed via Equation (18),  
\[y=\frac{l}{2}(\eta+1), \tag{18}\]  
where \(l\) is the length of the element.
## 4 Scheme Design: MPMF
### Design Abstraction  
The message-forwarding policy based on movement patterns (MPMF) considers the following cases when selecting a relay node for receiving a message:  
* The MPMF policy decides the best relay that is one out from a candidate set of one- or two-hop relay nodes which are moving closer to the destination and have frequent contacts with the destination node. If the candidate node is found, any message-carrying node should forward the messages in buffer to this candidate node as it is going to leave beyond the coverage of a two-hop distance and move closer to the destination.
* In presence of only one-hop relay with respect to a message-carrying node, a one-hop relay node is selected by considering its location and destination's movement. A message-carrying node considers the destination's and one-hop relay's moving direction to take a forwarding decision. If the one-hop relay and the destination  
Figure 1: (**a**) Front-side scanning with respect to a destination node, and (**b**) geometric angle formation at one- and two-hop distance.  
node are both moving in the same direction, the one-hop relay can be selected as a candidate node.
* An additional metric, i.e., centrality of a node, is used to avoid the situation that a message-carrying node continually carries its message if the one-hop relay is not moving toward the destination node. The centrality metric measures the ability of a node to communicate with other nodes in a network. A relay node with a higher centrality implies its effectiveness to deliver messages to the destination node and can be considered the best candidate node for the next message-forwarding action.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 3 Common Classification of Photocatalysts
### Oxide Photocatalyst
#### 3.1.2 Bi\({}_{2}\)O\({}_{3}\)-Based Photocatalyst  
Bi-based oxides are an important class of functional materials that are widely used in many fields [64]. For example, they are used as electronic ceramic materials (zinc oxide varistors, ceramic capacitors, ferrite magnetic materials), electrolyte materials, high-temperature superconducting materials, and materials for photoelectric conversion. Among them, Bi\({}_{2}\)O\({}_{3}\) possesses its own unique properties, such as a high electrical conductivity, low energy bandgap (about 2.8 eV), and low energy band structure, which means that Bi\({}_{2}\)O\({}_{3}\) can absorb visible light in the solar spectrum. Therefore, Bi\({}_{2}\)O\({}_{3}\) has great potential for photocatalysis application [65; 66].  
The semiconductor Bi\({}_{2}\)O\({}_{3}\) mainly has four crystal forms, including \(\alpha\), \(\beta\), \(\gamma\), and \(\delta\). The band gaps of \(\beta\)-Bi\({}_{2}\)O\({}_{3}\) and \(\alpha\)-Bi\({}_{2}\)O\({}_{3}\) are 2.58 eV and 2.85 eV, respectively, which can absorb visible light with wavelengths exceeding 400 nm [65]. \(\alpha\)-Bi\({}_{2}\)O\({}_{3}\) is the most widely used among these four different Bi\({}_{2}\)O\({}_{3}\) semiconductors in photocatalytic reactions, since \(\alpha\)-Bi\({}_{2}\)O\({}_{3}\) is the most thermodynamically stable among these four crystal structures [67].  
The development of Bi-based semiconductor materials obviously solves the problem of the visible light absorption of TiO\({}_{2}\). However, a low quantum efficiency and photogenerated carrier recombination is still an urgent problem that needs to be solved for the Bi-based photocatalysts. The doping modification of the photocatalyst is an effective method for improving its photocatalytic performance. Commonly used modification methods include semiconductor surface photosensitization, semiconductor surface noble metal deposition, metal/nonmetal doping, and complex semiconductor modification. To date, Bi-based photocatalysts have achieved a variety of important research results in the applications of atmospheric purification, organic wastewater treatment, heavy metal ion removal, and sterilization [68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84].  
Figure 2: Various modification methods of a semiconductor photocatalyst.
# Advances in High-Precision NO2 Measurement by Quantum Cascade Laser Absorption Spectroscopy
## 1 Introduction
LODs below 10 pptv using QCLAS have been achieved with very long optical path length of 200 m [33; 34]. The CLD, CES and TDLAS techniques have been widely used and their performance investigated in a significant number of laboratory studies and inter-comparison experiments [21; 35; 36; 31]. In contrast, reviews of QCLAS for high-precision NO2 detection are sparse, despite its demonstrated high sensitivity and selectivity [32]. Existing uncertainties related to measurements and calibration methods for direct ambient NO2 measurements is the rational for the recent MetNO2 project of the European Metrology Programme for Innovation and Research (EMPIR), funded by the European Association of National Metrology Institutes (EURAMET). More accurate measurements of NO2 will bring greater confidence in identified trends in air quality and support the development and implementation of effective, evidence-based mitigation policies needed to reduce pollution levels. Within this context, the following paper provides details on the development, characterization and validation of a new QCLAS instrument, including the analysis of a two-week field inter-comparison at the suburban site Dubendorf (Switzerland).
# Relevance of Surface Electromyography Assessment and Sleep Impairment in Scoliosis: A Pilot Study
## 2 Materials and Methods
### Evaluation
#### 2.2.4 Electromyography Assessment  
The examination also included an electrical activity assessment of the paravertebral muscles for all subjects included in the study. The evaluations were performed in orthostatic position and in flexion and hyperextension of the trunk. The data were collected in two distinct moments: before (T1) and after (T2) completing the therapeutic protocol. The rehabilitation program lasted 12 months.  
The evaluation was performed using surface electromyography. The modular and portable BIOFEEDBACK 2000 X-Pert system(Bifofeedback Xpert-the multi-media system from Schuhfried-Modling, Austria) was used in the electromyography evaluation. The collected data was transmitted via Bluetooth to an associated computer. The software processed the information and then translated it into diagrams. The EMG module benefited from 2 channels (EMG 1 and EMG 2). EMG channel 1 was positioned on the right side of the rachis, while EMG 2 was positioned on the left side.  
For a correct and unaltered measurement, adequate tegument cleaning (removal of dust or grease) and placement of electrodes were necessary. As electrodes are positioned on the surface of the skin, the method is noninvasive.  
The position of the electrodes followed the paravertebral muscles at a distance of 2 cm from the spine's axis; electrodes were symmetrically placed on the right and left side of the scoliotic's curvature apex. A neutral electrode was positioned on the spinous process of the C7 vertebra (Figure 2).  
Subjects were asked to perform one trunk flexion before valid measurement in order to confirm the correct placement of the electrodes.  
The amplitude of the electrical signal was the parameter evaluated from the EMG spectrum. A mean value and a symmetry index (left/right) were calculated. The symmetry index could offer possible leads regarding the degree of muscle imbalance. The EMG evaluation process lasted for 1 min and had a frame frequency of 0.25 ms.  
Strong amplitude variations of the collected EMG signal (mV) imposed the calculation of mean values in relation to maximum amplitude values. A range of \(\pm\)10% of the maximum amplitude was considered.
# A Deep-Learning-Based Quality Control Evaluation Method for CT Phantom Images
## 2 Materials and Methods  
### CT Phantom Image Acquisition  
Phantom images for CT QC were acquired from those utilized in real tests spanning from 2008 to 2022. CT scans from three hospitals and featuring nine different machines were employed for image acquisition. The machines included the Discovery CT750 HD(GE Healthcare, Waukesha, WI, USA), LightSpeed VCT (GE Healthcare, Waukesha, WI, USA), Optima CT660 (GE Healthcare, Waukesha, WI, USA), SOMATOM Definition (Siemens Medical Systems, Forchheim, Germany), SOMATOM Definition AS (Siemens Medical Systems, Forchheim, Germany), SOMATOM Definition AS Plus (Siemens Medical Systems, Forchheim, Germany), Sensation 16 (Siemens Medical Systems, Forchheim, Germany), SOMATOM Definition Flash (Siemens Medical Systems, Forchheim, Germany), and IngenuityCT (Philips Healthcare, Cleveland, OH, USA). An AAPM Model 76-410 CT Performance Phantom (Fluke Corporation, Everett, WA, USA) was used for image acquisition, as depicted in Figure 1.  
The imaging conditions were standardized to 120 kVp, 250 mAs, with a beam width of 10 mm. The exposure field was set to over 25 cm, with a pre-exposure field of 25 cm. Standard reconstruction algorithms were applied accordingly, as illustrated in Figure 2 [13].  
All images submitted for medical image evaluations had undergone assessment by clinical experts at the respective institutions. The images comprised those from CT equipment that successfully passed QC and those from equipment that failed QC and required repair or disposal. Given the relatively small number of images from failed equipment compared to those from passed equipment, a larger proportion of images were obtained from the latter category. Furthermore, while there was an initial intent to incorporate images deemed as passed by clinical experts but failed by medical image evaluation institutions, there was alignment between the assessments of the experts and the institutions, precluding the acquisition of separate datasets for such instances.