esa-sceva/satcom-chunk-collection huggingface dataset:
# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
## 3 Star Identification
### Impact of False Stars on Identification Rate  
When a star sensor images the sky, the star image may contain not only stars but also false stars such as satellites, planets, and other celestial bodies which are not included in the star catalog. These false stars could lead to redundant and false matching during the process of star identification. To evaluate the impact of false stars on the identification rate of the proposed algorithm, varying numbers of false stars are added to 1000 frames of actual star images. The average identification rate under different false star conditions is recorded and compared with other subgraph isomorphism algorithms. The experimental results are shown in Figure 10.  
Figure 9: Identification rate with different magnitude errors.  
The results in Figure 10 show that, under the same experimental conditions, the identification rate of the proposed algorithm is significantly higher than that of the other algorithms. As the number of false stars increases, the identification rate of all algorithms decreases, but the decline is slowest with the proposed algorithm. Moreover, with the same number of false stars, the identification rate of the proposed algorithm remains higher than the others. The analysis of the experimental results is explained below. The Mortari algorithm performs star identification across the entire celestial sphere, using the matching group composed of primary and accompanied stars. When a bright false star is treated as the primary or accompanied star, it easily causes redundant and false matching, making the algorithm less robust against false stars. The You, Liu, and Wang algorithms perform coarse identification over the entire celestial sphere first, then fine identification within a localized area, reducing the retrieval range for navigation stars during the fine identification, which offers some robustness to false stars. However, the proposed algorithm only performs star identification within the basic unit of the navigation feature library, significantly reducing the retrieval range compared to other algorithms, making the algorithm less susceptible to false stars and resulting in fewer redundant and false matches and stronger robustness.
# Simultaneous Detection of Nine Key Bacterial Respiratory Pathogens Using Luminex xTAG(r) Technology
###### Abstract  
Early diagnosis and treatment are crucial to the outcome of lower respiratory tract infections (LRTIs). In this study, we developed an assay combining multiplex PCR and Luminex technology (MPLT) for the detection of nine important respiratory bacterial pathogens, which frequently cause LRTIs. These were _Streptococcus pneumoniae_, _Moxacillin catarrhalis_, _Staphylococcus aureus_, _Streptococcus pyogenes_, _Haemophilus influenzae_, _Mycoplasma pneumoniae_, _Legionella_ spp., _Pseudomonas aeruginosa_, and _Klebsiella pneumoniae_. Through the hybridization reaction between two new synthesized multiplex PCR products and MagPlex-TAG Microspheres, we demonstrate that the detection limits for these nine pathogens were as low as \(10^{2}\)-\(10^{3}\) CFU/mL. Furthermore, 86 clinical bronchoalveolar lavage fluid specimens were used to evaluate this method. Compared with the results of nine simplex real-time PCR reactions targeting these nine pathogens, this MPLT assay demonstrated a high diagnostic accuracy for _Streptococcus pneumoniae_ (sensitivity, 87.5% and specificity, 100%). Furthermore, sensitivity and specificity for the other eight pathogens all attained 100% diagnostic accuracy. In addition, the consistency between MPLT and the nine real-time PCR reactions exceeded 98.8%. In conclusion, MPLT is a high-throughput, labor-saving and reliable method with high sensitivity and specificity for identifying nine respiratory pathogens responsible for LRTIs. Indeed, this assay may be a promising supplement to conventional methods used to diagnose LRTIs.  
Keywords:respiratory pathogens; lower respiratory tract infections; multiplex PCR; Luminex; detection method +
Footnote †: journal: Journal of Environmental Research and Public Health
## 3 Diversity and Reliability
### Throughput-Reliability Trade Off  
The objective of the analysis is to test the cost of considering a reliability-oriented system model that has to bear on its average system throughput while increasing the number of RRHs cooperation to guarantee ultra-reliability. We implement a Monte Carlo-based computer level simulation to study the fundamental trade-off between reliability and average system sum throughput in CRAN cooperating mode for each scheme, and discuss in Section 4. Note that it is difficult to evaluate this analytically. In the simulation, we test the reliability with the following metrics:  
\[Rel_{\phi}^{ref}=1-\mathbb{P}(\text{SIR}_{\phi}<\theta)=1-\text{F}_{\text{SIR} _{\phi}}(\theta). \tag{18}\]  
where \(\phi\in\{\text{S, TAS, MRT}\}\) respectively, \(\text{SIR}_{\phi}\) is given in (1) and \(\text{F}_{\text{SIR}_{\phi}}\) is the corresponding outage probability and \(\theta\) is the SIR threshold. Next, we evaluate the average system sum throughput with the following metrics:  
\[\text{TP}_{\phi}=R_{\phi}^{ref}+R_{\phi}^{A}, \tag{19}\]  
where \(R_{\phi}^{ref}\) is the rate of intended URLLC user and \(R_{\phi}^{A}\) is the corresponding average sum rate of non-cooperating RRHs active users. Then,  
\[R_{\phi}^{ref}=\text{Rel}_{\phi}^{ref}\log_{2}(1+\theta), \tag{20}\]  
\[R_{\phi}^{A}=\mathbb{E}\Big{[}\sum_{i=k+1}^{N}\log_{2}(1+\text{SIR}_{\phi}^{i })\Big{]}, \tag{21}\]  
Finally, from (19)-(21) we have that,  
\[\text{TP}_{\phi}=\text{Rel}_{\phi}^{ref}\log_{2}(1+\theta)+\mathbb{E}\Big{[} \sum_{i=k+1}^{N}\log_{2}(1+\text{SIR}_{\phi}^{i})\Big{]}. \tag{22}\]  
Note that as \(k\) increases, the reliability of intended user increases while overall average system sum throughput decreases.
## 3 System Model
### Trading Model  
In Figure 1, three entities are involved in each transaction: the requester gateway (ReqGW), provider gateway (ProGW) and Smart Market Gateway (SmGW). \(ProGW_{j}\), being the resource supplier (seller), first feeds the amount of the resource (e.g., energy) it is willing to sell out to the \(SMP\) resource chamber, such as the smart grid (energy grid), via a transmission channel. \(ProGW_{j}\) then broadcasts the offerings anonymously to the blockchain auction board, also including the price, volume (amount), unit and type of the resource to be sold. \(ReqGW_{i}\), being the potential resource requester (buyer), receives the copy of \(ProGW_{j}\)'s broadcasted message. If \(ReqGW_{i}\) selects \(ProGW_{j}\) as the potential supplier, then it sends a trading negotiation message to the respective \(ProGW_{j}\) to initiate the trading negotiation process. After the successful negotiation, \(ReqGW_{i}\) releases the agreed price, and the resource is released by \(ProGW_{j}\), thereby completing the trading activity.
# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 2 Analysis of Coal Loading Operations
### Loading Operation Notification  
Currently, in scenarios with multiple loading bunkers, customers, and vehicles, loading operation notifications are manually generated by staff, who rely on their own experience. This subjective and uncertain method frequently leads to extended vehicle queues at the loading bunker, awaiting their turn for loading. Hence, it is crucial to holistically consider customer orders, prioritization, vehicle loading capacity, arrival time window for loading, loading bunker capacity, and other variables to formulate an optimal loading schedule. This strategy enables efficient and strategic allocation of loading resources, thereby improving loading efficiency, guaranteeing prompt order deliveries, and mitigating resource squandering and cost escalation. Figure 2 illustrates the notification process for multi-customer loading operations within the coal intelligent loading system.  
Figure 2: Multi-customer loading operation notice of coal intelligent loading system.  
Coal commodities are traded via auctions, and customers receive notifications to dispatch vehicles for coal loading. Typically, the loading operation notification is issued by the coal enterprise following the customer orders attained from the coal auction transaction. It provides instructions to drivers and relevant personnel on coal loading. Assume the existence of customers indexed by \(i(i=1,2,\cdots,N)\), transport vehicles indexed by \(j(j=1,2,\cdots,M)\), and loading bunkers indexed by \(k(k=1,2,\cdots,K)\) in the enterprise. The multi-customer multi-vehicle configurations can be loaded at any of the \(K\)-loading bunkers. Establish the loading plan based on various factors to determine when the \(j\) vehicle of customer \(i\) will be loaded at the \(k\) loading bunker, and subsequently issue the loading operation notification. The notification contains information such as the loading bunker serial number \(k\), customer number \(i\), vehicle number \(j\), loading operation time \(t_{ij}^{k}\), loading start time \(t_{s}\), and loading end time \(t_{e}\). The loading plan is issued as shown in Table 1.  
\(T_{ij}\) represents the delay time for vehicle loading in this context. Specifically, it specifies that after loading bunker \(k\) is finished for customer \(n\) vehicle \(m\), the loading process of vehicle \(j\) customer \(i\) commences at time \(t_{s}\). Subsequently, upon the completion of the loading process after a duration of \(t_{ij}^{k}\), the vehicle departs from the enterprise. At this point, \(T_{ij}\) signifies that the actual loading duration of vehicle \(j\) serving customer \(i\) exceeds the anticipated loading time upon arrival at the enterprise.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 6 Discussion
### Choice of a Geoid Model  
The accuracy of marine geoid models is often unknown, as the conventionally used precise GNSS-leveling control points cannot be used for validation purposes over marine areas. It can be assumed that the deficiencies in models may reach up to a few decimeters in the shorter wavelength spectrum. For instance, [11] and [15] showed errors over a decimeter in a marine gravity data void area in the NKG2015 geoid model (in the eastern Gulf of Finland, cf. Figure 2). In contrast, the Estonian national geoid model EST-GEOID2017 [58] appears to perform better, due to additional marine gravity data being included in the model development (see [11], who also provide a comprehensive review of these two models and their differences). The differences between the two models along the vessel's routes are shown in Figure 20b.  
Figure 19: (**a**) Histograms of residuals (all campaigns combined): the impact of the data filtering approach, and (**b**) a comparison between residuals (a section of campaign C3) resulting from the two approaches.  
For the next experiment, the NKG2015 geoid model was exchanged for the EST-GEOID2017 during data processing. The resulting discrepancies are compared in Figure 20a. Note that the red histogram differs from those in Figure 16b, Figure 18a, and Figure 19a, since the comparison was conducted over a reduced area (EST-GEOID2017 does not extend southward from \(57^{\circ}\) N). The comparison in Figure 20a demonstrates the slightly better performance of the EST-GEOID2017 model, which is consistent with previous studies' results.  
Since the geoid models contain errors, it would be interesting to know how model errors influence the resulting SSH data. This question can be investigated, as differences exist between the two used geoid models. Figure 20b suggests that the data filtering error propagation is negligible (as assumed in Section 5.4.1), because the two SSH datasets are practically the same. This finding indicates that if a geoid model has reasonable accuracy, the model's inaccuracies do not influence the derived SSH (by reducing SSH to residuals for data filtering), which also implies that errors in DT do not propagate significantly either. The results would likely be poorer by neglecting geoid and DT information entirely during data processing (recall the example in Section 6.2).
###### Abstract  
In this in vitro study, we assessed the color stability of an A-2186 room-temperature-vulcanizing (RTV) silicone elastomer by incorporating silver nanoparticles during accelerated artificial aging. Using five intrinsic silicone pigment types, including no pigment (colorless), red, blue, mocha, and a combination of the three, we created 160 disk-shaped specimens. These were evenly distributed across 20 experimental groups, each containing 8 samples (\(n=8\)). The specimens underwent aging for 250 and 500 h in an artificial aging chamber. A colorimeter was used to measure the values of L'a\"b\" according to the Commission Internationale de L'Eclairage (CIE) standards. The 50:50% perceptibility threshold (\(\Delta\)E* = 1.1) and acceptability threshold (\(\Delta\)E* = 3.0) were used in the interpretation of the recorded color differences. At the 0.05 level of significance, the one-way analysis of variance (ANOVA) and Tukey post hoc test were used in the statistical analysis. Fourier-transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR) showed that 0.2% AgNPs after 500 h of aging protected the silicone elastomer matrix and all characteristic bonds of the silicone elastomer. In contrast, silicone without AgNPs showed the distortion of all bonds after 500 h. Chromatic alterations (\(\Delta\)E* > 0) were observed in all specimen groups, surpassing the perceptible threshold (1.1 units), except for mocha, with 0.2% AgNPs after 250 h of aging, which remained below the perceptible threshold (\(\Delta\)E* = 0.97). All groups demonstrated \(\Delta\)E* values below the acceptable threshold, except for the red color, which exhibited a highly significant color change (\(p=0.000\)). This study determined that all specimens, including colorless silicone, underwent color changes (\(\Delta\)E* > 0), with red displaying a notably significant chromatic alteration. Additionally, AgNPs demonstrated substantial protection of the silicone and reduced the color change across all groups and colors, with enhanced efficacy corresponding to higher AgNPsN concentrations (0.2% AgNPs > 0.15% AgNPs > 0.1% AgNPs).  
color stability; silver nanoparticles; maxillofacial silicone; artificial aging; ANOVA Article
# Electrodegradation of Selected Water Contaminants: Efficacy and Transformation Products
## Abbreviations  
The following abbreviations are used in this manuscript: ACN--acetonitrile; AOPs--advanced oxidation processes; APPE--alkylphenol polyethoxylate; BDD--boron-doped diamond; BSTFA--bis(trimethylsilyl)trifluoroacetamide; BTA--1H-benzotriazole; DAD--diode-array detector; DBP--dibutyl phthalate; ECHA--European Chemicals Agency; EF--electro-Fenton; EO--electrooxidation; ESI--electrospray ionisation; EU--European Union; GC--gas chromatography; HLB--hydrophilic-lipophilic balance; HPLC--high-performance liquid chromatography; HRMS--high-resolution mass spectra/spectrometry; LC--liquid chromatography; LD\({}_{50}\)--lethal dose for 50% of test organisms; LOD--limit of detection; LRMS--low-resolution mass spectra/spectrometry; MAX--mixed anion exchange; MeOH--methanol; MMO--mixed metal oxide; MQ--MilliQ ultrapure water; MS--mass spectrometry; MS/MS--tandem MS; NPDE--nonylphenol diethoxylate; n-NP--_nt_-nonylphenol; NPPE--nonylphenol polyethoxylate; n-OP--_nt_-octylphenol; t-OP--_tert_-octylphenol; t-OPME--_tert_-octylphenol monoethoxylate; OPPE--octylphenol polyethoxylate; t-OPPE--_tert_-octylphenol polyethoxylate; PIS--product ion spectra; PTFE--polytetrafluoroethylene; RSD--relative standard deviation; SIM--selected ion monitoring; SPE--solid-phase extraction; SRM--selected reaction monitoring; SVHC--Substance of Very High Concern; TIC--total ion current; TIP--2,4,6-triiodophenol; TMCS--trimethylchlorosilane; TOC--total organic carbon; TOF--time-of-flight MS; TP--transformation product; UHPLC--ultrahigh-performance liquid chromatography; UV--ultraviolet light/spectrum; UV-C--ultraviolet spectrum C; VFCW--vertical flow constructed wetland.
## 4 Analysis and Results
### Performance Results
#### 4.2.2 Storage Overhead  
The storage overload specifies the total cost of bits required to be stored in each entity. Table 5 shows the storage overhead for the client, LB, AB, and CB. In the proposed scheme, each client (publisher or subscriber) is required to store its actual identity (ID\({}_{\text{C}}\)), ID\({}_{\text{B}}\), AID\({}_{\text{C}}\), H(MID\({}_{\text{C}}\)), and SK\({}_{\text{AM,C}}\). Since we apply SHA-1 as a type of hash function that produces a hash digest of 160 bits, the size of bits for \(\mid\text{AID}_{\text{C}}\mid\,=\,\mid\text{H(MID}_{\text{C}})\mid\,=\,\mid\text {SK}_{\text{AM,C}}\mid\,=\,\mid\text{160}\) bits. On the other hand, we assume the bits in \(\mid\text{ID}_{\text{C}}\mid\,=\,\mid\text{ID}_{\text{B}}\mid\,=\,\mid\text {16}\) bits. Therefore, the total storage cost for the client is (3 \(\times\) 160) + (2 \(\times\) 16) = 512 bits.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Entity** & **Computation Cost** \\ \hline Client & 11 T\({}_{\text{h}}\) \\ LB & 18 T\({}_{\text{h}}\) \\ AB & 18 T\({}_{\text{h}}\) \\ CB & 7 T\({}_{\text{h}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computation cost for the client, LB, AB, and CB.  
On the other hand, the broker is required to store its actual identity (ID\({}_{\text{B}}\)) = 16 bits and SK\({}_{\text{AM}\_\text{B}}\) =160 bits. Since the AM is deployed with B on the same server, the storage cost of the secret AM parameters SN, MK1, and MK2 are incurred by B. Assuming \(|\,\text{SN}\,|\,=\,|\,\text{MK1}\,|\,=\,|\,\text{MK2}\,|\,=\,32\) bits, the total storage cost for B is 16 + (3 \(\times\) 32) + 160 = 272 bits. However, the storage requirements for FB (LB and AB) will bear the cost of both C and B combined. Thus, the total size of bits for FB is 512 + 272 = 784 bits.
## 4 Improvement of Thermal Performance
### Encapsulation
#### 4.1.1 Microencapsulation  
Microencapsulated PCMs emerge from a coating process of the pure PCM. Droplets or individual particles of the PCM are coated with another material to form capsules of 1-1000 \(\upmu\)m size. The core material can be any solid-liquid PCM, and the shell (container) material can be an inorganic or a polymer material. The most studied inorganic shells are SiO\({}_{2}\) and TiO\({}_{2}\)[23], while the organic shells used are poly(methyl methacrylate) (PMMA) [24], polystyrene [25], melamine-formaldehyde [26], and other, more complicated, structures [27]. There are numerous microencapsulation methods that can be used (Figure 3). The selection of the suitable microencapsulation method depends on the physical and chemical proper ties of both core and shell materials [22] and on the chemical environment for which they are intended to be used, for example in building [28].
## 2 Background
### IoMT Context
#### 2.3.1 IoMT Architecture  
Figure 1 illustrates the IoMT layers. The IoMT architecture operates mainly through four layers [19], as described below.  
* The data collection layer performs the data sensing and collecting activities. It acquires patients' medical parameters from sensors, actuators, edge servers, hand-held devices, and medical sensors via the data sensing acquisition protocols.  
* The network layer transfers the collected medical data from the data collection layer through a wired or wireless network to deliver the data to the third layer (i.e., the data management layer). This layer connects all medical things in the network, and exchanges data.
* The data management layer uses the middle-ware applications and services needed by IoMT applications and users such that the interoperability between the heterogeneous entities being used is ensured in this layer. Other essential services, such as storing, processing, and interpreting the collected medical data, are provided in this layer.
* The application layer supports intelligent interaction between a user and the IoMT system. Here, the user can easily interconnect and manage medical things and display the medical data.
## 6 Results and Discussions
### 85-Bus System
#### 6.2.1. Results without Reducing the Search Space
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **APL (\%)** & **LR (\%)** & **V\({}_{min}\) (p.u.)** & **VD (p.u.)** & **Ann. cost (\$/year)** & **Net sav. (\$/year)** & **Net sav. (\%)** & **SD** \\ \hline Without SCs & 315.714 & - & 0.8713 & 0.8203 & 165,939.28 & - & - & - \\ \hline PGSA [26] & 161.40 & 48.88 & - & - & 98,231.80 & 67,707.4 & 40.8 & - \\ DSA [26] & 144.01 & 54.39 & 0.9224 & - & 97,871.70 & 68,067.6 & 41.02 & - \\ \cline{1-1} TILBO [2] & 143.18 & 54.65 & 0.9242 & - & 96,815.40 & 69,123.9 & 41.66 & - \\ \cline{1-1} SSA [23] & 143.09 & 54.69 & - & - & 91,890.80 & 74,048.5 & 44.62 & - \\ IHA [26] & 144.72 & 54.16 & 0.9370 & - & 91,654.80 & 74,284.4 & 44.76 & 358.240 \\ MBA [28] & 149.79 & 52.55 & 0.9300 & - & 92,831.67 & 73,107.6 & 44.06 & - \\ PODESCA & 148.25 & 53.04 & 0.9207 & 0.3121 & 91,018.47 & 74,920.8 & 45.15 & 0.01208 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results between PODESCA and other methods for the 85-bus system.
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 3 Range Determination for a Specular Target
### Phase-Focused Method  
[PERSON] and [PERSON] [PERSON] and [PERSON] (1983) discussed the SAR focused on point targets. Here we extend the idea to a generic water body of any shape and size by defining the following cost function (\(CF1\)):  
\[CF1(\xi)=\max_{\xi}\left|\sum_{n}\sum_{r}z(n,r)^{*}\times\mathcal{Z}(n,r|\xi) \right|^{2} \tag{2}\]  
where the variable \(\xi\) is again the water level. There are two possible ways to describe this equation: one way is to see it as a phase unwinding function, which rotates each vector \(z\) to zero phase, before stacking them; another way is to describe it as a matched filter of data \(z\) with model \(\mathcal{Z}\). The water level is solved by finding the value of \(\xi\) that maximizes the match.  
The upper plot in Figure 6 is the \(CF1\) for the peanut lake example. The data were simulated for a lake level of 17 cm. \(CF1\) was computed for a range of levels from \(-25\) cm to \(+65\) cm. The cost function peaks sharply at the correct level, which was simulated as 17 cm.  
It is not yet known if the computation required by CF1 is justifiable given the assumptions in the modeling. For example, the model needs map M as an input. Conceivably the map may come from water mapping with optical images. The Sentinel 2A/B constellation, for example, can provide in theory a worldwide water map at five-day intervals (excluding clouds). However, the water boundaries may not be known as precisely if optical and altimeter passages are not simultaneous.  
Another suitable cost function (\(CF2\)) may be defined as follows:  
\[CF2(\xi)=\min_{\xi}\sum_{n}\sum_{r}\left|\left|z(n,r)\right|^{2}-\left| \mathcal{Z}(n,r|\xi)\right|^{2}\right|^{2} \tag{3}\]  
\(CF2\) discards the phase information, relying only on the echo amplitudes. The optimum water level is the one that minimizes the amplitude difference. The cost function is shown in the bottom  
Figure 6: Calculation of the two cost functions described in Section 3.1 for the peanut-shaped lake example.  
panel of Figure 6. The absolute minimum finds the correct water level, 17 cm. The solution is only slightly less peaked than the coherent focus.  
There is no appreciable saving in computational time. The main advantage of _CF2_ is that it leads to a much simplified algorithm, which needs much less reliance on accurate water maps. In the next section a simple algorithm that mimic _CF2_ with very few computations is described.