esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Compression Algorithm
### Performance Analysis
By Lemma 2, any two occurrences of \(w_{i}\) are respectively transformed to \(\alpha\beta\gamma\) and \(\alpha^{\prime}\beta\gamma^{\prime}\) such that \(|\alpha|=|\alpha^{\prime}|\), \(|\gamma|=|\gamma^{\prime}|\), and \(|\alpha\gamma\alpha^{\prime}\gamma^{\prime}|=O(\log n)\). In the case that \(w_{i}\) is not a boundary occurrence, \(w_{i}=a^{+}\lambda b^{+}\) for some string \(\lambda\) and repetitions \(a^{+},b^{+}\). For repetitions \(a^{+}\) and \(b^{+}\), the number of different nonterminals produced by the replacement of \(a^{+}\) and \(b^{+}\) is bounded by \(O(1)\). If \(\lambda\) is a boundary occurrence, this case is the same as the one in which \(w_{i}\) is a boundary occurrence. If \(\lambda\) is not a boundary occurrence, \(\lambda=c^{+}\lambda^{\prime}d^{+}\) for some string \(\lambda^{\prime}\), and \(c\
eq a\) and \(d\
eq b\). In this case, any occurrence of \(\lambda\) inside \(a^{+}\lambda b^{+}\) is transformed to exactly same string. Thus, for a single queue, we can estimate \(\#(w)=\#(w_{1},\ldots,w_{m-1})+O(\log n)=O(m\log n)=O(g_{*}\log n)\). Because the number of queues is at most \(O(\log n)\), the size of the final dictionary is \(O(g_{*}\log^{2}n)\). Finally, we estimate the space complexity of our algorithm. **Theorem 4**. : _The space required by LCA-online is bounded by \(O(g_{*}\log^{2}n)\), where \(g_{*}\) is the minimum grammar size and \(n\) is the input string length._  
Proof. : The number of queues is bounded by \(O(\log n)\) and the length of any queue is \(O(1)\). Thus, required space for the queues is \(O(\log n)\). For the reverse dictionary, the space is bounded by the generated grammar size. By the Theorem 3, the space of the reverse dictionary is bounded by \(O(g_{*}\log^{2}n)\). Thus, the total space is bounded by \(O(g_{*}\log^{2}n)\).
###### Abstract  
There is an increasing interest in biodegradable metal implants made from magnesium (Mg), iron (Fe), zinc (Zn) and their alloys because they are well tolerated in vivo and have mechanical properties that approach those of non-degradable metals. In particular, Zn and its alloys show the potential to be the next generation of biodegradable materials for medical implants. However, Zn has not been as well-studied as Mg, especially for stent applications. Manufacturing stents by laser cutting has become an industry standard. Nevertheless, the use of this approach with Zn faces some challenges, such as generating thermal stress, dress sticking on the device, surface oxidation, and the need for expensive thin-walled Zn tubing and post-treatment. All of these challenges motivated us to employ photo-chemical etching for fabricating different designs of Zn (99.95% pure) stents. The stents were constructed with different strut patterns, made by photo-chemical etching, and mechanically tested to evaluate radial forces. Stents with rhombus design patterns showed a promising 0.167N/mm radial force, which was comparable to Mg-based stents. In vitro studies were conducted with uncoated Zn stents as control and Parylene C-coated Zn stents to determine corrosion rates. The Parylene C coating reduced the corrosion rate by 50% compared to uncoated stents. In vivo studies were carried out by implanting photo-chemically etched, uncoated Zn stent segments subcutaneously in a C57 BL/6 mice model. Histological analyses provided favorable data about the surrounding tissue status, as well as nerve and blood vessel responses near the implant, providing insights into the in vivo degradation of the metal struts. All of these experiments confirmed that Zn has the potential for use in biodegradable stent applications.  
Zinc; magnesium; photo-chemically etching; in-vitro; in-vivo; histology; subcutaneously; Parylene C
# A Comparative Study of Storage Batteries for Electrical Energy Produced by Photovoltaic Panels
## 1 Introduction
### Literature Review
This system redirects excess solar energy to an energy storage system. The stored energy is then efficiently used during the charging of the electric vehicle, thus diminishing dependence on the mains supply to a minimum. In ref. [22], a PI regulator with a fuzzy logic controller is presented for charging batteries using photovoltaic panels. In [23], modern technologies for manufacturing transparent, bi-facial, and flexible photovoltaic panels are presented. These technologies are aimed at improving the energy efficiency of the photovoltaic panels. Ref. [24] explores the connection between individuals who have photovoltaic panels and the probability of them owning a battery-powered electric vehicle. It was found that the probability of a household/family owning an electrical vehicle is significantly increased if the respective house is equipped with photovoltaic panels. Ref. [25] presents an analysis of the charging capacity of LiFeP04 batteries for use in electric vehicles with BLDC-type DC motors. In the abovementioned articles, a variety of applications for electrical energy produced by photovoltaic panels are presented, including its storage in batteries, its use to supply water pumping systems, and its delivery to the electrical grid. By optimizing photovoltaic systems connected with batteries, an increase in the quantity of energy produced by such systems can be achieved, leading to a reduction in the extent of environmental pollution. Research concerning the storage of electrical energy produced by photovoltaic panels requires practical implementation. This article presents a program, developed by the author in MATLAB-Simulink, for controlling the storage in batteries of electrical energy produced by photovoltaic panels.
## 5 Delay Calculation Error Analysis
### Calculation Errors of By-Vehicle Type and By-Bus-Line Type  
Based on the simulation results of the average vehicle delay of 178 sets of examples, we compared and analyzed the delay calculation results by-vehicle type and by-bus-line in the multi-discounting standard. The average errors of the average delay of cars were both 5.69 s. The average errors of the average delay of bus, where the average error of by-vehicle type, was 3.52 s and the average error of by-bus-line type was 3.9 s. There was little difference between the two results, and the error of by-bus-line type was slightly higher by 0.38 s. The main reason for this was that there is a certain error in calculating the total fleet delay, and the number of vehicles on each bus line was relatively small, thus the error of the average delay of buses on each line would be larger.  
The calculation of bus lines took the average delay of each bus line, and the result was more refined, which took into account the difference in passenger capacity of each bus line when carrying out the coordinated control of bus priority, and received a more accurate per capita delay result, thus reducing the per capita delay at intersections. In addition, compared with the single conversion standard delay calculation method, the average error of the average delay of cars and the average delay of buses in bus line reduced by 1.63 s and 17.85 s, respectively, with a reduction rate of 22.27% and 82.07%, which was more accurate. Therefore, for the coordinated control of bus priority, it is appropriate to adopt the calculation method of by-bus line in multi-conversion standard.
## 2 Modeling and Simulation Methods
### Design of the Verification Device
The adoption of different materials will cause slight differences in the device transmission performance; therefore, it is necessary to make brief adjustments to optimize the final size parameters. We designed two sizes of SCLPFs equipped with alumina and PTFE sheets, respectively. They have consistent performance parameters, with only slight differences in the size of the internal conductor segments. In the central low-impedance region, a pair of dielectric sheets are placed on the top surface of the inner conductor and the bottom surface of the outer conductor, separately. The size of the alumina sheets is 8 \(\times\) 5 \(\times\) 0.25 mm\({}^{3}\), and the size of PTFE is 8 \(\times\) 6.5 \(\times\) 0.5 mm\({}^{3}\). The frequency response in Figure 3 shows that two SCLPFs loaded with different dielectrics can achieve good transmission performance, thereinto, the insertion loss is less than 0.1 dB, and the return loss is generally greater than 20 dB from 1.5 to 5 GHz. The simulation results also indicate that the electric field between the dielectric sheets can reach nearly 6000 V/m. Therefore, the gap will experience multipactor first with the increase in input power since the region possesses the largest electric field and the narrowest gap.
# Link Pruning for Community Detection in Social Networks
## 2 Related Work
### Graph Sparsification  
Graph sparsification is a technique that reduces the size of a graph by maintaining the structural information of the graph primarily based on link pruning [16]. This technique can reduce the size of network data represented as a graph and increase the efficiency related to the use of computer resources and operation time. A recent python library for graph sampling called 'Little Ball of Fur' [17] provides representative link sampling methods, such as [18; 19]. The two usual link sampling methods introduced in [18] are the Random Edge (RE) sampling method and the Random Node-Edge (RNE) sampling methods. The RE sampling method samples links independently and uniformly at random, and the RNE sampling method selects nodes and a link that belongs to the chosen node. The link sampling methods introduced in [19] select nodes and a link using an additional induction step. The induction step adds all links that exist between the sampled nodes, and this step is adequate to preserve the original graph structure. For graph clustering with link pruning, there have been several attempts to sparsify graphs [20; 21; 22]. Graph sparsification methods are used for designing a scalable clustering algorithm [20] and a distributed clustering algorithm [21]. In [22], LinkBlackHole* adopted a link sampling method for graph sparsification, where each node samples links with probability that is linear to the log of the degree. This method guarantees that the difference between a sampled graph and an original graph is bounded based on Chernoff bound [23]. Recent studies have developed methods that perform sparsification by maintaining the attributes of a graph, such as a clustering coefficient, connectivity, and the shortest path [16; 24]. We conducted a process of link attribute calculation to remove less significant links. Through this process, rather than maintaining the structural information of the graph, we achieve the goal of performing graph sparsity to efficiently and accurately identify communities.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 6 Measurement Results and Analysis
### Time Dispersion  
The time dispersion parameters along the Tx-Rx separation distance at the three considered industrial environments are presented in Figure 10a-c. From visual inspections of the figures, it can be shown that the RMSDS increases as the Tx-Rx separation distance increases. The RMSDS values vary from 4.1 ns to 19.8 ns, from 10.6 ns to 21.0 ns, and from 3.0 ns to 24.2 ns for the Manufacturing hall at Dokka Fastners AS, the Test room at Dokka Fastners AS, and the NTNU MANULAB environments, respectively. The mean excess delay is less than 5 ns at all locations, as can be seen by the black curve plots in Figure 10a-c. It can be observed that the spreading factor of (8) is less than unity at all locations along with the Tx-Rx separation distances, implying that most of the MPCs fall in the early delay bins with a high power concentration. The minimum, maximum, and average values of RMSDS are listed in Table 5.  
Figure 10: Time dispersion parameters in (**a**) the Manufacturing hall at Dokka Fastners AS, (**b**) the Test room at Dokka Fastners AS, and (**c**) NTNU MANULAB. MED, mean excess delay.  
Figure 9: K-factor along with a measurement distance of 5 m in (**a**) the Manufacturing hall at Dokka Fastners AS, (**b**) Test room at Dokka Fastners AS, and (**c**) NTNU MANULAB.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multicolumn{1}{c}{**K-Factor (dB)**} & \multicolumn{1}{c}{**RMSDS (ns)**} \\  & **(Min., Max., Avg)** & **(Min., Max., Avg.)** \\ \hline Manufacturing hall at Dokka Fastners AS & 1.8, 21.6, 9.6 & 4.1, 19.8, 12.3 \\ Test room at Dokka Fastners AS & 0.9, 19.9, 8.8 & 10.5, 21.0, 15.4 \\ NTNU MANULAB & \(-\)0.3, 25.9, 9.9 & 3.0, 24.2, 13.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: K-factor and RMSD at all measurement locations.
## 3 Results and Discussion
### Forming Properties  
The flow curves, which were calculated using the geometry of the cylindrical hot compression samples and the measured force and strain for all combinations of homogenization heat treatments, are shown in Figure 8. The flow curves show the influence of the experimental addition of RE to the Al-1.4 Fe alloys on their deformation properties. Alloy K3 exhibited the most favorable forming properties when the samples were homogenized at 580 \({}^{\circ}\)C for 6 h (see Figure 8a). The flow stress was slightly lower with a longer homogenization (10 h) at 580 \({}^{\circ}\)C, as can be seen in Figure 8b. The additional homogenization time did not contribute to a significant reduction in flow stress. Therefore, a time of 6 h was chosen as the optimum homogenization time at 580 \({}^{\circ}\)C. Homogenization at 600 \({}^{\circ}\)C led to higher flow stresses during the hot compression tests for both homogenization times and for all alloys with the RE additions (see Figure 8c,d).  
Figure 6: SEM image and EDS mapping of the homogenized reference alloy K2 at a temperature of 580 \({}^{\circ}\)C for 10 h.  
Figure 7: SEM image and EDS mapping of the homogenized reference alloy K3 at a temperature of 580 \({}^{\circ}\)C for 10 h.  
Due to the grain refining effect on the \(\alpha\)-Al grains and formation of discontinuous eutectic structures of the various Fe intermetallics [25,31] that form in Al-Fe alloys, it was expected that the addition of Ce and La would have a favorable influence on the alloys' mechanical properties. Uniform homogenization transforms the lamellar metastable and stable Al\({}_{x}\)Fe\({}_{y}\) phases into short rods, which also improves the mechanical properties of the material. Nevertheless, the formation of Al\({}_{11}\)Ce\({}_{3}\) can reduce the refining effect of the mixed RE combinations. This has a negative effect on the mechanical properties of the alloy, as shown by the agreement of our results with those reported in [1]. The results obtained also show the beneficial use of La as a microadditive in Al-Fe alloys. During the homogenization heat treatment, the La-bearing phases successfully homogenize in the form of small globules (Figures 5 and 7) making the material easier formable due to a content of only small phases and an absence of phases with sharp edges. Ce-bearing phases during the homogenization remain in the form of rods that appear shorter and rounder (Figures 6 and 7), which also improves the formability of the material. Analysis of the forming properties shows that a combined addition of Ce and La is the most favorable RE addition.
## 2 Results and Discussion
### Profiling of the Phenolic Composition
The quantification of phenolics was carried out by comparison with commercial standards, when available. For those compounds that could not be quantified with their corresponding standards, a reference compound was selected based on structural similarity and considering the functional groups that may affect the ionisation properties (i.e., carnosol derivatives were quantified as carnosol, rosmanol derivatives as rosmanol, flavonols as rutin (quercetin-rutinoside), flavones as luteolin-4-glucoside, etc.). Finally, the molecules responding to the electro-spray ionisation (ESI) source in a unique way  
Figure 1: Main rosmary (poly)phenolic compounds. Peak numbers refer to components listed in Table 1.  
with respect to the reference compound of choice, or not reaching the limit of quantification of the corresponding reference compound, were not quantified. The amount of (poly)phenolic compounds in this rosemary extract was 166.32 \(\pm\) 11.05 mg/mL. Although the (poly)phenolic profile of the extract was composed of a high number of different phenolic structures (Table 1), triterpenoids accounted for the 97.2% of this phenolic content (\(161.66\pm 10.64\) mg/mL). Furthermore, this was attributed mainly to the amount of carnosic acid derivatives in the extract (77.1% of total phenolics, Table 2). Flavonoids represented about 1.4% (\(2.38\pm 0.22\) mg/mL) of the total amount of detected (poly)phenolic compounds, followed by triterpenoids (1.3% of total phenolics, 2.10 \(\pm\) 0.25 mg/mL). Phenolic acids made up only 0.1% of the total phenolic fraction. The amount of phenolic compounds previously reported for other rosemary extracts was quite variable and ranged from \(\sim\)39.3 mg/g [18] to 523 mg/g [12], with some extracts showing a similar content to that reported here [3]. In accordance with our data, other rosemary extracts were composed mainly of carnosic acid, followed by carnosol and other triterpenoids, with flavonoids as minor components [3; 12; 18]. It should be noted that the amount and relative contribution of each class of (poly)phenolic compounds to rosemary extracts have been reported to be dependent on the extraction procedure and solvent used [17; 18; 19]. In addition, irrigation conditions, harvest time, storage conditions, and drying treatments are also factors that may affect the final phenolic composition of rosemary extracts [18; 20]. Figure 2: Some of the (poly)phenolic compounds present in the rosemary extract.
# A Spaceborne Mounting Method for Fixing a Cubic Fabry-Perot Cavity in Ultra-Stable Lasers
###### Abstract  
A spaceborne mounting method for fixing a 100 \(\times\) 100 \(\times\) 100 mm cubic Fabry-Perot (FP) cavity is presented. The method constrains the FP cavity in eight directions with a titanium bracket, PEEK gaskets, and vacuum glue. Three criteria were proposed for judging whether the FP cavity is offset or not during aerospace vibration tests. Results indicate that the proposed method is a reliable, robust, and stable way to fix a cubic FP cavity in ultra-stable lasers (USLs). This approach paves the way for the use of USLs in space.  
ultra-stable lasers; FP cavity; space applications
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
## 1 Introduction
### Our Contributions  
In this paper, we propose a new data-sharing scheme, which is called unified ciphertext-policy weighted attribute-based encryption (UCP-WABE), in cloud computing. UCP-WABE achieves both security and high performance. In this scheme, every attribute has its own weight depending on its importance defined in the system. The data providers share their data under their weighted access policies. When there is a mutual sub-policy among the weighted access policies, UCP-WABE optimizes the encryption of the data. UCP-WABE is proven to be selectively secure [23] on the basis of CP-WABE [16]. We carry out experiments for UCP-WABE, and the implementation results exhibit that UCP-WABE has better efficiency.
# Asian Affective and Emotional State (A2 ES) Dataset of ECG and PPG for Affective Computing Research
###### Abstract  
Affective computing focuses on instilling emotion awareness in machines. This area has attracted many researchers globally. However, the lack of an affective database based on physiological signals from the Asian continent has been reported. This is an important issue for ensuring inclusiveness and avoiding bias in this field. This paper introduces an emotion recognition database, the Asian Affective and Emotional State (A2 ES) dataset, for affective computing research. The database comprises electrocardiogram (ECG) and photoplethysmography (PPG) recordings from 47 Asian participants of various ethnicities. The subjects were exposed to 25 carefully selected audio-visual stimuli to elicit specific targeted emotions. An analysis of the participants' self-assessment and a list of the 25 stimuli utilised are also presented in this work. Emotion recognition systems are built using ECG and PPG data; five machine learning algorithms: support vector machine (SVM), k-nearest neighbour (KNN), naive Bayes (NB), decision tree (DT), and random forest (RF); and deep learning techniques. The performance of the systems built are presented and compared. The SVM was found to be the best learning algorithm for the ECG data, while RF was the best for the PPG data. The proposed database is available to other researchers.  
affective computing emotion recognition system; physiological signals +
Footnote †: journal: _articles
## 3 Results and Discussion
### Biogas and Methane
[PERSON] (2019) [22] employed the pre-treatment at a SCO\({}_{2}\) to MSS ratio of 0.55/1 (by volume), producing 0.62 dm\({}^{3}\)/gVS biogas containing approx. 78% CH\({}_{4}\)[22]. The non-pretreated MSS only yielded 0.43 dm\({}^{3}\)/gVS biogas. Evidently, biogas yield improvements were higher in batch experiments than in the present study. Many researchers have noted the relationship between the OLR and the yields of AD-produced biogas [5,30,31]. [PERSON] et al. (2007) [30] examined the effect of different ORLs (0.5, 1.0, 2.3, 2.9, 3.5, and 4.3 gVS/dm\({}^{3}\)-day) on the AD of municipal waste mixed with municipal sewage. They achieved the maximum biogas production of 0.36 dm\({}^{3}\)/gVS-day at ORL = 2.9 gVS/dm\({}^{3}\)-day--being close to the optimal ORL level found in the present study. The methane content of the biogas ranged from 68 to 72% [30]. Researchers have pointed out that reactor overload can lead to diminished pH and the inhibition of methane-producing microbes, which are the main producers of biogas from acetic acid [32]. Hence, the OLR levels in excess of what can be absorbed by methanogens can lead to the cessation of methane production during AD, as well as to hydrogen and VFA accumulation [27]. Exemplary effects of the intensification of biogas/methane production from sewage sludge as a result of various pre-treatment methods are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 3 Results  
In order to verify the effectiveness of the proposed FDI algorithm in the environment of vibration interference and multipath effect, the generation of simulated faults is established, and the simulation results show that the proposed method can quickly detect and isolate faults.  
The simulation and actual data experiment of autonomous vehicle navigation with fault are established to simulate the parameters of vehicle autonomous navigation sensor in a fault environment and collect vehicle navigation data in complex scenes. The proposed FDI algorithm based on data validation is in the next section.  
Figure 3: Structure diagram of MSS fault detection.