esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Identification and Quantitation of Compounds and OAV Analysis

Table 1: **(a)** GC-O identified odor-active compounds in RRT samples with the method of aroma intensity. **(b)** Identification, standard curves and concentration (mg/kg) of compounds detected in RRT samples. In addition to ester compounds, acids were another important class of aroma active compounds found in the RRT samples. Seven acids were identified, which are summarized in Table 1(a). These included acetic acid, 3-methylbutanoic acid, hexanoic, E-3-hexenoic acid, heptanoic acid, and octanoic acid. According to the AI size of the aroma substance, acetic acid, 3-methylbutanoic acid, and hexanoic acid were the most powerful odor-active compounds contributing to the aroma profile of RRT fruits. The concentrations and odor activity values (OAVs) of the ester compounds obtained by SPME-GC-MS are presented in Table 1(b). A total of 37 volatile compounds in RRT juice samples were quantitated (Table 1(b)). The major volatile compounds of samples were ethyl acetate (45.24 mg/kg), acetic acid (88.72 mg/kg), heptanoic acid (97.12 mg/kg), and 3-methylbutanoic acid (8.69 mg/kg).
## 3 Results and Discussion
### Sensory Properties  
The effects of berry thinning on the sensory properties are shown in Figure 5. Berry thinning improved Shine Muscat grapes' sweetness, flavor, and overall acceptance. BT50 and BT30 gained higher scores for sweetness, flavor, and overall acceptance than BT0. The sweetness score was the highest for BT50, followed by BT30 and BT0. The trend of sweetness scores was similar to that of TSS, showing an increasing trend with increasing berry thinning. The sourness score was the lowest in BT50 and was similar between BT30 and BT50, despite a decreasing trend of TA with increased berry thinning. The firmness and astringency scores were similar between the sample groups. The flavor scores showed a similar trend to that of sweetness. It was assumed that the increasing trend in flavor scores was related to the increasing trend of linalool concentration with increasing berry thinning. Furthermore, linalool oxide seemed to help enhance Muscat flavor in BT30 and BT50, since it was an active odorant in these sample groups. The overall acceptance scores showed a similar pattern to that of sweetness and flavor. The overall acceptance was highly affected by the sweetness and flavor attributes rather than the other attributes.
## 4 Results
### Experimental Genes
In point B, the material in this layer leads to a relatively low pore water pressure, so Gene C  
Figure 11: Calcium carbonate precipitation distribution in soil profile 2 due to the system with Gene B after (**a**) 1 day; (**b**) 3 days; (**c**) 5 days; (**d**) 7 days. Figure 10: Calcium carbonate precipitation distribution in soil profile 2 due to the system with Gene A gene after (**a**) 1 day; (**b**) 3 days; (**c**) 5 days; (**d**) 7 days. has the greatest precipitation rate because the gene expression has a negative correlation with pressure in the range of 0-200 kPa. It can be concluded that in point A, a system with Gene A has the highest precipitation value and rate in both soil profiles, then followed by Gene C and Gene B. As for point B, Gene C has the best performance by combining both soil profiles, followed by Gene A and Gene B. As mentioned above, the depth of point A represents a typical soft clay layer under the bearing strata, so it can be deduced that Gene A has the best stabilisation effect in reducing the settlement of shallow soft clay strata under load. Point B can be seen as a potential location of bearing strata of piles; hence, one can conclude that Gene C is the most suitable gene for stabilising the pile foundation. Combining the precipitation patterns and typical points in both soil profiles, it can be understood that for the strata immediately beneath the ground surface (z = 1 m), there is no significant difference among the tested genes in precipitation value after 7 days. Hence, all three genes are capable of applications considering shallow treatment depth (e.g., road embankment). If the stabilisation rate is considered, Gene A and Gene C are the most suitable for soil profiles 1 and 2, respectively, due to the strong effect in the early stage  
Figure 12: Calcium carbonate precipitation distribution in soil profile 2 due to the system with Gene C gene after (a) 1 day; (b) 3 days; (c) 5 days; (d) 7 days. Figure 13: Variation of calcium carbonate precipitation with time at points A and B in (a) soil profile 1; and (b) soil profile 2.  
of the reaction. As the strata goes deeper (2-5 m), it can be concluded that Gene A is most suitable for the stabilisation for most applications (e.g., shallow foundation), as the stress and settlement in the centre area are the most significant, Genes B and C might be employed when the settlement of the surrounded existing structure is the top priority consideration. For further depth (>6 m), all three genes have much less precipitation as the bacteria number declines sharply. In soil profile 1, Gene A and Gene C have a better effect in stabilising the centre and edge areas, respectively. In soil profile 2, Gene C has the best behaviour with homogenous precipitation laterally. These two genes can be utilised according to the geology conditions when the deeper area of soil needs to be stabilised (e.g., deep foundation).
# Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records
###### Abstract  
The Chicago Array of Things (AoT) is a robust dataset taken from over 100 nodes over four years. Each node contains over a dozen sensors. The array contains a series of Internet of Things (IoT) devices with multiple heterogeneous sensors connected to a processing and storage backbone to collect data from across Chicago, IL, USA. The data collected include meteorological data such as temperature, humidity, and heat, as well as chemical data like CO\({}_{2}\) concentration, PM2.5, and light intensity. The AoT sensor network is one of the largest open IoT systems available for researchers to utilize its data. Anomaly detection (AD) in IoT and sensor networks is an important tool to ensure that the ever-growing IoT ecosystem is protected from faulty data and sensors, as well as from attacking threats. Interestingly, an in-depth analysis of the Chicago AoT for anomaly detection is rare. Here, we study the viability of the Chicago AoT dataset to be used in anomaly detection by utilizing clustering techniques. We utilized K-Means, DBSCAN, and Hierarchical DBSCAN (H-DBSCAN) to determine the viability of labeling an unlabeled dataset at the sensor level. The results show that the clustering algorithm best suited for this task varies based on the density of the anomalous readings and the variability of the data points being clustered; however, at the sensor level, the K-Means algorithm, though simple, is better suited for the task of determining specific, at-a-glance anomalies than the more complex DBSCAN and HDBSCAN algorithms, though it comes with drawbacks.  
sensor network; IoT; anomaly detection; clustering; machine learning; neural networks; Chicago AoT +
Footnote †: journal:  
0000-0001-5000-0001-5000][PERSON]. [PERSON], [PERSON], C.Y.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 6 Conclusions  
Algorithmic metatheorems tell us when and why the divide-and-conquer approach works for certain problems. They are statements following the pattern \"If a problem can be described _in a certain logic_ and can be _decomposed in a certain way_, then the problem can be solved with _a certain amount of resources_\". In the present paper, the focus lay entirely on algorithmic metatheorems where the \"certain logic\" was monadic second-order logic and the \"certain way of decomposing\" graph were tree decompositions. While Courcelle's Theorem tells us that the \"certain amount of resources\" needed in this case are \"linear time\", the first algorithmic metatheorem presented in this paper showed that \"logarithmic space\" also suffices. It turned out that computing tree decompositions is the hard part in the algorithm and if the tree decomposition is part of the input, the complexity drops down to NC\({}^{1}\). Finally, we saw that if the tree decompositions have constant depth, the complexity drops even further, namely to AC\({}^{0}\) or TC\({}^{0}\), depending on whether we just want to determine whether a formula is satisfied or whether it is satisfied by a vertex set whose desired size is given as part of the input.  
During the course of this paper, I tried to give an impression of the many different applications that algorithmic metatheorems for space and circuits have. There were a number of \"low-hanging fruits\": since many problems can be described in monadic second-order logic, literally hundredsof problems can be solved in logarithmic space when the input graphs have bounded tree width. In contrast, the \"high-hanging fruits\" were less numerous (but also more delicious): one can sometimes apply algorithmic metatheorems _internally_ in algorithms even when the input graphs do _not_ have bounded tree width, since one can use a different algorithm for input graphs of high tree width. This allowed us to prove that the even cycle problem can be solved in logarithmic space and to completely classify the prefix classes of existential second-order logic over graphs. Even further removed from the statements of algorithmic metatheorems were applications where the inputs are not even graphs. Nevertheless, algorithmic metatheorems allow one to give simple proofs that the visibly pushdown languages lie in NC1 and that the unary subset sum problem is complete for TC0.  
As a concluding remark, let me point out that this paper focused entirely on decision problems. All of the theorems from this paper and in the world of algorithmic metatheorems in general also holds for \"constructing\", \"optimization\", and \"arithmetic\" versions, where the objective is to construct a solution, to find a solution of a certain size, and to compute the number of solutions with a certain property, respectively.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 4 Design
### Beamforming Matrix and Direction Vector Construction  
With this background on phase mode excitation of circular arrays, we introduce UCA-MUSIC algorithm [34] to solve the direction finding problem. For the purpose of making the transformation of direction vector \(A(\vec{\theta})\) from element space to beamspace, we need to introduce the beamformer \(F_{r}^{\mathrm{H}}\):  
\[B(\vec{\theta})=F_{r}^{\mathrm{H}}\cdot A(\vec{\theta}). \tag{15}\]  
In order to simply the calculation, we also present \(F_{e}^{\mathrm{H}}\), which is a intermediate quantity.  
\[F_{e}^{\mathrm{H}}=C_{v}V^{\mathrm{H}}, \tag{16}\]  
where  
\[C_{v}=diag\{j^{-M},\cdots,j^{-1},j^{0},j^{-1},\cdots,j^{-M}\}, \tag{17}\]  
and  
\[V=\sqrt{N}[w_{-M},\cdots,w_{M}]. \tag{18}\]Equation (17) helps to eliminate the term \((j)^{|m|}\) in Equation (14). In Equation (11), the column vector \(w_{m}^{\rm H}\) has been defined. According to the above discussion, we can get:  
\[F_{e}^{\rm H}A(\vec{\theta})=C_{v}V^{\rm H}A(\vec{\theta})=\sqrt{N}J(\beta)v( \phi). \tag{19}\]  
The information of the azimuthal angle \(\phi\) is only in the vector \(v(\phi)\):  
\[v(\phi)=[\ e^{-jM\phi},\ \ \cdots,\ \ e^{-j\phi},\ \ 1,\ \ e^{j\phi},\ \ \cdots,\ \ e^{jM\phi}\ \ ]^{\rm H}. \tag{20}\]  
Through observation, this is similar in Vandermonde form to the VULA direction vector. \(J(\beta)\) contains the information of elevation angle \(\theta\):  
\[J(\beta)=diag[J_{M}(\beta),\ J_{M-1}(\beta),\ \cdots,\ J_{1}(\beta),\ J_{0}( \beta),\ J_{1}(\beta),\ \cdots,\ J_{M}(\beta)]. \tag{21}\]  
Now, we introduce \(W^{\rm H}\) that has centro-Hermitian rows. The \(F_{r}^{\rm H}\) is constructed by premultiplying \(F_{e}^{\rm H}\) by \(W^{\rm H}\). The beamformer \(F_{r}^{\rm H}\) can be expressed like this:  
\[F_{r}^{\rm H}=W^{\rm H}F_{e}^{\rm H}=W^{\rm H}C_{v}V^{\rm H}. \tag{22}\]  
Thus, the beamspace manifold vector \(B(\vec{\theta})\) will be:  
\[B(\vec{\theta})=F_{r}^{\rm H}A(\vec{\theta})=\sqrt{N}W^{\rm H}J(\beta)v(\phi). \tag{23}\]  
The multiplication of two centro-Hermitian vectors can construct a real-valued beamspace manifold vector. Thus, \(W\) must satisfy \(\widetilde{I}W=W^{*}\):  
\[W=\frac{1}{\sqrt{M^{\prime}}}[\ v(\alpha_{-M}),\ \ \cdots,\ \ v(\alpha_{0}),\ \ \cdots,\ \ v(\alpha_{M})\ \ ], \tag{24}\]  
where \(M^{\prime}\) represents the total number of \(M\). \(M^{\prime}=2M+1\) and \(\alpha_{i}=\frac{2\pi i}{M}(i\in[-M,M])\).  
Through observation, we learn that, during the progress of construction, a real-valued beamspace manifold vector is also actually the progress of smoothing. It helps the algorithm to eliminate the adverse impact of coherent signal in the environment.
# Design, Implementation and Power Analysis of
## 4 Discussion
### Characteristics of Relative Energy Saving Ratios  
Figure 10 shows a plot of the RESR parameters against the number of light bulbs. Figure 10,b, display the increase in \(\mathrm{RESR_{BS}}\) and \(\mathrm{RESR_{MS}}\) with an increase in the number of light bulbs.  
Thus, more energy savings are achieved when the system is deployed with a higher number of light bulbs. It also points out \(\mathrm{RESR_{BS}}\) and \(\mathrm{RESR_{MS}}\) are directly proportional to the power of light bulbs. It can also be noted that like the energy consumption ratio parameters, the relative energy saving ratios shows a typical straight line for 60 W incandescent and 8 W LED bulbs. While considering 0.5 W LED bulbs, a low power watt bulb, the RESR parameters varies in a quite logarithmic curve pattern for up to 8 bulbs. Then, the line approaches a straight line like the 60 W incandescent and 8 W LED bulbs.  
Figure 9: Energy consumption ratio vs. number of light bulbs for: (**a**) Comparison of \(\mathrm{ECR_{BS}}\) of 60 W incandescent Light, 8 W LED and 0.5 W LED light bulbs and (**b**) comparison of \(\mathrm{ECR_{MS}}\) of 60 W incandescent Light, 8 W LED and 0.5 W LED light bulbs.
## 1 Introduction  
The depletion of fossil fuel sources, the wobbling prices of fuels, and the increased pressure regarding environmental and social aspects have increased the industrial focus towards renewable energy resources such as organic waste [1; 2; 3; 4; 5; 6; 7; 8; 9]. Organic residues have been considered an inexpensive, renewable, widely available, and environmentally friendly feedstock for biofuels production [10; 11; 12; 13; 14]. Currently, large quantities of biofuels are generated from first-generation resources, such as starch, corn, and sugar [15; 16; 17; 18; 19]. Although this is a more efficient way of producing fuel compared to fossil fuel sources, the drawback of using first-generation resources is that these products are part of the food supply. The increased demand for first-generation resources negatively affected the availability of food [20; 21; 22; 23; 24]. Additionally, first-generation resources require large amounts of fertile land, and most agricultural land is already occupied. Therefore, a better fitting source for the production of biofuels is organic residues since they have no negative impact on food production or land use, resulting in a more sustainable feedstock utilization for the production of bioethanol [25; 26; 27; 28].  
Lignocellulose is a natural composition consisting of cellulose, hemicellulose, and lignin. The dry weight mainly consists of cellulose, namely 35-50%, a further 20-35% of hemicellulose, and around 10-25% of lignin [29]. Lignocellulosic waste can come from several sources, namely industrial waste (e.g., recycled newspaper, food industry residues, sawdust), agricultural waste (e.g., bagasse, rice straw, corn stover), forestry waste (e.g., wood chips, grasses, hard and softwood), bioenergy crops (e.g., common reeds, switchgrass), and municipal solid waste [30; 31; 32]. The lignocellulosic biomasscan be converted into bioethanol via a biochemical pathway. Biochemical conversion is based on enzymatic hydrolysis of the biomass into sugars. These sugars are fermented, and after distillery of the fermentation liquid, pure ethanol is obtained.  
From the early 2000s to 2012, the biofuel consumption in Europe flattened out after increasing steadily. In 2017, 2718 t of oil equivalents (toe) of bioethanol was consumed for transport in Europe, of which 121 toe was consumed by the Netherlands [33]. Although much research has been done on the ethanol production from lignocellulosic biomass [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44], the economic viability of a bioethanol plant in the Northern Netherlands is yet unknown. Therefore, this research aims to investigate the feasibility of a bioethanol plant treating organic residues (sugar beet pulp and grass straw) in the Northern Netherlands. In this study, the economic analysis is conducted to evaluate the profitability of the plant based on net present value (NPV) and internal rate of return (IRR). This study provides techno-economic information to a broad audience and can also be used as a baseline study for further business investigations.
# PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block
## 3 Results and Discussion
### Comparison of Different Attention Mechanisms  
For the purpose of evaluating the effect of using different attention mechanisms in the SGAB, we conducted an experiment.  
As shown in Table 5, the use of different attention mechanisms in the SGAB could affect the final accuracy of the model. From the experimental results, we achieved the following observations:  
1. The accuracy of only using Channel Attention in the SGAB or Spatial Attention in the SGAB is lower than the accuracy of these modules with Gate Convolution Filters (GCFs) inside. The reason is that the Gate Convolution Filters (GCF) proposed by us allow features related to cracks to flow to the top layers, while inhibiting the features unrelated to cracks, which effectively controls the flowing of features.
2. The accuracy of using both Channel Attention and Spatial Attention with a Gate Convolution Filter (GCF) in the SGAB could achieve the best performance, because this mixed attention not only rearranges the features of different channels, but also selects the channel features related to cracks through the channel gates. Moreover, spatial features related to cracks are selected through spatial gates. Thus, these two features blended together could provide more feature information for the top layers.
## 4 Results
### TSP Results  
Each of the 50 TSP instances consists of 100 cities. An instance is defined by a random distance matrix, such that the distance between cities is a uniformly random integer from the interval \([1,1000]\). The results are shown in Figure 2 with number of evaluations on the horizontal axis at log scale, and average solution cost over 50 runs on the vertical axis.  
Consistent with the extremely strong FDC that we earlier observed for reversal mutation for the TSP, we find that reversal is clearly dominant on the 100-city TSP instances within both the EA (Figure 2a) and SA (Figure 2b), finding lower-cost solutions with fewer evaluations. All comparisons to reversal mutation are extremely statistically significant (e.g., Wilcoxon rank sum test \(p\)-values very near 0) except for the shortest 100 evaluation runs. For SA, the second-best mutation operator is insertion (results also statistically significant). We earlier saw that insertion had the second strongest FDC for the TSP. Swap and the various cases of cycle mutation all find solutions of approximately equivalent cost within the SA, especially for very long run lengths.  
The EA comparison (Figure 2a) is a bit more interesting. Although for mid-length runs, insertion is second-best at statistically significant levels, the various cases of cycle mutation surpass insertion mutation for the longest runs. Insertion mutation exhibits a premature convergence effect, converging to a significantly suboptimal solution \(10^{5}\) evaluations into the EA runs, as does swap. However, cycle mutation continues to find lower-cost solutions, overtaking insertion mutation. The convergence effect in the EA is due to the smaller neighborhoods of swap and insertion, leading to greater impact of local optima. Cycle mutation has a larger neighborhood so better avoids this, continuing to show progress. In fact, even though we saw near-zero FDC for \(\text{Cycle}(\alpha)\), all cases of \(\text{Cycle}(\alpha)\) continue to make progress, although converging at a slower rate than reversal.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 4 Results and Evaluations
### The Pulse Width Modulation Charge Controller Efficiency Loss from Charging  
The efficiency of the PWM charge controller is critical to determining the total amount of power the solar module will generate. The PWM charge controller cannot reach the maximum power point, because it can operate linearly by controlling the voltage of the solar panel [19]. The efficiency loss can be calculated using the maximum power point provided by the manufacturer, which indicates that the voltage at the maximum power point is 18 V, and the nominal voltage of the three-cell lithium-ion battery is 11.1 V.  
Therefore, the nominal efficiency loss of the PWM charge controller was estimated by  
(The voltage at maximum power point - nominal voltage/nominal voltage) \(\times\) 100% = 62%
## 2 Results and Discussion
### Optimization of MSPE Conditions
#### 2.3.2 Adsorption Time  
To monitor the adsorption kinetics of the MMIPs, the effect of adsorption time was investigated by varying the shaking time (0-150 min), as shown in the kinetic curves in Figure 7b. The adsorption capacities of \(S\)-(\(-\))-OFL and \(R\)-(+)-OFL increased with increasing adsorption time and reached adsorption equilibrium at 30 min; thus, 30 min was chosen as the optimal extraction time. This fast adsorption could be due to a large number of active sites on the surface imprinting cavities of the MMIPs, which results in faster diffusion from the solution to the active sites. A test of the q values of \(S\)-(\(-\))-OFL and \(R\)-(+)-OFL indicated that there is no significant difference between the adsorption of \(S\)-(\(-\))-OFL and \(R\)-(+)-OFL on MMIPs using _rac_-OFL as the template with increasing time (\(p>0.05\)). The adsorption kinetic data obtained from batch experiments were analyzed using pseudo-first-order and pseudo-second-order equations. The equations and the calculated k and q values are listed in Table 2. The results indicated that the pseudo-second-order model better described the adsorption  
Figure 7: Optimisation of extraction conditions: Effect of pH on OFL adsorption onto MMIPs (adsorption time of 30 min) (**a**), effect of adsorption time on OFL adsorption onto MMIPs (pH 5) (**b**), effect of desorption time on the recovery of OFL (**c**), effect of elute solvent on the recovery of OFL (**d**).  
process for OFL onto the MMIPs. The calculated equilibrium adsorption capacities (q\({}_{e,c}\)) of \(S\)-(\(-\))-OFL and \(R\)-(+)-OFL from the pseudo-second-order model are closer to the experimental q\({}_{e}\) values.