esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Representing Data Provenance in DBMS
### Field and Value Level Provenance Representation  
Value provenance transformation (VPET) derives from the provenance transformation of tuples. Using a similar way of representing schema provenance graph and tuple provenance route, we utilize the field provenance graph (PGraph of Field, FPGraph) and value provenance route (VPRoute) to represent field- and value-level provenance. For \(VPet(Q_{i},I,t[A])\):  
**Definition 10**.: _(1) PGraph of Value (VPGraph), value provenance graph \(VPGraph(Q_{i},I,t[A])=<Vv_{r},Vv_{o},Ev>\) (2) PGraph of Field (FPGraph), provenance graph for field \(\mathbf{A}\), \(FPGRaph(I,\mathbf{A})=<V_{r},V_{o},E>\), where, \(V_{r}=\cup Vv_{r},E=\cup Ev,\forall t\in VPGraph(Q_{i},I,t[A])=<Vv_{r},Vv_{o},Ev>\). (3) PRoute of Value (VPRoute), value provenance route \(VPRout(Q_{i},I,t[\mathbf{A}])=<FPGraph(I,\mathbf{A})\), \(BP>\),\(BP=\{||p_{rid},rid=1,\dots,MAX_{RD}\}\), \(b_{rid}=0\ if\ V_{rid}\
otin TupleGraph(Q_{i},I,t[A]),b_{rid}=1\ if\ V_{rid}\in Tuple Graph(Q_{i},I,t[A])\)._  
Looking back to Example 2 in Figure 3, the VPET representation for each record in field \(\mathit{Info}\) is \(\mathit{OProj}(\mathit{Shipment.Jn})\). It is easy to represent the provenance graph for field \(\mathit{Info}\) (Figure 8a). For field \(\mathit{sm}\), the provenance transformation representations corresponding to each tuple are \(\mathit{OAggr}(\mathit{OJoin}(\mathit{OJoin}(\mathit{Shipment.quantity}, \mathit{OProj}(\mathit{Shipment.quantity})),\mathit{Parts.price}))\) and \(\mathit{OAggr}(\mathit{OJoin}(\mathit{Shipment.quantity},\ \mathit{Parts.price}))\), as shown in Figure 8b.
# Trigonometric Inference Providing Learning in Deep Neural Networks
## 3 Methodology
### A Toy Example  
To better illustrate how the learning algorithm works, we let a simple model fit a straight line. Consider a model of only two neurons, each forming a layer, and we have a model that outputs \(W_{2}(W_{1}(x))\) in which the non-linearity is omitted. Using the trigonometric training method, we trained this model with different choices of parameterinitials, input ranges, and target line slopes. If the target line is denoted as \(y=0.3x\), the goal is to train the model that will be stable at \(\sin(w_{1})\sin(w_{2})=0.3\). Figure 4 summarizes the fitting results and learning dynamics of the proposed algorithm under various conditions. The parameters for the different conditions are summarized in Table 2. In this case, the learning rate is chosen as \(2^{-2}\) for avoiding multipliers in the gradient descent stage (note \(2^{-2}\) is not required, other approximate values can work as well), and the learning dynamics subplots are drawn from the first 100 iterations. In addition, the training sample size is 300, and the final fitting curves are the results of 100 epochs of training.  
It is clear that the gradient descent functions when Equation (17) is satisfied. When the target slope is larger than 1 (see subfigure (c,g)) or sample data are drawn with large variance (subfigure (d,h)), the proposed algorithm struggles to reach the target; however, samples around 0 are still well approximated (subfigure (d)). Fortunately, in modern deep neural networks, these situations are not common and can be avoided. In addition, even if \(w_{2}\) was set to 500 at the beginning, the algorithm still provided learning. Because trigonometric functions are periodic, a weight that is too large will not affect the convergence of the network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Initials** & **a** & **b** & **c** & **d** \\ \hline \(w_{1}\) & 0.5 & 0.43 & \(-0.78\) & 0 \\ \(w_{2}\) & 0.1 & 500 & \(-0.07\) & 1.83 \\ \(\mathcal{N}_{x}\) & (0.03, 0.3) & (0.3, 0.3) & (0.03, 0.3) & (0.03, 2) \\ \(m\) & 0.07 & \(-0.61\) & 1.73 & \(-0.54\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial parameters of the toy example. Note that m stands for the target slope, and \(\mathcal{N}_{x}\) denotes the mean and variance of the samples. All samples were normally distributed.  
Figure 4: Line fitting and learning dynamics of trigonometric inference under various conditions. The initial parameters for (**a**–**d**) are listed in Table 2. (**a**,**e**) represent an ideal condition in which samples are concentrated around 0. (**b**,**f**) represents a situation in which samples are drawn from comparatively large means. (**c**,**g**) depict the learning dynamics when the slope is larger than 1, which is impossible for sine activation to reach. (**d**,**h**) designate a large sample variance.
## 3 Results and Discussion
### Biological Properties
#### 3.3.3 Impact on Adipolysis and Adipokines Secretion  
Lipolysis is one of the most important metabolic processes occurring in the adipose tissue, and it is related to the decomposition of triglycerides [35]. The results obtained (Figure 4) showed that 3T3-L1 differentiated adipocytes treated with pre-digested yogurts showed a slight increase in adipolysis when compared with isoproterenol (the assay control); however, no significant differences (\(p>0.05\)) were verified (Figure 4A). The glycerol release values were similar to those reported in the previous works [17; 18].  
Despite the non-significant differences, the CBY samples increased the adipolysis 1.1 times more than ABY samples. Concerning the adipokines secretion (Figure 4B), the presence of pre-digested yogurt samples led to a significant (\(p<0.05\)) increase in adiponectin secretion when compared with the basal activity. The presence of ABY samples increased by ca 62% the adiponectin secretion compared with the basal activity and by ca 53% when compared with CT yogurt. There were also significant (\(p<0.05\)) increases in the presence of coconut oil yogurts. In this case, coconut yogurts promoted a ca 47% additional adiponectin secretion when compared with basal activity and 35% more when compared with the CT yogurt. A previous study reported that oleic acid (the major constituent of avocado oil) is responsible for an increase in the adiponectin secretion in 3T3-L1 cells [36]. In relation to coconut oil, an in vitro study showed that lauric acid (the main fatty acid in CO) did not affect adiponectin secretion in 3T3-L1 cells [37]. Regarding leptin secretion (Figure 4C), all yogurts promoted a significant (\(p<0.05\)) reduction in its secretion. Comparing the fortified yogurts and control, no significant differences (\(p>0.05\)) were observed. Previous studies showed that medium-chain fatty acids present in coconut oil can down-regulate leptin  
Figure 3: Hepatic lipid accumulation results for the different yogurts (**A**) without induced steatosis; (**B**) steatosis induced. Different letters mean significant differences between yogurts as determined by a one-way ANOVA test (\(p<0.05\)). CT—control yogurt, COX—coconut oil yogurt; CBY—coconut bigel yogurt; AOY—avocado oil yogurt; ABY—avocado bigel yogurt.  
section in rats [38; 39], while for avocado oil yogurts, the effect can be related to the high amount of oleic acid which is responsible for the high reductions in leptin secretion in 3T3-L1 cells [40].  
Figure 4: Effect of yogurts on adipolysis and adipokines secretion. (**A**) adipolysis; (**B**) adiponectin; (**C**) leptin. Different letters mean significant differences as determined by a one-way ANOVA test (\(p<0.05\)) between samples. CT—control yogurt, COY—coconut oil yogurt; CBY—coconut bigel yogurt; AOY—avocado oil yogurt; ABY—avocado bigel yogurt.
## 4 Discussion
[PERSON] et al. [9] observed that ale beers were characterized by ethyl octanoate and ethyl decanoate, becoming markers to differentiate it from white bread wasted beers, which were characterized instead by the presence of phenyl ethyl alcohol, in accordance with the present results. Terpenes are usually related to the addition of hops, resulting in a flowery odor in the aroma profile [47], and their profile is in line with our findings. Geraniol and citronello are known to be influenced by yeast metabolism during fermentation, and sesquiterpenols, esters, and alcohols are only suspected to be bioconverted by yeasts [50]. These results follow the means of the banana and floral aroma intensities in the descriptive analysis. These represent markers aromas of wheat-based beers [51], and, although there were no statistical differences with the control beer, higher average scores were given to beers with wheat flakes only. In industrial production, to maintain the quality and style of beers with wasted bread, they can be marked by the presence of acetaldehyde, which contributes to green/apple notes [27]; this compound is normally one of the most abundant components in the crumb of wheat bread [10; 52]. Food waste results in the loss of not only the product itself but also all the resources used in the supply chain [5; 53]. The use of wasted bread has proven to be an efficient substitute for wheat flakes. [PERSON] et al. [53] investigated the environmental impacts of waste management and ways of valuing surplus bread in Sweden; they found that source reduction has the greatest environmental savings, followed by donation, ethanol production, animal feed, and beer production. These recovery strategies are favored over anaerobic digestion and incineration, which offer the smallest environmental savings. For instance, in beer production, the observed savings of \(-0.46\) kg CO\({}_{2}\)eq in the global warming category are the result of replacing malted barley with surplus bread [53]. [PERSON] et al. [54] compared the production of standard craft beer with a process that uses surplus bread and found that the latter could reduce environmental impacts by 20%, due to the lower need for barley and the use of grain used as animal feed. As a limitation, this study included only one type of bread in the beer formulation. Variations in bread ingredients and production methods could influence the results. Additionally, the bread was dried before use, and the utilization of the bakery product without previous treatment can be an alternative, which could save energy and time but might also yield different outcomes. Future studies should include toxicological analyses to assess the presence of mycotoxins in the malt, wheat flakes, and bread utilized. Moreover, monitoring changes in _S. cerevisiae_ metabolomics and spoilage microorganisms, such as lactic acid bacteria, during the brewing process is crucial in ensuring the quality of the beverage. Further research is needed to thoroughly evaluate the industrial-scale use of wasted bread in beer production.
# Crawling Magnetic Robot to Perform a Biopsy in Tubular Environments by Controlling a Magnetic Field
## 5 Experiment and Verification  
### Verification of Puncturing Force  
The proposed CMR should generate a puncturing force sufficient to insert a needle into the tissue to collect sample tissue. According to [PERSON] et al. [24], a minimum force of 0.3 N is required to puncture the liver tissue of pigs. Figure 9a shows an experimental setup to measure the propulsive force of the CMR. First, the pseudo-glass tube containing the CMR was fixed in a water container, and a jig connected to the load cell (Honeywell, Morris Plains, NJ, USA) was placed in front of the pseudo-glass tube. The experimental setup was then placed inside the MNS. Once the external precessional magnetic field generated from the MNS was applied to the CMR for 5 s, the propulsive force of the crawling CMR was measured by the load cell. Figure 9b shows the maximum magnitude of the propulsive force according to the rotational frequency of the external precessional magnetic field when the magnitude of the external precessional magnetic field is 12 and 14 mT, respectively. When the magnitude of the external precessional magnetic field was 12 mT, the puncturing force increased with the increase in the rotational frequency. However, the puncturing force did not increase by more than 280 mN even if the rotational frequency increased beyond 25 Hz. When the magnitude of the external precessional magnetic field was 14 mT, the puncturing force also increased with the increase in the rotational frequency. However, when the rotational frequency of the external magnetic field was over 30 Hz, the puncturing force decreased because of the step-out phenomenon, in which the crawling body cannot follow the rotational frequency of the external precessional magnetic field. As shown in Figure 9b, the puncturing force was 0.46 N when the magnitude and the rotational frequency of the external precessional magnetic field were 14 mT and 25 Hz. We confirmed that the proposed CMR can generate a puncturing force sufficient for the biopsy needle to be inserted into the liver tissue of pigs.  
Figure 8: (**a**) Components of the CMR. (**b**) Assembled CMR.
## 2 Materials and Methods
### Theoretical Backgrou
#### 2.1.9 Content-Based Filtering  
Content-Based Filtering recommends items similar to those a user has previously engaged with by analyzing the attributes of the content, creating a user profile, and reflecting the user's past choices, preferences, and feedback. Based on this reflected profile, it recommends content that aligns with the user's tastes. The advantage of a content-based recommendation system is that it reflects clear preferences and does not require data from other users. It allows for personalized recommendations that consider an individual's unique preferences and can explain the rationale behind each recommendation to the user. A limitation, however, is that it cannot recommend new types of content that the user has not previously experienced. Such systems are widely applied in various fields including online shopping, streaming services, and social media platforms.
## 3 Optimal Power Allocation (PA), Cooperative Relaying and Channel Models in PD-NOMA
### Fuzzy Inference System for Optimal PA
#### 3.1.1 Selection of Fuzzy Sets  
At this stage, the fuzzy inputs and their respective membership functions (MFs) are chosen to unfold the complete range of inputs and outputs. The FIS system takes different input signals at the gNodeB, such as \(d\), \(SNR\), and \(d_{f}\) for each serving user in its vicinity. Each input has three triangular MFs, which are labeled as low (L), moderate (M), and high (H). In the fuzzification stage, all three input variables are transformed into fuzzy values: \(\mu_{d}\), \(\mu_{SNR}\) and \(\mu_{d_{f}}\). Our proposed FIS system is implemented for \(d\) = 200 m, \(SNR\) = 30 dB, and \(d_{f}\) = 30 m. However, if the universe of discourse changes, the same MF should be utilized  
Figure 2: Block diagram of the proposed cooperative relaying FIS based PD-NOMA system.  
to cover the entire range of input parameters. The output function is also triangular and consists of five MFs: very low (VL), low (L), moderate (M), high (H), and very high (VH). All the three inputs and output MFs are shown in Figures 3-6, respectively. The triangular MFs are produced with min-max (and-or) operations [42] for implication/aggregation. With a high point \((\alpha,h)\) and end points \((e,0)\) and \((f,0)\), the triangular membership function \(T(s)\) is given by:  
\[T(s)=\begin{cases}h(\frac{\delta-\varepsilon}{\alpha-\varepsilon})&\text{ for } \quad e\leq s\leq\alpha\\
## 2 Related Work  
Navigation tasks have seen significant advancements over the past decades. Simultaneous Localization and Mapping (SLAM) has successfully built maps and localized robots in dynamic environments. Traditional path-planning approaches, such as the A* algorithm, [PERSON]'s algorithm, and potential field methods, are commonly used to compute collision-free paths [11]. Control algorithms enable robots to make decisions by managing the velocity of the wheels to adapt to varying terrains [12]. In addition, numerous sensor-based advances have improved robot perception, further improving autonomy [13; 14].
# Interference of Spread-Spectrum EMI and Digital Data Links under Narrowband Resonant Coupling
## 2 Communication Channel Modeling under Resonant EMI Coupling
### Modelling of a Communication Channel Affected by EMI  
In the above model, it is now assumed that the communication channel is also corrupted by EMI generated by an aggressor whose operation is based on periodic or randomly SS-modulated switching signals.  
In case of periodic signals, the EMI spectrum has nonzero components only at the harmonics \(kf_{0}\), with \(k\in\mathds{N}\), of the switching frequency \(f_{0}\) of the aggressor. By contrast, when SS techniques are applied, the instantaneous frequency \(f\) of the aggressor varies according to the law  
\[f(t)=f_{0}+\delta f_{0}\xi(t) \tag{2}\]  
where \(f_{0}\) is the central frequency, \(\delta\) is the modulation depth and \(0\leq\xi(t)\leq 1\) is the modulation profile. As a consequence, the EMI spectral power is more or less uniformly spread over the bandwidth \([kf_{0},kf_{0}(1+\delta)]\) and the spectral peaks are consequently reduced. By using SS-modulations with a random modulation profile \(\xi(t)\) optimized for this purpose, the EMI power can be effectively spread over the whole spreading bandwidth \([kf_{0},kf_{0}(1+\delta)]\) in a nearly uniform way, leading to a reduction of the EMI p.s.d. to \(P_{k}/kgf_{0}\) which makes it easier to comply with EMC regulations.  
Under the above hypotheses, EMI around an harmonic \(kf_{0}\) within the bandwidth \(B\) of the communication channel in Figure 2 is modelled by a wide-sense stationary (WSS) narrowband Gaussian process, independent both of the background noise and of the transmitted signal, with total power \(P_{\text{EMI}}\) and bandwidth \(W\), completely or partially overlapping the signal bandwidth \(B\).
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 2 Materials and Methods
### Dynamic Time Warping
#### 2.3.2 Time-Constrained DTW  
Computing the entire DTW matrix can be a time-intensive procedure, especially when data rich sequences are compared. Furthermore, comparing an element of a sequence with all other elements of another sequence can lead to erroneous results, especially for crop mapping (e.g., aligning a winter crop with a summer crop). Therefore, applying global constraints on time warping have proven to speed up the processing, while providing meaningful results [65]. Two of the most-known global constraints are the Sakoe-Chiba band [23] and the Itakura parallelogram [66], which limits the warping path to a certain band around the diagonal of the matrix or allowing more distortions in the middle of the matrix, respectively.  
For SITS data analysis, these global constraints might not be suitable mainly because the satellite images are irregularly sampled and consequently the defined constraint might lack a temporal meaning [11]. A more appropriate approach would be to limit the computation of the DTW matrix to a predefined maximum time delay, \(w\). An element of the matrix will be computed only if the date difference (\(date_{diff}\)), which is a function returning the difference in time between the two dates of the compared elements of sequences, is smaller or equal to \(w\). In computation of the DTW matrix, this condition must be evaluated beforehand. If it is true, the procedure remains unchanged, otherwise the element of the matrix is set to \(+\infty\)[11]. The difference between a time-constrained and a time-weighted DTW relates to the fact that while for time-constrained DTW all elements found within \(w\) time delay are considered in computation, for time-weighted DTW a temporal cost is added using a linear or a logistic model [18].  
Two special cases may arise when choosing the time-constraint on the warping path. First, when \(w\) = 0 the computation is limited to the diagonal elements, resembling the Euclidean distance. Second, when \(w\) is higher than the temporal length of the longest sequence the entire DTW matrix is computed. Therefore, the value of \(w\) needs to be adjusted to the characteristics of SITS analyzed to make sure that a warping path exists [11]. The procedure of computing a time-constrained DTW is described in Algorithm 1. Examples of two DTW matrices for TA1 and TA2 with 45-day time-constraints are depicted in Figure 7.  
We compared different single-band and multi-band DTW classifications after applying time constraints of 15, 30, 45, and 60 days, respectively (for the remainder of the article, the terms DTW15, DTW30, DTW45, and DTW60 will be used). These values were selected to consider the diversity of crop cycles lengths, in accordance with the temporal sampling of the SITS used. Values higher than 60 days would already allow the alignment of winter crops with summer crops. Furthermore, we compared these results with those obtained by computing the entire DTW matrix (DTWFull) and computing the Euclidean distance (\(w\) = 0) (DTW0).
# An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking
## 4 Multicast Mechanism
### Leaving a Group  
The process of the receiver joining the multicast group is shown in Figure 5. When a receiver is no longer interested in the multicast group identified by MGEID, it informs the RER it connected to leave the group (arrow 1). The RER removes the interface connected to the receiver from the out-interface list of the NFT entry corresponding to the MGEID. If the RER finds that its GMT does not contain the MGEID and the out-interface list of the NFT entry is empty (arrow 2), it deregisters the mapping between the MGEID and its NA with NRS (arrow 3), sends a LEAVE message containing its RID to the Parent NA of the NFT entry (arrow 4), and removes the NFT entry.  
When a router receives the LEAVE message sent to it, it first checks whether its GMT contains the mapping between the MGEID and the RID carried in the message. If so, it removes the mapping between the MGEID and the RID from its GMT. Otherwise, it  
Figure 4: The example of the MJN selection process in AHHM.  
Figure 5: The message sequence chart diagram of joining a group.  
removes the incoming interface of the message from the out-interface list of the NFT entry corresponding to the MGEID. Then, if its GMT does not contain the MGEID and the out-interface list of the NFT entry becomes empty (arrow 5), the router deregisters the mapping between the MGEID and its NA with NRS (arrow 6), sends a LEAVE message containing its RID to the Parent NA of the NFT entry (arrow 7), and removes the NFT entry.  
Figure 6 details the leaving process of the proposed AHMM approach. As shown in Figure 6a, when [PERSON] is no longer interested in the multicast group specified by MGEID\({}_{1}\), it just informs [PERSON] that it wants to leave the group. Then, [PERSON] sends a LEAVE message to [PERSON] and removes the NFT entry of MGEID\({}_{1}\). After receiving the LEAVE message, [PERSON] removes the mapping between MGEID\({}_{1}\) and RID\({}_{N6}\) from its GMT, sends a LEAVE message to [PERSON], deregisters the mapping between MGEID\({}_{1}\) and N\({}_{N2}\) with NRS, and removes the NFT entry. When N1 receives the LEAVE message sent from N2, it removes the incoming interface (2) of the message from the out-interface list of the NFT entry.  
Then, R2 informs N7 that it wants to leave the multicast group, as shown in Figure 6b. N7 removes the incoming interface (2) of the message from the out-interface list of the NFT entry corresponding to MGEID\({}_{1}\). Then, R3 also informs N7 that it wants to leave the multicast group specified by MGEID\({}_{1}\), as shown in Figure 6c. N7 sends a LEAVE message to N1 and removes the NFT entry of MGEID\({}_{1}\). After receiving the LEAVE message sent from N7, N1 removes the mapping between MGEID\({}_{1}\) and RID\({}_{N7}\) from its GMT.  
Figure 6: The example of the leave process in AHHM. (**a**) R1 leaves the multicast group; (**b**) R2 leaves the multicast group; (**c**) R3 leaves the multicast group.