esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Nevertheless, even in these situations, SLTs exchanged some information or instructions with the parents when they pickup up their children from the location where the speech and language therapy session had just taken place. Still, 14 SLTs answered that the family participated during the entire session, while 13 answered that the family participated at the end (when picking up the children) and 1 SLT reported participation at the beginning (when dropping off the child). Speech and language therapists considered that lack of time of family members, due to everyday or work occurrences, was a negative and detrimental factor, since it could mean limited family involvement. The questions regarding the continuation of the intervention session at-home were particularly important to the goals of our research. The section started with a question supported by numerous references in literature, i.e, sending activities to be done at-home [11, 17], receiving 24 positive and 4 negative answers. Interestingly, even the negative answers do not mean that no activity was promoted. These four SLTs preferred to contact the family directly and instruct them on how to change or adapt everyday routines into post-session activities, instead of using worksheets. This question had a follow-up regarding the types of activities sent, where games was the most common type of activity. This is quite understandable taking into consideration the typical age groups. When questioned regarding objectivity and how the SLTs could check if the child had done the activity, most SLTs seem to rely on feedback and observation, and infer if the work was done. While this is a viable solution, it has some shortcomings. Feedback depends on the details provided and the reliability of the person providing it. Parents knowing what is socially expected of them might be tempted to be less realistic regarding activity execution, time spent on it and end results. Speech and language therapists were also questioned about what tools they needed to check if the activities were done correctly. Most SLTs (16 in total) preferred simply to infer or ask the parents if the activities were done. Eight SLTs would use other means to check if the activities were being done, either by role-playing or assessing the new skills gained. The remaining questions in this section were related with the perceived importance of at-home activities and difficulties perceived by the SLTs. Some answers, revealed parents' lack of understanding of what was expected of them, blaming the child for \"underperformance\" and SLTs for inadequate and delayed feedback. These are questions stressed out by the FG requirements and the literature [101], that supported the use of features for real time reporting and possible activity re-set and feedback. The final section of the questionnaire further explored the concept of a new tool. It was, above all, a mechanism to gather, assess, and rank a set of ideal characteristics.
## 3 Results and Discussion
### Sensory Evaluation of Beers  
The last stage of the research was related to the sensory analysis of the obtained beers. The characteristics of the aroma were studied using the QDA method. On a 9-point scale, the intensity of aromas was determined and grouped into nine categories (fruity, floral, roasted, herbal, woody, vegetable, earthy, animal and chemical), the same as those used to interpret the results of the olfactometric analysis.  
Compared to hopped unfermented wort, the intensity of fruit and floral aromas (associated with fermentation products) increased significantly, and the intensity of roasted aromas decreased [Figure 4]. Beers obtained with the use of yeasts _W. anomalus_ and _S. kudriwzevii_ were characterized by the highest intensity of fruit and floral aromas. Among the detected notes, notes of red apple and strawberries dominated. The greatest contributors to strawberry fruit aroma are ethyl butanoate, ethyl hexanoate, methyl butanoate and methyl hexanoate among esters; 2,5-dimethyl-4-hydroxy-3(2H)-furanone (DMHF) and 4-methoxy-2,5-dimethyl-3(2H)-furanone (DMMF) among furanones; laniod and nerolidol among terpen; and methanethiol among sulfur compounds [55]. The first two compounds were characterized by a high intensity of aroma in the discussed samples (Table 3). Low-alcohol beer was characterized by the lowest intensity of the discussed notes, which confirms the large impact of the fermentation process on this type of aroma. Roasted notes remained relatively intense in beers obtained with _Ha. uvarum_ and _S. kudriwzevii_ yeast. The aroma of beer obtained with the participation of _Ha. uvarum_ yeast was characterized as porter-like, with notes of honey.  
Figure 4: Sensory analysis (QDA) of produced beers with non-conventional yeasts. (**a**)—_Saccharomyces yeast and Torulaspora delbrueckii;_ (**b**)—other non-_Saccharomyces yeast_.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 2 Background
### What Value Does 5G Bring to Fog Computing?  
Fifth-generation technology promises to elevate the cellular network and not only to interconnect people, but also to interconnect and control machines, things, and devices. The main characteristics of 5G networks are high speed data transfer, zero latency, and ubiquitous connectivity, which should support a wide range of high-performance applications and services [17]. Fifth-generation technology will provide new levels of performance and efficiency that will allow new user experiences and new industries to connect. Moreover, 5G will offer multi-Gbps data rates, ultra-low latency, huge capacity, and a more consistent user experience. For instance, [PERSON] et al. [18] used fog computing to support the 5G-enabled Internet of Vehicles (IoV) and thus provide accurate location service and address the problems associated with the failure of the Global Navigation Satellite System (GNSS). They proposed a topology that relies on the GNSS-free emergency location service in the 5G-enabled IoV, which contains fog clusters and fog nodes to collect traffic data and process requests rapidly. The combination of fog computing and 5G-enabled IoV helped to achieve high precision in estimating the vehicles' location. The significant value that 5G brings to fog computing is the large number of simultaneous devices supported by 5G cells compared to 4G. The utilization of 5G in smart city scenarios means that individual components of the smart city can be connected autonomously, sharing data, aggregating it, and exploiting it in real time.
## 2 Results and Discussion
### Optimization of QuEChERS
#### 2.3.2 Selection of Extraction Solvent  
The main factor used to choose the extraction solvent was its miscibility with analytes. Traditional organic solvents such as acetonitrile, ethanol, and methanol, were tested using 1 g of NaCl and 4 g of MgSO\({}_{4}\). As the phase separation in the salting-out step was impossible with methanol and ethanol, acetonitrile was the chosen alternative. Different acetonitrile volumes (2.5, 5, and 10 mL) were tested to study its influence on the ERs% of the analytes. Figure 2a shows that the ERs% of each _Alternaria_ toxin increased gradually as the volume increased. However, as larger volumes might have induced the enrichment factor, we did not investigate larger volumes. Thus, 10 mL acetonitrile was adopted in the following study. Acidification of acetonitrile with 1% acetic acid was also tested. As shown in Figure 2b, the acid improved the ERs% for all _Alternaria_ toxins, especially for TeA.  
Figure 1: Effect of the ratio of sample/water (_m/v_) on the extraction recoveries (ERs%) of four _Alternaria_ toxins: tenuazonic acid (TeA), alternariol (AOH), alternariol monomethyl ether (AME), and tentoxin (TEN).  
Figure 2: Effect of (**a**) the volume of MeCN and (**b**) percent of acetic acid on the ERs% of four _Alternaria_ toxins: TeA, AOH, AME and TEN.
## 3 Simulation Results and Discussion
### Amplitude of Random Perturbation  
The amplitude of random perturbation \(\delta\)u is one of the important parameters for the RMS-Prop algorithm. As \(\delta\)u increases, the convergence speed of the algorithm can be significantly improved. However, when it exceeds a threshold, the convergence will deteriorate, which is not only reflected as instability, but the inability to reach a high convergence value as well.  
Figure 3a,b show the convergence curves of the RMS-Prop algorithm with different \(\delta\)u. The cost function is J\({}_{3}\) and \(\eta\) = 0.4. When \(\delta\)u is 0.02 or 0.04, it is too small for the algorithm to converge to 0.99 within 300 iterations. When \(\delta\)u is 0.06, the algorithm converges to a high value at a relatively fast speed. When \(\delta\)u exceeds 0.06, although the algorithm converges extremely fast in the first few steps, the convergence value begins to deteriorate within 300 iterations, which is explicit in the PER curves. Thus, 0.06 is the optimal value for the amplitude of random perturbation, and the algorithm has both a fast convergence speed and high accuracy with \(\delta\)u = 0.06.
## 2 Methodology
### Nonlinear FCE  
When using a linear FCE method, simply increasing the weight of an index may not adequately reflect its true influence on the overall evaluation result. For example, when a certain index value of the evaluation object is very high while other index values are relatively low, in the actual situation, it can be considered excellent or poor. However, after applying the weighted average method, due to the insufficient influence of the weight, the prominent influence of this index cannot be reflected, which may lead to the overall evaluation result being inconsistent with the actual situation [22; 23]. By contrast, a nonlinear FCE approach can address this limitation. It treats the linear weighted operator as a special case within a broader nonlinear framework. The nonlinear fuzzy matrix synthesis operator is presented in Equation (12):  
\[f(\alpha_{1},\alpha_{2},\cdots,\alpha_{n};s_{1},s_{2},\cdots,s_{n};\mathbf{\Gamma})= (\alpha_{1}s_{1}^{\lambda_{1}}+\alpha_{2}s_{2}^{\lambda_{2}}+\cdots+\alpha_{n}s _{n}^{\lambda_{n}})^{\frac{1}{4}}, \tag{12}\]  
where \(\alpha_{i}\) is the weight of the \(i\)-th evaluation index; \(s_{i}\) is the value of the \(i\)-th column in the fuzzy evaluation matrix; \(\lambda_{i}\) is the prominent influence coefficient, which reflects the significance degree of the index on the result; and \(\lambda_{i}\geq 1\). The larger the value of \(\lambda_{i}\), the greater the prominent influence degree of the evaluation index on the result. \(\mathbf{\Gamma}=[\lambda_{1},\lambda_{2},\ldots,\lambda_{n}]\) is the prominent influence coefficient vector of the evaluation index, \(\lambda=\max(\lambda_{1},\lambda_{2},\ldots,\lambda_{n})\), and Table 1 presents the value standard of the prominent influence coefficient [23].
## 2 Materials and Methods
### Sitting Down and Standing Up Experiment  
#### 2.2.1 Motion Analysis  
During the motion analysis of sitting down and standing up, IMUs were used to analyze the motion for the sake of clinical practice in the future. IMUs will not disturb a person's motion or restrict the measurement environment because they are small and lightweight devices, suitable for use in clinical practice. As shown in Figures 1 and 2, the participant was evaluated using six IMUs incorporating tri-axis accelerometers, gyroscopes, and magnetometers (TSND151, ATR-Promotions Co., Ltd., Kyoto, Japan). The six IMUs were attached to a fixed belt at the level of the C7 spinous process, L4 spinous process, bilateral position 10 cm above the patella, and bilateral position 10 cm above the lateral malleolus, to measure the motion of upper trunk, lower trunk (pelvis), thigh segment, and bilateral shank, respectively. Some motion, such as vibration and the rotation of each segment could be analyzed by acquiring data from the attached IMUs. The signals were sampled at a frequency of 100 Hz. Meanwhile, Nintendo's Wii Balance Board (WBB; Nintendo, Kyoto, Japan) was used to measure vertical reaction forces. The measurement accuracy of the WBB is \(\pm\) 7.84 N (0.8 kg) from 0 to 68 kg and \(\pm\) 11.76 N (1.2 kg) from 68 to 100 kg. Two WBBs were placed under the participant's feet and on the chair to measure the vertical ground reaction force and the vertical reaction force from the chair, respectively.  
Figure 3 shows an overview of the measurement settings. The participant started from a standing position in front of the chair and then sat down and stood up. Sitting down and standing up were conducted eight times. A rest period of approximately 5 s was arranged between each time. An armless, backless chair was adjusted to the height of the subject's knee (determined as the distance from the lateral knee joint line to the floor). The sit-to-stand (STS) movement was performed barefoot, and the distance between each foot was 10 cm, in accordance with previous research [22]. The participation moved at a self-selected velocity, comfortably, and performed the STS movement eight times without using her hands.
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 4 Protocol Evaluation
### Evaluation Setup
If no near anchor node satisfying this property is found, it forms a new larger ring and repeats the process until at least one near anchor node is found satisfying this property. In the present implementation, an initial ring radius equal to 10 has been chosen and if no node is found that satisfies the aforementioned property, the radius is doubled at each step. This variant is hereafter denoted as **FRESH-near-v1** or **Modified-near-v1** depending on whether the original FRESH protocol is used or the one modified by introducing the AoI metric. 3. **Relay node selection based on \"first near anchor\" node**: this variant is exactly the same as the above with the difference that here the source node only needs to find one near the anchor node that has an encounter time with the destination more recent than its corresponding time with the destination. On the contrary, in the above variant, when a near anchor node is found that satisfies this property, all the near anchor nodes of the source (with the specific ring radius) are searched and among them the \"optimal\" one is selected, i.e., the nearest anchor node that has the most recent encounter time with the destination compared to all others near anchors. This variant is hereafter denoted as **FRESH-near-v2** or **Modified-near-v2**, depending on whether the original FRESH protocol is used or the one modified by introducing the AoI metric.
## 3 Estimation of PCV Corrections  
### Mathematical Models  
Modeling GNSS observation requires the computation of the geometric distance between the antenna phase center location of the GNSS satellite at signal emission time and the antenna phase center location of the receiving antenna at signal reception time. The phase center location usually differs from the mechanical antenna reference point (ARP). The difference vector is conventionally described by a set of phase center corrections. Such  
\begin{table}
\begin{tabular}{c  
a set of corrections consists of a PCO vector, which defines the position of the mean antenna phase center, with respect to the ARP and a consistent function, which models the azimuth and zenith dependent PCVs [36].  
Phase center corrections have some inherent degrees of freedom. One set of corrections consisting of a PCO vector \(r_{0}\) and an azimuth and zenith dependent function \(\phi(\alpha,z)\) can be transformed into a new set, consisting of \(r_{0}^{\prime}\) and \(\phi^{\prime}(\alpha,z)\), which gives the same result:  
\[\begin{array}{l}r_{0}^{\prime}=r_{0}+\Delta r\\ \phi^{\prime}(\alpha,z)=\phi(\alpha,z)-\Delta r\cdot e+\Delta\phi\end{array} \tag{1}\]  
where \(\alpha\) and \(z\) are azimuth and zenith angles, \(\Delta\phi\) is an arbitrary offset and cannot be separated from the receiver clock. The unit vector \(e\) denotes the direction from the receiver to the satellite. The offset vector \(\Delta r\) can be chosen arbitrarily. Preferably, PCVs should not induce a PCO and \(\Delta r\) should be zero. In that case, the mean antenna phase center is explicitly defined by the PCO. This convention is particularly important if one would only apply PCO and no PCV [14].  
Taking computational burden into consideration, residual approach is employed in this research. The antenna PCV is represented as piecewise linear function with respect to the zenith and azimuth angles in the antenna fixed coordinate system. The model assumes that PCV is composed of different grids, and zenith and azimuth angles are equally divided. When the observation is at the point of P within the grid ABCD, its PCV value can be linearly interpolated:  
\[\begin{array}{l}\Delta_{PCV,P}=(1-\gamma)(1-\beta)\Delta_{PCV,A}+\gamma(1- \beta)\Delta_{PCV,B}+\gamma\beta\Delta_{PCV,C}+(1-\gamma)\beta\Delta_{PCV,D}\\ \gamma=(\alpha-\alpha_{1})/(\alpha_{2}-\alpha_{1}),\beta=(z-z_{1})/(z_{2}-z_{ 1})\end{array} \tag{2}\]  
where \(\Delta_{PCV,P}\) is the PCV at point P and is observation, \(\Delta_{PCV,i}(i=A,B,C,D)\) are the PCVs at points A, B, C, D are parameters to be estimated. \(\gamma\) and \(\beta\) are combination coefficients, \(\alpha\) and \(z\) are azimuth and zenith angles of P, \(\alpha_{1}\) is the azimuth of point A and D, \(\alpha_{2}\) is the azimuth of B and C, \(z_{1}\) is the zenith of A and B, \(z_{2}\) is the zenith of C and D.
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 4 Proposed Strategy
### Slice Management through Resource Transfer (SMART)  
The VNF Manager (VNF-M) is the core of the SMART scheme since it is responsible for making the ultimate determination of the success of NSRs after thoroughly checking the available resources. VNF-M is responsible for four essential responsibilities related to NSRs and physical resources, such as dynamic provisioning, inner slicing, reporting, and priority provisioning as shown in Figure 4. In order to carry out the related duties, VNFs are defined individually.
## 3 Results and Discussion
### Biological Activity of AmPS
#### 3.2.3 Anti-Inflammatory Potential  
The anti-inflammatory potential of the crude polysaccharides from _A. mella_ was evaluated using in vitro assays investigating the inhibitory effect on proinflammatory enzymes including cyclooxygenase (COX, lipoxygenase (LOX) (Table 4), and hyaluron (HYAL). The AmPS fraction was found to suppress the COX-1 isoform activity in a concentration-dependent manner; however, no activity against the COX-2 isoform was detected in the tested concentrations of 0.5 and 1 mg/mL. Our previous study revealed that crude polysaccharides from the edible species _Cantharellus cibarius_ inhibited COX-1 and COX-2 activity by 43.86% and 51.59%, respectively [31]. The percentage of LOX inhibition for AmPS was 38.83%, which is rather moderate compared to that of acetylslicylic acid used as a standard (93.97%). However, the inhibitory activity of polysaccharides from a different edible mushroom from Poland--_Sparassis crispa_--was estimated at 23.8% during our previous investigation [10]. The activity of AmPS in relation to hyaluronidase was determined in this study as well. This enzyme is responsible for the degradation of hyaluronic acid. When uncontrolled, the process may promote the development and progression of an inflammatory condition [32]. The AmPS fraction was shown to inhibit HYAL with an IC\({}_{50}\) value of 590.81 \(\pm\) 1.83 ug/mL. The activity of sample was two times lower than that of the standard compound EGCG (IC\({}_{50}\) 266.21 \(\pm\) 1.18 ug/mL). Previous findings revealed that xylosyl galactotvan from _A. mella_ has significant anti-inflammatory properties via decreasing the level of tumor necrosis factor- and cytokine monocyte chemotactic protein-1 in RAW264.7 macrophages [26]. [PERSON] et al. [5] demonstrated that a polysaccharide from the fruiting body of honey mushroom stimulated murine lymphocyte proliferation caused by concanavalin A or lipopolysaccharide, which makes it a possible immunopotentiating agent. Similarly, our results support the assumption that polysaccharides from the honey mushroom may alleviate certain inflammatory diseases.
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 4 The Architecture and Complexity Analysis of the Proposed Decoder
### Proposed Decoder Architecture  
The overall architecture of the proposed soft decision decoder is shown in Figure 3. The decoder includes five modules. The soft information register is used to store soft information from the demodulator and perform cyclic shifting to obtain the different information sequences, that is, the information set. The following decoding operations are repeated according to the cardinality of the information set, and finally the candidate codeword with the minimum correlation discrepancy is selected as the decoding output. As described in Section 3, the candidate sequence generator, the re-encoding unit, the correlation discrepancy calculation unit, and the decoding result update unit work in the sequential manner.  
Figure 3: The overall architecture of the proposed soft decision decoder.  
The decoding process is explained as follows. The soft information sequence \(\mathbf{r}\) from the demodulator is first buffered in the soft information register. Then, the first \(k\) components, i.e., the soft information corresponding to the information bits, are output to the candidate sequence generator to construct a series of candidate information sequences. Next, the re-encoding unit encodes the candidate information sequences to obtain the candidate codewords. The residual \((n-k)\) components of \(\mathbf{r}\) are output to the correlation discrepancy calculation unit for calculating the correlation discrepancy of each candidate codeword. The candidate codeword and its correlation discrepancy are computed serially. After the candidate codewords and the corresponding correlation discrepancies are input into the decoding result update unit, the register is used to circularly shift the buffered candidate codeword. When all the operations for a single information sequence are completed, the soft information register performs a cyclic shift on the buffered data to generate another new information sequence. In this way, the operations for each information sequence are repeated. After all the \(l\) information sequences are processed, the final decoding output is obtained in the decoding result update unit.  
Among all the modules of the decoder, the candidate sequence generator and the correlation discrepancy calculation unit need to be carefully designed. For the candidate sequence generator, the iterative algorithm can significantly reduce the number of comparison operations and thus reduce the overall implementation complexity. For the correlation discrepancy computation unit, the complexity of the direct summation computation is high. In our design, we consider the systolic array architecture for the two modules. The systolic array architecture is composed of several modules working in pipeline mode, and each module is called a Processing Element (PE).