esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
The DBSCAN algorithm used in the CAVeCTIR model can be expressed in pseudocode as follows:  
DBSCAN(D, eps, MinPts): \\ C = 0 \\ for each unvisited point P in dataset D: \\ mark P as visited \\ NeighborPts = regionQuery(P, eps) \\ if sizeof(NeighborPts) < MinPts: \\ mark P as NOISE \\ else: \\ C = next cluster \\ \end{tabular}  
expandCluster(P, NeighborPts, C, eps, MinPts)  
expandCluster(P, NeighborPts, C, eps, MinPts):  
add P to cluster C  
for each point P' in NeighborPts:  
if P' is not visited:  
mark P' as visited  
NeighborPts' = regionQuery(P', eps)  
if sizeof(NeighborPts') >= MinPts:  
NeighborPts = NeighborPts joined with NeighborPts'  
if P' is not yet member of any cluster:  
add P' to cluster C  
regionQuery(P, eps):  
return all points within P's eps-neighborhood (including P)
## 3 Results and Discussion
### Results of Surface Roughness Measurement  
Due to the layered nature of model formation in the FFF additive manufacturing process, the roughness parameters were determined on the side surface of the reference component in the vertical direction (perpendicular to the layer system). The sidewall is obtained by extruding successive layers in the \(Z\)-axis, and its roughness is directly influenced by the height of the layer of the applied filament. Figure 6 shows the results of measuring the \(Ra\) parameter values for analyzed models.  
As expected, the layer height has a direct effect on the value of the \(Ra\) parameter describing the average height of surface roughness. Increasing the layer height by 0.04 mm increased the value of the \(Ra\) parameter by an average of 2.23 \(\upmu\)m when changing the height from 0.12 mm to 0.16 mm and further to 0.20 mm. For a layer height of 0.12 mm, the value of the \(Ra\) parameter for the produced samples averaged 7.70 \(\upmu\)m, which is comparable to the surface obtained by drilling or the surface obtained by rough turning. No clear and unambiguous influence of the fill rate on the measured values of the \(Ra\) parameter was noted.  
Figure 7 shows the results of measuring the values of the \(Rq\) parameter for individual models manufactured with varying FFF process parameters.  
Similarly, to the \(Ra\) parameter, a lower value of the \(Rq\) parameter should be assessed as more beneficial and indicating a higher quality of the manufactured model. In the case of the mean square deviation of the profile \(Rq\), an increase of its value by an average of 2.74 \  
roughness. The measurements can be used to estimate the influence of the layer height on the obtained values of surface parameters when selecting the adjustable parameters of the FFF process carried out in similar conditions using PLA as the model material. The obtained set of results did not allow for determining a clear and unambiguous influence of changes in the percentage of the filling degree on the measured values of selected parameters describing the roughness of the lateral surface of the models (_Ra_, _Rq_, and _Rz_).  
Figure 9 shows the measured lateral surface profiles for representative samples from the group with different layer heights.  
According to the characteristics of the FFF process, the S1 sample manufactured using a 0.12 mm layer was characterized by a higher density of peaks and valleys compared to the other samples. This directly affects the height of the surface unevenness--mainly by limiting the depth of the valleys. As a result, a model of higher technological quality was obtained, but it was obtained by dividing the 3D CAD model into a larger number of layers during the preparation of the process and ultimately requires a larger number of working passes, extending the manufacturing time.  
Figure 8: Results of measurements of the _Rz_ parameter values of the surface roughness of analyzed models.  
Figure 7: Results of measurements of the _Rq_ parameter values of the surface roughness of analyzed models.
## 3 Discussion  
Post-harvest withering plays a central role in determining the compositional and sensory characteristics of Valpolicella red wines [1; 3]. From a quantitative point of view, the main physiological change associated with this traditional practice is water loss, that is carried out up to an average of 30% weight loss depending on wine style. This has major implication for grape composition, most notably increased concentration of metabolites such as sugars, phenolics, and certain aroma compounds, directly influencing composition of the resulting wine. Additional important consequences of increased sugar levels are related to changes in yeast metabolism, which can further impact wine composition. However, it has been recently shown that post-harvest withering is not simply a dehydration process, with many complex metabolic transformations beyond simple concentration taking place inside the berry, inducing important modifications in the pool of grape secondary metabolites, including volatile compounds [17]. In consideration of this complex scenario, one of the purposes of the present study was to investigate how and to what extent withering of the grapes affects the volatile composition of the resulting wine. Second, and most important, this study had the objective to assess the potential of an alternative withering approach to modulate Corvina wine volatile composition. Although post-harvest withering is traditionally carried out in warehouses (locally called \"frtutaio\"), there is an ongoing interest towards the exploration of alternative strategies that can be applied to obtain a suitable degree of over-ripening or withering, also with the aim of producing alternative wine types and styles [2]. Among these, cane-cut on-vine has been shown to positively influence wine aroma and phenolic composition [6; 7; 8; 9; 10; 11; 12]. In the present study, an alternative approach to on-vine withering, still based on blocking xylem flow but not involving cane cutting, was investigated in comparison with conventional fruttaio withering. As sugar levels at grape crush were similar for both withering modalities, any difference is expected to result from differences in grape composition in terms of secondary metabolites or interaction with yeast.
# State Merging and Splitting Strategies for Finite State Machines Implemented in FPGA
## 2 Materials and Methods
### State Splitting Procedure
#### 2.4.2 State Splitting Procedure for Critical Path Minimization  
The state splitting procedure for speed maximization comes from Ref. [40] but is adapted to use both binary and one-hot types of encoding. Just like in the work [40], the key strategy relies on searching for the set \(D\) of all states fulfilling the conditions for splitting:  
\[\exists a_{i}\in A,\text{card}(B(a_{i}))\,>1, \tag{17}\]  
\[\exists a_{j}\in B(a_{i}),r_{j}\leq r^{*}, \tag{18}\]  
where \(r_{j}\) is the number of arguments of the function that initiates the transition to state \(a_{i}\), \(r^{*}\) is the upper limit of the number of arguments for all transition functions, \(A\) is a set of internal states, and \(B(a_{i})\) is a set of states with transitions to state \(a_{i}\).  
If the conditions are satisfied, for each state from the set \(D\) the trial splitting is made. Each state \(a_{i}\in D\) is split into two new states. The first state is related to transitions from state \(a_{j}\in B(a_{i})\), where \(r_{j}=\max\). The second state is related to the remaining transitions to state \(a_{i}\). Finally, state \(a_{i}\) is selected for real splitting, which best fits the optimization criteria in regard to the FSM operation speed (minimization of critical path length \(S_{i}\)).
# An S/C/X-Band 4-Bit Digital Step Attenuator MMIC with 0.25 \(\upmu\)m GaN HEMT Technology
## 4 Conclusions  
A DC-12 GHz 4-bit digital step attenuator MMIC using 0.25 \(\upmu\)m GaN HEMT technology was presented. The proposed attenuator was implemented using DPDT switched-path topology and shows low insertion loss, RMS phase, and amplitude errors. The maximum attenuation range was 30 dB with an attenuation step of 2 dB. The measured insertion loss of the reference state was 2.8\(-\)8.3 dB at DC-12 GHz. The RMS phase and amplitude errors were less than 8.5\({}^{\circ}\) and less than 1 dB, respectively. The DC power consumption was nearly zero. The compact chip size was 2.45 mm \(\times\) 1.75 mm including pads. The proposed GaN digital step attenuator MMIC can be expected to be applied to high-power and wideband phased array radar systems covering the S/C/X-band.  
Conceptualization, J.-G.K.; validation, J.-G.K.; formal analysis, J.-G.K.; data curation, writing--original draft preparation, J.-G.K.; writing--review and editing K.-H.L.; visualization, K.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data can be obtained from the authors on request.  
The work reported in this paper was conducted during the sabbatical year of Kwangwoon University in 2015.  
The authors declare no conflict of interest.
# Knowledge Management in Construction Health and Safety Based on Ontology Modeling
## 3 Development of Ontology Model for Construction Health and Safety
### Ontology Scope and Knowledge Sources
#### 3.2.2 Risk Context  
Risk contexts refer to construction health- and safety-related knowledge, such as risk type, risk precursors, risk consequence and mitigations.  
* An essential component of risk knowledge is risk precursors. These are conditions, occurrences and progressions before an accident. An accident can happen as the combined result of different precursors, and similar precursors tend to occur as similar accidents [40].
* There are diverse forms of safety risks in construction that may coincide with particular construction activities. The several typical types and classifications of risk and risk scenarios are defined in the Occupational Injury and Illness Classification Manual and accident reports, as seen in Table 2. However, more risks than those listed occur on the construction site during construction, and even a single construction task can be linked to several risks. For example, installing a roof can result in an eye injury, a fall from height, heat and sun exposure, hand-arm vibration, being struck by objects, etc.
* Risk mitigation contains four subclasses: equipment, material, labor and safety measures. It is further explained in Section 3.3.3.
# Antimicrobial Volatiles of the Insect Pathogen _Metarhizium brunneum_
## 2 Materials and Methods  
### M. brunneum Isolates and Culture Conditions for Collecting VOCs  
VOCs of _M. brunneum_ isolates ARSEF 4556 (origin: _Boophilus_ sp., USA), ARSEF 3297 (origin: _Boophilus_ sp., Mexico) and V275 (origin: _Cydu pomonella_, Austria) were collected from cultures produced on four different media known to influence their physiology and virulence [32]. Media included (_w/v_): (1) osmotic stress medium (OSM, 8% glucose, 2% peptome, 5.5% agar, 5.5% KCl), (2) high C:N (75:1) medium (9.1% glucose, 1% peptome, 2% agar), (3) intermediate C:N (35:1) medium (4% glucose, 1% peptome, 2% agar) and (4) low C:N (10:1) medium (0.6% glucose, 1% peptome, 2% agar). VOCs were collected 7 and 14 days post-inoculation, corresponding with the mycelial and sporulating stages of the fungi. All media were obtained from Sigma (Poole, UK), except peptome, which was obtained from Oxoid, Hampshire, UK. Cultures were produced using 10 mL medium in 25 mL glass vials incubated in the dark at 25 \({}^{\circ}\)C. There were three technical replicates per treatment (isolate, culture medium) with the whole experiment being repeated on two independent occasions.
## 2 Seawater Channel Modelling Elements
### Scintillation and Turbulence
#### 2.5.2 Turbulence  
Turbulence in underwater environments results from the irregular motion of water masses, leading to variations in the refractive index of the water column.  
Turbulence affects the water's AOP by causing light scattering and refraction. This leads to effects such as beam spreading, beam wandering, and changes in the angular distribution of light. These effects contribute to the overall attenuation and degradation of the optical signal and are considered when analysing AOP, such as beam attenuation and optical depth.
## 3 Design of Hardware and Software for the Data Monitoring System
### Design of Hardware for the Data Monitoring System
#### 3.1.1 Design of the Power Track  
Two different supplying systems are included. One +5 V DC current for the alimentation of the ATMega328 and others. This alimentation will be installed on the Master and Slave modules. One +3.3 V DC current for the alimentation of the xBee integrated (Wireless module).  
The selected devices to supply the power are the NCP7805 TG regulator for the +5 V and the UA78M35 KKCS for +3.3 V, allowing the data-logger being connected to a wide range of DC transformers.  
Besides the power filter has been improved from the Arduino design, adding a small ceramic capacitor, using parallel connexion, to the electrolytic filter. Thus, final combined filter has better proprieties in terms of resistance and price.  
Note that, if the location has a high rate of illumination, it could be considered the option of looking for a position with enough luminal intensity in order to include a photovoltaic source to recharge a data-logger battery. Once the supply system has been designed, the next step is to define the position of each component and connect the tracks with the external connections. This step will be different in all the modules.  
The PINs used to connect different boards have to cover both sides in order to be able to have a module up and also down. The connection distribution is similar to the Arduino modules, that way it is possible to create new applications just adding new software and the corresponding board. A reset switch is also available in the Master and Slave modules; moreover this signal affects the connected Wireless module too.
## 3 Results
### Evidence Map  
Figures 3 and 4 present the results of the evidence map for the 18 studies. In addition, Table 5 shows the levels of evidence of the meta-analyses included in the study according to the PAGAC.  
Figure 3: Descriptive evidence map of meta-analyses. TE, therapeutic exercise; MT, mid-term; LT, long-term. On the \(x\)-axis the studies are classified according to the study variable, while on the \(y\)-axis they are categorized according to their score on the Modified Quality Assessment Scale for Systematic Reviews (AMSTAR), with a score above 20 being considered high-quality.  
Figure 4: Mapping based on effect size of meta-analyses. TE, therapeutic exercise; MT, mid-term; LT, long-term. On the \(x\)-axis the studies are classified according to the effect size of the meta-analysis with standard mean difference (SMD). On the \(y\)-axis they are categorized according to the strength of evidence based on the Physical Activity Guidelines Advisory Committee (PAGAC).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**C Criteria**} & \multicolumn{1}{c}{**Effect**} & **Evidence** \\ \hline
**Outcome** & & & & & & & & \\
**Headache and Intervention Type** & **Applicability** & **Generalizability** & **Risk of Bias or Study Limitations** & **Quantity and Consistency** & **Magnitude and Precision of Effect** & **SMD (98\% CI)** & \\
**(No. of Meta-Analyses)** & & & & & & & \\ \hline
**Pain intensity** & & & & & & & \\ \hline - & **Migraine, AE (2)** & Strong & Moderate & Strong & Limited & Moderate & \(-1.16\) (\(-1.90\), \(-0.41\)) & **Moderate** \\ \hline - & **TTH, MT (3)** & Strong & Moderate & Limited & Limited & Moderate & \(-0.83\) (\(-1.47\), \(-0.19\)) & **Limited** \\ \hline - & **TTH, MIX (2)** & Strong & Moderate & Strong & Limited & Limited & \(-0.59\) (\(-0.85\), \(-0.33\)) & **Moderate** \\ \hline - & **C**CH, MIX (3)** & Strong & Limited & Moderate & Limited & Limited & \(-0.49\) (\(-0.86\), \(-0.12\)) & **Limited** \\ \hline
**Frequency of symptoms** & & & & & & & \\ \hline - & **Migraine, AE (2)** & Strong & Moderate & Limited & Limited & Moderate & \(-0.75\) (\(-1.08\), \(-0.43\)) & **Limited** \\ \hline - & **TTH, MT (2)** & Strong & Moderate & Limited & Limited & Moderate & \(-0.91\) (\(-2.18\), \(0.36\)) & **Limited** \\ \hline - & **C**CH, MIX (2)** & Strong & Moderate & Limited & Limited & Limited & \(-0.30\) (\(-0.84\), \(0.23\)) & **Limited** \\ \hline
**Disability** & & & & & & & \\ \hline - & **TTH, MT (2)** & Strong & Moderate & Limited & Limited & \(-0.27\) (\(-0.47\), \(-0.06\)) & **Limited** \\ \hline \hline \end{tabular}  
* 98% CI, 98% confidence interval AE, Aerobic exercise CCH, Corvogenic headache MT, Manual therapy intervention; MIX, mixed intervention; SMD, standard mean difference; TTH, tension-type headache  
\end{table}
Table 5: Committee assigned grades for the effects of manual therapy or therapeutic exercise on headache-related outcomes.
## 4 Discussion
### Model Limitations  
The simulations presented in the current study were based on the big-leaf modelling methodology [97], where the canopy of a stand is considered a homogeneous layer with constant photosynthetic parameters. The photosynthetic algorithm used in the current model version (P-model v1.0, 2020) does not use species-specific parameters nor does it define different canopy layers (sun/shade) with discrete photosynthetic characteristics. It is thus a generic photosynthetic carbon assimilation algorithm that optimizes the carbon assimilation-transpiration trade-off. An alternative implementation of the sun/shade big-leaf model for the Mediterranean pine forests of Lesvos has been presented in [PERSON] et al. [43] with GPP estimates similar to those presented in this study. A species-specific implementation of our model is currently under development.  
In the current version of the model, a key addition was the empirical soil heterotrophic respiration algorithm, which is constrained with site-specific soil flux data. This enabled us to estimate NEP and explore whether the study plots will remain C-sinks under future climatic conditions. In that respect, a species- and (potentially) site-specific stand autotrophic respiration model could increase the confidence of our simulations. It should be noted that the current version of the model lacks demographics, i.e., it provides snapshots of annual carbon fluxes on predefined stands. Consequently, the model cannot be used for dynamic vegetation simulations but rather can be set up to simulate C-fluxes across stands with varying environmental conditions and stand structure.
## 2 Materials and Methods  
As we specified in Section 1.3, the programmability of SDN allows us to extend the SDN controller with an AI-based protection functionality. One of the obvious applications of AI is the advanced traffic handling, and consequently, automated filtering during an SDN-based firewall's operation. The AI element functionality is often supposed to work as a decision element. In most cases, the AI element helps to determine one of the decision states according to incoming flow characteristics.  
In this article, we introduce a specific neural network-based decision procedure that can be considered for application in any flow characteristic-based traffic handling controller. For the sake of being concise and transparent, we consider the decision element state space to be composed of the following items: allow, block, forward to selected ports, application layer inspection and four levels of quality of service (QoS) settings (low, normal, high and critical). For the same reason, we consider only L4 communication, as described in Section 3.2.1. Nevertheless, the decision element, which includes other possible communication protocols and an extended set of items in the state space, can be developed using the same procedure.  
Therefore, in order to demonstrate the idea of this contribution, we aim to develop a decision procedure capable of making a decision based upon the incoming flow characteristics defined by both source and destination IPv4 addresses and also by the amount of traffic expressed as packets per second. Each IP address is composed of four octets, which are used as unique inputs. In addition, source and destination port numbers are considered as relevant inputs. Hence, 11 inputs specify the decision.  
While regarding artificial neural network as an engine for the decision procedure, we will describe briefly below three hypotheses to develop the traffic handling controller described above.
## 3 Methods
### Topic Modeling (LDA)  
We employ the LDA approach for topic modeling. LDA is a generative model applied to identify latent topics in a collection of documents [70; 71]. The underlying assumption is that a probability distribution of latent topics represents each document. Additionally, each topic is assumed to be a probability distribution of words, with both the topic distributions across all documents and the word distributions of topics sharing a common Dirichlet prior. The objective of LDA is to uncover the latent topics and their corresponding probabilities within the collection, along with the distribution of topics across each document. This is achieved by maximizing the likelihood of the observed data, given the topic assignments for each word in each document and the topic distributions across the entire matrix of documents (rows) and words (columns). The likelihood function is described as follows:  
\[\mathrm{P}(Documents,topics,words|\alpha,\beta)=\prod_{d=1}^{D}p(\theta_{d}| \alpha)\prod_{n=1}^{N_{d}}p(z_{d,n}|\theta_{d})p(w_{d,n}|z_{d,n},\phi_{k}) \prod_{k=1}^{K}p(\phi_{k}|\beta)\]  
\(D\) is the total number of documents in the corpus. \(\theta\) is a document topic distribution, and \(\theta_{d}\) is the topic distribution for document \(d\) of \(K\) topics and \(p(\theta_{d}|\alpha)\sim Dir(\alpha)\). \(K\) is the predefined value for several topics. \(N_{d}\) is the total words in document \(d\). \(\phi\) is a topic-word distribution, where \(\phi_{k}\sim Dir(\beta)\). \(z_{d,n}\) is the topic assignment and a multinomial distribution with parameter \(\theta_{d}\).  
We utilized a collapsed Gibbs sampling technique implemented in the STM package [72] for parameter estimation. The number of topics is optimized using semantic coherence [73] and exclusivity [74; 75] metrics for adaptive LDA model selection. Semantic coherence is maximized when the most probable words in each topic frequently co-occur, and exclusivity maximizes the criterion measuring the exclusive co-occurring keywords between topics. The selected number of topics \((k)\) based on semantic coherence and exclusivity from LDA analysis provide topic-word and document-topic distribution.