esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Spatial-Temporal Variability of Carbon-Water Fluxes in the Study Area  
The spatial variability of carbon-water fluxes in the study area has significant implications for the region's ecosystems. Existing research in this field highlights a growing interest in monitoring the consequences of WUE and GPP variations on ecosystems worldwide [41; 43; 91; 92; 93; 94; 95]. In this study, we conducted a comprehensive analysis of multiyear averaged WUE and GPP values for the Middle East, revealing substantial spatial heterogeneity across seven land cover types and 14 subtypes of climate patterns.  
The observed spatial variations in WUE and GPP can be attributed to a combination of topographic factors, vegetation physiological characteristics, and cultivation methods [38; 39; 96; 97]. These findings align with previous studies by [PERSON] et al. [98] and [PERSON] et al. [99], emphasizing the importance of considering these factors in ecosystem carbon-water flux assessments. Notably, the northern part of the study area, characterized by temperate climates (Cfa and Cfb) and hosting a variety of deciduous and evergreen forests, exhibited the highest annual WUE and GPP values. These regions benefit from relatively abundant water resources and favorable environmental conditions, leading to enhanced carbon productivity per unit of water consumed. In contrast, the central region of Iran, eastern Syria, and southern Iraq, which experience arid desert climates (BW and BS), displayed the lowest annual WUE and GPP values. In these regions, water scarcity and prolonged drought conditions pose challenges, ultimately leading to reduced carbon productivity.  
While our study sheds light on the spatial variations of WUE and GPP, it is important to indicate the presence of additional influential factors. The complexity of interactions between climatic conditions, topography, and vital ecological factors contributes to the observed spatial heterogeneity of WUE and GPP [100; 101].
# Formalism-Driven Development: Concepts, Taxonomy, and Practice +
## 2 Preliminaries
### Implementation Stage
#### 3.3.5 Channel  
According to Remark 4, a channel can be either synchronous or asynchronous. A synchronous channel usually serves synchronization purposes instead of data transfer within a system modeled by a system graph. Its data structure at least contains the metadata. For an asynchronous channel, it contains at least the metadata, a buffer, and a set of operations associated with the buffer.  
Regarding the implementation, a channel has two types: internal channel and external channel. An **internal channel** only receives messages within the system while an **external channel** can also receive messages from the outside of the system. An internal channel is naturally embedded into the control flow, while an external channel requires interaction with processes outside of the system. An outside process can be a program that sends messages to channels (by in-memory or on-disk interactions), a user who can input messages to channels (by I/O stream), a network protocol that passes messages to channels (by port), etc. A skeleton program integrates built-in modules to support the implementation of external channels according to concrete requirements.  
Notably, execution needs to take care of waiting for a channel. An internal channel \(c\) gets into **waiting** if \(c\) is synchronous and the sending system is not in the state right before sending a message or \(c\) is asynchronous and \(Cap(c)<1\). If \(c\) is an external channel, it gets into waiting if no outside process sends a message to \(c\).  
**Example 9** (Channel in Transaction Client).: _The transaction client in Example 1 has an anonymous action \(c^{\intercal}tx\) attached to the transition relation between init and waiting. This communication action will not proceed, i.e., channel \(c\) gets into waiting until it consumes a message via channel \(c\). From the perspective of implementation, the execution only resumes when state variable \(tx\) has the value received from channel \(c\)._  
_In our implementation, information about a new transaction is pushed into channel \(c\) when that transaction is submitted._
# Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources
## 2 System Model  
Without loss of generality, we consider the origin-centered UCA-based receiver consisting of \(M\) antenna elements, which tries to estimate DOAs of \(K\) signal sources as illustrated in Figure 1.  
The system model of this paper deals with generalized steering vector and received signal model, and thus it can be easily extended to any other array structures. In addition, it is assumed that all signal sources exist in Line of Sight (LoS) to the receiver; multipath signals are negligibly small or absent as in many related studies. Then, the received signal, \(\mathbf{y}(\in\mathbb{C}^{\mathrm{M}})\), at the UCA receiver is given by  
\[\mathbf{y}=\mathbf{A}\mathbf{x}+\mathbf{w}, \tag{1}\]  
where \(\mathbf{A}(\in\mathbb{C}^{\mathrm{M}\times\mathrm{K}})\) denotes the matrix concatenating the steering vectors of each incident signal source, i.e., \(\mathbf{A}=[\mathbf{a}(\gamma_{1})\cdots\mathbf{a}(\gamma_{k})\cdots\mathbf{ a}(\gamma_{K})]\). Herein, a column of \(\mathbf{a}(\gamma_{k})(\in\mathbb{C}^{\mathrm{M}})\) indicates the steering vector of \(k\in\{1,\cdots,K\}\)-th source. When considering a UCA as a receiving array antenna, the directional information, \(\gamma_{k}=\{\phi_{k},\theta_{k}\}\), represents a set consisting of the azimuth angle \(\phi_{k}(\in[-\pi,\pi])\) and the elevation angle \(\theta_{k}(\in[0,\pi/2])\) of the \(k\)-th signal source. In addition, we assume that the distance between all signal sources and the receiver is far enough and thus the far-field assumption is valid. Therefore, only the position vector of each array element and signal directional vector are used to effectively reflect the distance difference that occurs when the plane wave reaches each antenna to the phase under the far-field assumption. Then, the \(m\in\{1,\cdots,M\}\)-th element of the steering vector \(a_{m}(\gamma_{k})\) is defined as  
\[a_{m}(\gamma_{k})=e^{-j\frac{2\pi}{\lambda}\mathbf{p}_{m}\cdot\mathbf{s}( \gamma_{k})} \tag{2}\]  
where \(\lambda\) means the wavelength, \(\mathbf{p}_{m}(\in\mathbb{R}^{3})\) is the position vector of \(m\)-th element in Cartesian coordinate, and \(\mathbf{s}(\gamma_{k})(\in\mathbb{R}^{3})\) is the directional vector of \(k\)-th signal source given by  
\[\mathbf{s}(\gamma_{k})=-[\cos\phi_{k}\cos\theta_{k},\ \sin\phi_{k}\cos\theta_{k}, \ \sin\theta_{k}]^{\mathrm{T}}. \tag{3}\]  
Also, \(\mathbf{x}(\in\mathbb{R}^{K})\) in (1) means the received signal vector from \(K\) sources. In this paper, to evaluate the effect of the direction-finding performance caused only by additive noise, it is assumed that both Doppler frequency and carrier frequency are compensated in advance. In other words, \(x_{k}\), the \(k\)-th element of \(\mathbf{x}\), represents the signal power of \(k\)-th signal source, i.e., \(x_{k}=\sqrt{P_{k}}\), where \(P_{k}\) denotes the power of \(k\)-th signal source. Finally, \(\mathbf{w}(\in\mathbb{C}^{\mathrm{M}})\) denotes the Additive White Gaussian Noise (AWGN) vector, and it is assumed that \(\mathbf{w}\sim\mathcal{CN}(\mathbf{0},\sigma^{2}\mathbf{I}_{M})\).
composition of grapes. Lipid concentrations in grapes are affected by climatic conditions such as light exposure [8]. Of the geographical parameters in a wine-growing region, the climate is an important determinant of grape and wine composition [9]. Another study showed that grape berries from the Alvarinho white variety are more photosynthetically active when exposed to a higher number of sunlight hours, especially at the green phase of ripeness [10]. Irrespective of skin color, the most abundant unsaturated fatty acids (UFA) found in _Vitis vinifera_ berries are the C18 acids, specifically linoleic acid (C18:2), oleic acid (C18:1), and linolenic acid (C18:3) [11; 12]. The presence and levels of free fatty acids, both unsaturated and saturated, play a crucial role in influencing the production of volatile compounds by wine yeast [13]. During alcoholic fermentation, yeasts utilize only fatty acids in free form but not complex lipids with bounded fatty acids [14]. Furthermore, the lipid composition of grapes is essential in yeast cells because it stimulates the growth of yeasts and affects their metabolism in alcoholic fermentation [15]. The membrane structure of yeast significantly influences stress tolerance and resistance to substances such as acetic acid, ethanol, or acetaldehyde. In addition to encountering stressful environments, yeast cells have developed various molecular pathways to enable them to respond and adapt to changes in their surroundings [16].  
Although many analytical techniques for the determination of grape lipidomics have been developed [17; 18], they often involve time-consuming preparation procedures. Mass spectrometry has been widely utilized for the analysis of specific lipids. Gas chromatography with mass spectrometry (GC-MS) platform is commonly applied for lipid analysis, particularly for compounds like free fatty acids and steroids, which often require the derivatization of compounds in the sample matrix [19]. The development of high-resolution MS soft ionization methods has greatly facilitated the detection of compounds belonging to the cellular lipidome [20]. In recent years, due to the chemical complexity of grape-wine matrices and the lipid structural diversity and complexity, mass spectrometry (MS) in tandem with ultra-high-performance liquid chromatography (UHPLC) has become the innovative platform for detailed lipidomic analysis [2; 21]. UHPLC-MS/MS method has good sensitivity, specificity, and dynamic range [22].  
This study focused on the lipid composition of _V. vinifera_ L. cv. 'Marastina' berry skin from 11 vineyards located along a cultivation area considering the 'Marastina' as one of the significant indigenous white grape cultivars in Dalmatia. According to our knowledge, there are no reports on grape lipidomic in this variety. The objective was to investigate the comprehensive profile of lipids in 'Marastina' grape skins using a targeted approach by UHPLC-MS/MS. These results will be valuable for further research related to non-_Saccharomyces_ yeasts isolated from cv. 'Marastina' [23].
# A Study on the Effect of O\({}_{2}\) Diffusion on the Retention Time of Inert Agents
## 6 Conclusions  
In general, when evaluating the retention time of an inert agent, the retention time is evaluated as the time at which the initial inert agent concentration in the enclosure is reduced by 15%. However, in this study, it was evaluated based on the O\({}_{2}\) concentration rather than the agent concentration. This is because the inert agent's extinguishing ability is determined by how long the O\({}_{2}\) concentration can be maintained below a certain value.  
This study proposed a method that can reflect the O\({}_{2}\) diffusion when calculating the retention time of an inert agent, and this was verified through experiments and numerical analysis. Through this verification, a fixed interface slope \(\alpha\) was proposed for each agent, and the relationship between the interface slope \(\alpha\) and the retention time was proved. The interface slope \(\alpha\) eventually becomes proportional to the z-direction diffusion flux \(J_{d}\). Therefore, a large interface slope \(\alpha\) indicates that the z-direction diffusion flux \(J_{d}\) is large, which means that the retention time is prolonged.  
In addition, it was found that the turbulence mixing zone existed at the top of the enclosure owing to the turbulence of the incoming air. It was confirmed that the distortion of the interface slope \(\alpha\) was generated by this area. Therefore, it was confirmed that the difference between the theoretical retention time and the measured retention time was eventually caused by the turbulence mixing zone.  
When O\({}_{2}\) diffusion is reflected in the inert agent, the interface descending velocity \(\partial\dot{H_{i}}/\partial t\) is calculated as the sum of the z-direction advection velocity \(v_{a}\) and the z-direction diffusion velocity \(v_{d}\). Here, it was confirmed that the z-direction advection velocity \(v_{a}\) is proportional to the outflow volumetric flow \(\dot{V}_{o}\), and the z-direction diffusion velocity \(v_{d}\) is proportional to the interface slope \(\alpha\). Therefore, assuming that there is no difference in temperature inside and outside the enclosure, it was confirmed that the z-direction diffusion velocity \(v_{d}\) between the inert agents has a fixed value.  
Finally, it was confirmed that the theoretical retention time applied to the interface slope \(\alpha\), the leading edge of the interface \(H_{i}\), and the diffusion velocity \(v_{d}\) can reduce the difference from the measured retention time to within approximately 3%.  
Conceptualization, G.K. and H.-J.L.; data curation, G.K. and J.-H.C.; formal analysis, G.K., J.-H.C. and H.-J.L.; investigation, G.K. and J.-H.J.; software, G.K.; project administration, H.-J.L.; writing--original draft preparation, G.K.; writing--review and editing, H.-J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a 2-Year Research Grant of Pusan National University.  
The authors declare no conflict of interest.
## 4 Network Anomaly Forecasting  
In order to enable effective anomaly forecasting, it is necessary to predict the future network data based on historical performance data. The 5G vertical applications provide a diverse set of indicators, which can be used to conduct a comprehensive evaluation of network quality from multiple dimensions. These indicator data are presented in the form of continuous time series data, which can be utilized to predict the future network status and further achieve anomaly forecasting. To accomplish this, we employ a classic predictive network model called long short-term memory (LSTM) to predict the future network indicator data. LSTM is a type of recurrent neural network that is well suited for processing sequential data such as time series data. By training the LSTM model on historical performance data, we are able to generate accurate predictions of future network performance. In this section, the structure and algorithm of the network anomaly forecasting scheme are described in detail.
## 3 Results
### Barepodometric Platform  
Differences from pre- to postoperative parameters are reported in Table 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Preop Mean Value** & **Postop Mean Value** & \(p\)**-Value** \\ \hline FPI & 1.9 \(\pm\) 3.4 & 1.7 \(\pm\) 2.4 & _0.871_ \\ ALT (\({}^{\circ}\)) & 2.7 \(\pm\) 7.6 & 3.6 \(\pm\) 5.8 & _0.626_ \\ ALT (cm) & 0.75 \(\pm\) 2.1 & 1 \(\pm\) 1.6 & _0.626_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Physical examination outcomes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **No. (tot = 8)** \\ \hline Sex (M/F) & 7/1 \\ Age & 66.4 \(\pm\) 14.3 (48–81) \\ Side (Right/Left) & 6/2 \\ BMI & 28.14 kg/m\({}^{2}\) \\ \hline \hline \end{tabular} Abbreviations: M: male; F: female; BMI: Body Mass Index.  
\end{table} 
Table 2: Patients’ characteristics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PROMs** & **Preop Score** & **Postop Score** & \(p\)**-Value** \\ \hline AOFAS & 40.8 \(\pm\) 15.3 & 63.2 \(\pm\) 12.1 & _0.005_ * \\ MOxFQ & 66.9 \(\pm\) 14.3 & 34.4 \(\pm\) 14.9 & _0.002_ * \\ VAS & 4.9 \(\pm\) 1.8 & 2.5 \(\pm\) 2.1 & _0.037_ * \\ SF-36 & 63.6 \(\pm\) 9.4 & 74.2 \(\pm\) 10.3 & _0.027_ * \\
17-IFFI & 50.6 \(\pm\) 13.2 & 38.6 \(\pm\) 21.4 & _0.115_ \\ \hline \hline \end{tabular}  
* **Bold \(p\)-value** indicates reaching statistical significance.  
\end{table} 
Table 3: Clinical outcomes. We experimentally, peak pressure point was placed at the level of the calcaneus in 62.5% of cases, at the III metatarsal bone head in 25% of cases and at the II metatarsal head in 12.2% of cases. Postoperatively, peak pressure point was at the calcaneus in 87.5% and at the IV metatarsal base in 12.5%. The dynamic examination showed no significant changes from the pre- to postoperative period, except for a significant increase in mean pressure for the non-operated foot (Table 5). The peak pressure during the contact phase was localized at the calcaneus in 87.5% of cases and at the V metatarsal base of the bone in 12.5% of cases before surgery; at the last follow up, the peak pressure was localized in 100% of cases at the calcaneus. Preoperatively, during the intermediate support phase, the peak pressure was localized at the calcaneus in 50%, 12.5% and 37.5% of cases at the I, III and V metatarsal heads, respectively. Postoperatively, the peak pressure was localized at the calcaneus in 75% of cases and at the V metatarsal base in 25% of cases. During the propulsion phase, the peak  
Figure 4: Visual plantar pressure distribution before **(left)** and after surgery (**right**). The software used a color code based on specific pressure levels, indicating the varying loads as strong (red), medium-strong (green) and medium (blue).
## 2 Method Description
### Optimization Problem  
A trajectory of the FLMS under consideration can be represented as a parametric curve \(t\mapsto\mathbf{\mathrm{q}}(t)\), defined from the time interval \([0,T]\) into the joint space. Let us assume that the desired \(i\)-th eigenfrequency along the specified trajectory is \(\lambda_{i,\mathrm{d}}(t)\). The objective is to find values of the design parameters \(\mathbf{\mathrm{y}}\) such that the \(i\)-th eigenfrequency is as close as possible to \(\lambda_{i,\mathrm{d}}(t)\), for all \(t\in[0,T]\).  
The above-mentioned task can be recast as the minimization of the cost function  
\[f(\delta\mathbf{y})=\frac{1}{2}\int_{0}^{T}\Bigl{[}\hat{\lambda}_{i}^{p}( \mathbf{q}(t),\mathbf{y}_{0}+\delta\mathbf{y})-\lambda_{i,\mathbf{d}}(t)\Bigr{]} ^{2}\mathrm{d}t. \tag{10}\]  
The design parameters \(\delta\mathbf{y}\) must be constrained to assure the physical feasibility of the solution (e.g., avoiding negative values of mass or stiffness). Moreover, it may be desirable to impose specifications on the weight or bulk of the multibody system in order to not compromise its mechanical integrity or limit expenses, either of materials or energy requirements. Such constraints can be represented by a set \(\Gamma\) in which the optimal \(\delta\mathbf{y}\) is sought. Hence, the optimization problem is  
\[\min_{\delta\mathbf{y}\in\Gamma}f(\delta\mathbf{y}). \tag{11}\]  
It is important to note that this optimization problem is a quadratic program. In fact, it can be proved, with tedious but straightforward calculation, that (11) is equivalent to  
\[\min_{\delta\mathbf{y}\in\Gamma}\biggl{(}\frac{1}{2}\delta\mathbf{y}^{T} \mathbf{R}\delta\mathbf{y}+\mathbf{h}^{T}\delta\mathbf{y}\biggr{)}, \tag{12}\]  
where  
\[\mathbf{R}=\left[\begin{array}{cccc}\int_{0}^{T}l_{i,1}^{p}(\mathbf{q}(t))^ {2}\mathrm{d}t&\int_{0}^{T}l_{i,1}^{p}(\mathbf{q}(t))l_{i,2}^{p}(\mathbf{q}(t) )\mathrm{d}t&\cdots&\int_{0}^{T}l_{i,1}^{p}(\mathbf{q}(t))l_{i,n_{y}}^{p}( \mathbf{q}(t))\mathrm{d}t\\ &\int_{0}^{T}l_{i,2}^{p}(\mathbf{q}(t))^{2}\mathrm{d}t&&\vdots\\ &&\ddots&\int_{0}^{T}l_{i,n_{y}-1}^{p}(\mathbf{q}(t))l_{i,n_{y}}^{p}(\mathbf{q }(t))\mathrm{d}t\\ \text{symmetric}&&\int_{0}^{T}l_{i,n_{y}}^{p}(\mathbf{q}(t))^{2}\mathrm{d}t \end{array}\right] \tag{13}\]  
and  
\[\mathbf{h}=\left\{\begin{array}{c}\int_{0}^{T}l_{i,1}^{p}(\mathbf{q}(t)) \Bigl{[}l_{i,0}^{p}(\mathbf{q}(t))-\lambda_{i,\mathbf{d}}(t)\Bigr{]}\mathrm{ d}t\\ \int_{0}^{T}l_{i,2}^{p}(\mathbf{q}(t))\Bigl{[}l_{i,0}^{p}(\mathbf{q}(t))- \lambda_{i,\mathbf{d}}(t)\Bigr{]}\mathrm{d}t\\ \vdots\\ \int_{0}^{T}l_{i,n_{y}}^{p}(\mathbf{q}(t))\Bigl{[}l_{i,0}^{p}(\mathbf{q}(t))- \lambda_{i,\mathbf{d}}(t)\Bigr{]}\mathrm{d}t\end{array}\right\}. \tag{14}\]  
The polynomials \(l_{i,j}^{p}\), for \(j=0,1,\ldots,n_{y}\) are defined in Equation (9). It is important to recall that quadratic programming mandates linear equality and/or inequality constraints. Hence, it is recommended to cast the constraint set \(\Gamma\) accordingly in order to benefit from the numerical reliability of quadratic programming algorithms [22].
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 3 State-of-the-Art  
With the emphasis and success of private mobile networks in industrial settings, the NB-IoT has found its application in the industrial world--Narrow-Band Industrial Internet of Things (NB-IIoT). Its application relies on the existing infrastructure of the legacy LTE. However, the ad hoc WSN forms the bedrock of the IoT. This is because the introduction of a unifying infrastructure that simply allows the identification, addressing and communications of sensors and objects has enhanced the massive connectivity of these devices. This section is organized into two main sections. The first section is about the developmental trends and latest technological features of WSN, IoT, and NB-IoT. The second section presents the application case studies of WSN, IoT, and NB-IoT in industrial environments, respectively.
# Kinematics and Statics of the Gough-Stewart Platform
###### Abstract
The unit screw matrix obtained in kinematic analysis can be directly applied in static analysis, which results in a compact program structure. Then, the force screw equation using the unit screw matrix allows for a direct calculation of all constraint forces and torques at each joint. The main contributions of this paper are summarized as follows:  
1. Kinematics and statics modeling are unified via expressing parameters in screw coordinates. The kinematic parameters in screw form can be directly used in static equilibrium equations established using force screws. 2. Through combining kinematics and statics analysis, the forces and moments acting on each element of a mechanism in different configurations can be computed systematically. 3. The method proposed in this paper is highly universal and can be applied to the kinematics and statics analysis of variable mechanisms, and the validity of the method is verified via the kinematics and statics analysis of the Gough-Stewart platform. Compared with the conventional static analysis method, using the velocity screw and the force screw might eliminate complex algebraic manipulation and allow for the direct calculation of the six constraint variables for all joints. This method can also be applied to other planar and spatial complex mechanisms and manipulators. In this paper, the contents are arranged as follows: Section 2 introduces the geometry of the Gough-Stewart parallel mechanism. Section 3 describes the kinematic modeling method in screw form for multi-rigid-body systems and the kinematic modeling of the Gough-Stewart platform. Section 4 introduces the static modeling method using force screws and the unified kinematic and static modeling approach. The method is demonstrated through static modeling of the Gough-Stewart platform. In Section 5, the motion of the moving platform and the external loads are given, and the numerical simulation to perform the effectiveness of the modeling method is carried out. And finally, Section 6 concludes the paper.