esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of Covering Purslane on the Quality of the Grape Berries
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fermentation Stage**} & \multirow{2}{*}{**Years**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Treatments**} & \multirow{2}{*}{**Sig.**} \\ \cline{3-4}  & & & **CK** & **CP** & \\ \hline \multirow{4}{*}{Must} & \multirow{4}{*}{2018} & TSS (\(\circ\)Brix) & 23.47 \(\pm\) 0.18 & 23.00 \(\pm\) 0.26 & ns \\  & & TA (g/L) & 8.90 \(\pm\) 0.13 & 8.70 \(\pm\) 2.85 & ns \\  & & pH & 3.48 \(\pm\) 0.01 & 3.29 \(\pm\) 0.01 & ns \\ \cline{2-4}  & \multirow{4}{*}{2019} & TSS (\(\circ\)Brix) & 24.03 \(\pm\) 0.22 & 23.30 \(\pm\) 0.12 & * \\  & & TA (g/L) & 7.34 \(\pm\) 0.23 & 7.40 \(\pm\) 0.11 & ns \\  & & pH & 3.42 \(\pm\) 0.01 & 3.45 \(\pm\) 0.13 & ns \\ \hline \multirow{4}{*}{Wine} & \multirow{4}{*}{2018} & Residual sugar (g/L) & 1.98 \(\pm\) 0.33 & 1.88 \(\pm\) 0.17 & ns \\  & & pH & 4.09 \(\pm\) 0.01 & 3.93 \(\pm\) 0.00 & * \\  & & TA (g/L) & 4.95 \(\pm\) 0.19 & 5.33 \(\pm\) 0.17 & * \\  & & Alcohol degree (\%, \(v/v\)) & 12.90 \(\pm\) 0.58 & 12.40 \(\pm\) 0.10 & * \\  & & Volatile acid (g/L) & 0.51 \(\pm\) 0.03 & 0.53 \(\pm\) 0.01 & ns \\ \hline \multirow{4}{*}{Wine} & \multirow{4}{*}{2019} & Residual sugar (g/L) & 1.73 \(\pm\) 0.11 & 1.17 \(\pm\) 0.06 & * \\  & & pH & 4.07 \(\pm\) 0.12 & 4.14 \(\pm\) 0.01 & ns \\ \cline{1-1}  & & TA (g/L) & 4.90 \(\pm\) 0.01 & 5.53 \(\pm\) 0.06 & * \\ \cline{1-1}  & & Alcohol degree (\%, \(v/v\)) & 12.57 \(\pm\)0.07 & 12.22 \(\pm\) 0.12 & ns \\ \cline{1-1}  & & Volatile acid (g/L) & 0.55 \(\pm\) 0.02 & 0.63 \(\pm\) 0.08 & ns \\ \hline \hline \end{tabular} Values are reported as means \(\pm\) SD of three biological replicates, * indicates significant differences between the control and covering purslane (\(p<\) 0.05, \(t\)-test). ns = not significant.  
\end{table} 
Table 5: Must and wine physicochemical parameters of the clean tillage (CK) and covering purslane (CP). Figure 8: Effect of covering purslane on the concentrations of different flavonoid compounds in wines. (**a**) in the year 2018; (**b**) in the year 2019. * indicates significant differences between the control and covering purslane (\(p<\) 0.05, \(t\)-test).
## 4 Evaluation Method
### Weight Calculation Method  
#### 4.1.1 Expert Scoring Method (ESM)/Delphi  
The expert scoring method and the Delphi method [104] are fundamentally similar evaluation techniques; however, they differ in their implementation details and focus. Both methods prioritize the collection of expert opinions anonymously to mitigate mutual influence among experts and enhance the objectivity of the assessment. Expert scoring methods typically concentrate on the direct scoring of specific items, while the Delphi Principle adopts a more systematic method to predictive inquiry [105].  
Both methods are inherently subjective, relying on the personal judgment of experts. The information collection process for both methods often entails a longer evaluation cycle, as it requires gathering insights from multiple experts and conducting a thorough assessment. The absence of direct communication and discussion among experts may lead to the omission of significant information and perspectives, potentially compromising the accuracy and comprehensiveness of the forecast results. Consequently, these methods are often employed in conjunction with other techniques to minimize the impact of human error on the outcomes [7; 106] (Figures 5 and 6b).
# Polymer/Silica Hybrid Waveguide Thermo-Optic VOA Covering O-Band
## 1 Introduction  
To meet the ever-increasing demands of high-capacity communication, wavelength-division-multiplexing (WDM) techniques are widely researched and deployed for optical communications [1; 2; 3]. The total bandwidth is boosted, with the wavelength number (channels) increasing in the WDM system, via the arrayed waveguide grating (AWG) [4; 5]. Meanwhile, the optical signal power level difference deteriorates the optical signal-to-noise ratio (OSNR) and bit error rate (BER). The variable optical attenuator (VOA) is a critical component and effective method to preserve the power uniformities across all signal channels. The integration of the AWG and VOA arrays not only achieves wavelength blocking and equalizing, but it also enhances the reliability and reduces the cost [6; 7]. The power consumption of the VOA array is a challenge to their marriage on a single chip because of the temperature sensitivity of the AWG. Among all kinds of VOAs demonstrated [8; 9; 10; 11; 12; 13; 14; 15], polymer-based planar lightwave circuit (PLC) is an ideal platform to realize large scale integration, due to a large transparent window, low power consumption, and easy fabrication [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. These silicon-based optical devices offer low power consumption and fast speed, but technical issues, such as temperature dependence, polarization dependence, and optical fiber coupling, remain challenges for commercialization. Notably, the WDM system is operated under a large bandwidth. Especially coarse WDM (CWDM) in the O-band shows the center wavelengths with 1270 nm (lane 0), 1290 nm (lane 1), 1310 nm (lane 2), and 1330 nm (lane 3), respectively. VOA should show a large bandwidth cooperating with the WDM system. However, broadband polymer-based VOA is less discussed. In this paper, we design and fabricate a polymer/silica hybrid waveguide thermo-optic VOA, based on Mach-Zehnder interferometers (MZIs), covering the O-band. The multimode interferences (MMIs) used in the VOA are optimized to achieve low loss and large bandwidth. The VOA shows an extinction ratio (ER) of 18.64 dB at 1310 nm under 8.72 mW. An attenuation is larger than 6.99 dB covering the O-band. The rise and fall time of the VOA are 184 us and 180 us, respectively.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 2 Background and Related Work
A first attempt in this direction, beyond the preliminary version of the work presented in [10], was carried out in [9], where Arduino development boards connected to uBLOX N211 NB-IoT modems were used to collect data not only on the serving cell but also on neighboring cells. Collected data for each NPCI detected were collected over an area of \(4.5\,\mathrm{km}^{2}\) but averaged over \"pixels\" of \(20\times 20\,\mathrm{m}^{2}\), leading thus to a spatial discretization of data. WkNN-based fingerprinting was then adopted, using RSRP as RF parameter, and the authors reported an accuracy of about 90 m in the best case. It is worth noting that NB-IoT is not the only option for positioning using a narrowband technology: recent investigations have focused on other technologies, such as LoRa, with a positioning error between 10 and 20 m [22; 23], and SigFox, albeit with rather large errors in the order of several hundreds of meters [24; 25]. NB-IoT has, however, the notable advantage of being deployed by mobile network operators, allowing thus to leverage existing infrastructure to position devices, without the need to place anchor nodes to cover the area of interest.
## 3 The Setup
### The Entanglement Distribution Scheme  
The implementation of the GHZ\({}_{3}\)MQPEC protocol requires a versatile composite system consisting of smaller subsystems. The unique feature of this configuration is the entanglement of the corresponding qubits in the \(|GHZ_{3}\rangle\) state across all triplets of associated quantum registers. This is an immediate consequence of the employed Uniform \(|GHZ_{3}\rangle\) Distribution Scheme, as explained in the next Definition 2.  
**Definition 2** (Uniform \(|GHZ_{3}\rangle\) Distribution Scheme): _The Uniform \(|GHZ_{3}\rangle\) Distribution Scheme stipulates the following:_  
* _Alice_,_ \(0\leq i\leq n-1\)_, is endowed with an_ \(m\)_-qubit input register_ \(AIR_{i}\)_._
* _For every Alice_\({}_{i}\)_, Sophia, and Trent, each utilize an_ \(m\)_-qubit input register corresponding to Alice_\({}_{i}\)_, denoted by_ \(SIR_{i}\) _and_ \(TIR_{i}\)_,_ \(0\leq i\leq n-1\)_, respectively._
* _For every Alice_\({}_{i}\)_,_ \(0\leq i\leq n-1\)_, Trent generates_ \(3m\) _triplets_ \((p,q,r)\) _entangled in the_ \(|GHZ_{3}\rangle\) _state. Trent keeps the first qubit_ \(p\) _and transmits the second and third qubits_ \(q\) _and_ \(r\) _to Sophia and Alice_\({}_{i}\)_, respectively._This scheme effectively creates for every Alice\({}_{i}\), \(0\leq i\leq n-1\), a triplet of quantum registers, namely \((TIR_{i},SIR_{i},AIR_{i})\). These associated registers are strongly linked because their corresponding qubits are maximally entangled in the \(|GHZ_{3}\rangle\) state. This is visualized in Figure 1, which conveys this setup, whereby the qubits that belong to the same \(|GHZ_{3}\rangle\) triplet are painted in the same color.  
In this composite system, each register holds \(m\) qubits. It is important to emphasize that the spatial arrangement of the registers, whether they are co-located or dispersed across various locations, is of no consequence. The entanglement effect derived from the \(m\)\(|GHZ_{3}\rangle\) triplets will produce the required correlations, regardless of whether the composite system is confined to a single area or is fully distributed. This distinctive characteristic of entanglement enables us to conceptualize the entire setup as a cohesive system. Any practical implementation of the GHZ\({}_{3}\)MQPEC protocol will require [PERSON] to also prepare decoys. This technique is ubiquitous in the literature and is, typically, referred to as the _deoxy technique_. There are many well-written expositions that thoroughly explain the specifics; we refer to [20; 26; 36; 41; 43; 44; 69; 47], to cite just a few. The idea is that if the party responsible for the generation of the entangled triplets also produces decoys randomly chosen from one of the \(|0\rangle,|1\rangle,|+\rangle,|-\rangle\) states, it is possible to discover the presence of an eavesdropper. These decoys are randomly inserted into the transmitted sequence(s). We have not mentioned the decoys in Definition 2 to avoid clutter and make the main idea of the protocol easier to grasp.
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 4 Resolutions and Satellite Vibration
### Computation Complexity  
According to the principle of the proposed method, the received signal is first transformed into a two-dimensional matrix, whose size is \(M\times N\), and FFT is performed along the row of the matrix. Since the complexity of N-points FFT is \(\mathrm{O}(N\log_{2}N)\), the complexity of this process is \(\mathrm{O}(MN\log_{2}N)\).  
Next, for the process of compensating the phase, if the number of DM-RS symbols is K for a slot, the complexity of the least square method is \(\mathrm{O}(K)\), and the complexity of transform domain interpolation method is \(\mathrm{O}(N\log_{2}N)\). Therefore, the complexity of the process is \(\mathrm{O}(MN\log_{2}N)\).  
Then, extract the compensated signal at a particular frequency in the range domain, obtaining an M-points LFM signal in the azimuth domain. Construct a set of N matched filters, and the matched filtering process is equivalent to the convolution process, so the complexity is \(\mathrm{O}(M^{2})\), and the complexity of this process is \(\mathrm{O}(NM^{2})\).  
Finally, the computation complexity is \(\mathrm{O}(MN\log_{2}N+NM^{2})\).
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 5 Conclusions  
In this paper, we propose the LTFS to extend GHT by introducing the sign function, the two frequency integration parameters. The sign function broadens the chosen range of the order parameter into any positive numbers and leads to high-stability and high-resolution results. The two frequency integration parameters not only control the computation efficiency of LTFS, but also control its output resolution. Thus, we can flexibly choose the order parameter and two frequency integration parameters to optimize the computation efficiency and output resolution of LTFS. Synthetic 1D channel data testing, synthetic 2D seismic data, and actual 3D seismic data examples reveal that LTFS has higher resolution than GHT and can be widely used in signal processing and interpretation.  
Writing--original draft preparation, H.L.; methodology, H.L. and G.Z.; writing--review and editing, J.C. and Z.H.; software, Y.L.; data processing, K.L. All authors have read and agreed to the published version of the manuscript.  
The National Natural Science Foundation of China (Grant Nos. 41874168 and 41974160), the Sichuan Science and Technology Program for Distinguished Young Scholars (Grant No. 2019 JDJQ0053).  
Not applicable.  
Figure 7: Time slices at 1.7s with two interpreted channels: (**a**) amplitudes; (**b**) instantaneous amplitudes; (**c**) instantaneous amplitudes obtained by GHT with \(p=6\); (**d**) instantaneous amplitudes obtained by LTFS with \(p=6,f_{1}=0\) Hz, and \(f_{2}=90\) Hz; (**e**) most positive curvatures; (**f**) third-generation coherences (C3).
# Breeding Tomato Hybrids for Flavour: Comparison of GWAS Results Obtained on Lines and F1 Hybrids
## 4 Discussion  
Most of the efforts in tomato flavour have been directed to understand the genetic control of VOC accumulation in homozygous lines [20]. However, little is known on how the QTLs behave in the F1 hybrids where many of the flavour alleles could be heterozygous. To the best of our knowledge, this study is the first to address this issue by studying a GWAS panel at the homozygous and heterozygous levels, along with another expanded panel comprising additional elite cherry tomato F1 hybrids. With these three panels, we characterized the phenotypic diversity of 46 VOCs among a core collection of small fruited accessions at the line (CCI) and F1 hybrid (TCI) levels, in addition with considering the genetic resource and aromatic improvements that are already available in elite plant material (TCT). We identified numerous genomic regions that regulate key VOCs, with less than 10% overlap between line and F1 hybrid panels. We developed a strategy for F1 hybrid flavour improvement, broken down in two points: (i) Mining for novel CGs revealed in F1 panels to better understand VOC genetic control at the hybrid level. (ii) Achieving flavour improvement by suggesting candidate regions carrying overlapping associations in F1 hybrid panels for key VOCs, and consistent effect of the common tester FTMV genotype on closely related VOCs.
Despite links between SIF, GPP, and other VIs, seasonality analyses have typically used NDVI and have yielded conflicting results about the seasonal timing and patterns of NDVI compared to field GPP measurements [47; 59; 60; 61; 62; 63; 64]. A few studies have tracked seasonality with SIF from earlier instruments. [PERSON] et al. [3] showed that SIF from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument tracked the seasonality (spring onset and autumn shutoff) of photosynthesis, especially well in deciduous broadleaf forests. [PERSON], [PERSON] [8] showed that the growing season of northern high-latitude forests measured with SIF from GOME-2 and the Greenhouse gases Observing Satellite (GOSAT) was significantly shorter than the growing season measured with NDVI and that SIF tracked the changes in GPP related to temperature more closely than NDVI. Yet, comparisons of SIF from more recent, higher-resolution platforms, such as the Orbiting Carbon Observatory-2 (OCO-2), to VIs and the extraction of seasonality metrics using satellite SIF remain largely unexamined, except in tropical vegetation [65]. Such a comparison of satellite- and ground-based SIF to VIs has the potential to elucidate relationships among the metrics and determine important similarities and differences in how they capture critical changes, such as seasonal transitions in a regional DBF. The aim of this study is to compare phenological patterns of production for a temperate deciduous forest at the regional scale measured using OCO-2 and MODIS satellite-based proxies of production: solar-induced fluorescence metrics (SIF and SIF\({}_{\rm y}\)) and reflectance-based vegetation indices (GPP, NDVI, and EVI). Specifically, (1) are the average annual cycles and interannual variabilities of OCO-2 SIF and SIF\({}_{\rm y}\) and MODIS GPP, NDVI, and EVI significantly different? (2) Do productivity metrics from reflectance-based VIs and satellite SIF and SIF\({}_{\rm y}\) capture similar phenological timing? And (3) which environmental factors (canopy temperature, air temperature, and vapor pressure deficit) are most influential on productivity metrics?
## 3 Materials and Methods
### Methods
#### 3.2.4 Evaluation Metrics
The core idea behind BERTScore is to input two sentences individually into the BERT model, obtain their vector representations, and then compare these vectors. Unlike traditional similarity methods based on static word vectors, BERTScore more accurately assesses sentence similarity by incorporating contextual information and semantic features. In particular, for sentences that convey similar meanings despite differing in expression, BERTScore more effectively captures their underlying similarity. \[R_{BERT}=\frac{1}{|X|}\sum\limits_{X_{i}\in X}\max\limits_{S_{i}\in S}X_{i}^{T} S_{j} \tag{4}\]  
\[P_{BERT}=\frac{1}{|S|}\sum\limits_{S\in S}\max\limits_{X_{i}\in X}X_{i}^{T}S_{j} \tag{5}\]  
\[F_{BERT}=2\frac{P_{BERT}\times R_{BERT}}{P_{BERT}+R_{BERT}} \tag{6}\]  
where \(X\) denotes the reference text, \(X_{i}\) denotes the \(i\)-th reference text, S denotes the generated text, \(S_{j}\) denotes the \(j\)-th generated text, and \(X_{i}^{T}\) denotes the transpose matrix. FactCC is a metric designed to measure factual consistency by determining whether the generated text is consistent with the reference text. It relies solely on the reference text to verify factual alignment. The core idea behind FactCC is to input each generated sentence along with the reference text into a supervised classifier that has been trained on labeled data, indicating whether a sentence is factually consistent. By averaging the classifier'soutputs of overall generated sentences, FactCC produces an overall factual consistency score. The calculation is as follows:  
\[C_{\text{FactCC}}(S,R)=\frac{1}{|S|}\sum_{s\in S}P(C=1\mid s,R) \tag{7}\]  
where \(C_{\text{FactCC}}(S,R)\) denotes the factual consistency score for the generated text \(S\) given the reference text \(R\), and \(P(C=1\mid s,R)\) represents the classifier's probability that the generated sentence \(s\) is factually consistent with \(R\).
## 6 Q5: Future Direction
### Build Hybrid Communication Models for Future UIoT  
Based on the research study in Section 2, acoustic, optical, RF and MI are the communication technologies used in the UIoT environment. As shown in Table 2, each medium has its advantages and disadvantages. To overcome the technical challenges discussed in Section 3.2, it is necessary to port multi-medium (hybrid) communication technology in UIoT [21]. The multi-medium communication technology can improve the transmission speed, increase the battery life, and deliver reliable data transmission in UIoT.
# SDN-Based Routing for Backhauling in Ultra-Dense Networks
## 5 Problem Formulation
The optimal solution of the proposed problem is not feasible with an affordable complexity, hence, we divide the problem in two sub-problems: (i) finding the solution for the bandwidth-constrained problem of the control traffic, and (ii) finding the paths for the data flows that minimize the UTDs. Benchmark Approaches  
Before to explain the proposed approach, we shortly describe the methods considered to be benchmarks. Being the shortest-path routing protocols the most widely used, we have considered two of these algorithms. In particular, we consider the Dijkstra algorithm Dijkstra (1996) to find the path with the minimum cost in two different cases:  
* the cost of each edge is equal to 1, i.e., the routing algorithm looks for the path with the _minimum number of hops_ (MNH);
* the cost of each edge is the inverse of the edge capacity, i.e., the routing algorithm takes into account both the number of hops and the capacity of each link thus preferring links with higher capacity (HC). For each vertex of the graph, the Dijkstra algorithm [35] generates a shortest-path tree (A tree is a graph with one and only one path between every two nodes) with the given vertex as root, i.e., the node that originates the path. Two sets are maintained, one contains vertices included in the shortest-path tree and the other contains those not included. At every step the algorithm finds a vertex not yet included in the shortest-path tree that has minimum cost from the source. At the beginning only the root (i.e., the selected vertex of the graph) is included in the shortest-path tree. We suppose that first the algorithm (MNH or HC) is carried out for the control traffic with a bandwidth constraint: the selected path is the one that has minimum cost and has sufficient bandwidth to accommodate the control flow. Then the algorithm is performed again for the data traffic without any constraint.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 6 Resource Scheduling  
Recently, resource management literature has reported diverse scheduling approaches centered on NSL. Those approaches have applied techniques such as (meta)heuristics [166; 167], genetic algorithms [168; 169], and job-shop problem [170; 171] to program the execution time of elements composing a network slice. Figure 6 presents an architecture using RL and DRL to make resource scheduling decisions in a multitenant environment. In this architecture, the resource scheduling module schedules RAN, CN, and edge network resources to admitted NSLRs (after admission and allocation phases) by employing a RL/DRL agent, a RAN scheduler, an edge scheduler, and a CN sthe scheduler. The agent determines normalized weight values for each admitted NSLR belonging to a 5G/6G use case. Those values determine the scheduling priority, the time at which resources must be assigned, and the duration of each service offered by the admitted NSLR. Such values should lead to achieving a goal, for instance, meet performance requirements defined in SLA. In the raised example, the agent selects an action that, if taken, permits meeting the mentioned agreement. The agent learns to select actions that improve performance metrics included in SLA by considering the information on states and rewards from interaction with the environment by using, for example, A3C or dueling DQN. In the example, the schedulers use the values defined by the agent to program resource usage according to the priority, time and duration of slices, and resources available in the network substrate.