esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Diagrams of the spectral measure of coherence for four noise parameter time series were constructed using data from the entire network of stations and separately from data of northern, central, and southern groups of stations, as well as separately for stations allocated taking into account their remoteness from active volcanoes and separately for continental and coastal stations (Figure 1, Table 1). Using this approach, the features of the spatiotemporal distribution and coherent behavior of seismic noise statistics were found in connection with the preparation of local earthquakes with magnitudes of 7-8. Figure 1: Map of the Kamchatka Peninsula showing the location of seismic stations (Table 1), earthquake epicenters with \(M_{w}\) = 7.2–8.3, and tectonic plate boundaries: 1—seismic stations with codes indicated: blue designates coastal stations, and white designates continental stations; 2—earthquake epicenters; 3—areas of earthquake sources according to [20; 21; 22; 23; 24]; 4—boundaries of the northern, central, and southern groups of stations (from top to bottom); 5—northwestern and northeastern boundaries of the considered area of the Pacific Oceanic Plate (PA); 6—boundary of the North American continental plate (NA) with PA and small lithospheric plates Beringia (BE) and Okhotsk (OK) [25; 26]. White arrows indicate the direction of PA movement; numbers—the speed of TO movement [27]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Seismic** & **Station** & \multicolumn{3}{c}{**Coordinates**} & \multicolumn{3}{c}{**Frequency Range/**} \\
**Station** & **Code** & **N**\({}^{\circ}\) & **E**\({}^{\circ}\) & **h asl, m** & **Equipment** & **Registration** & **Relonging to Dedicated Groups of Stations** \\ \hline Avacha & AVH & 53.264 & 158.740 & 942 & CMG-6 TD & 0.033-40/100 & southern & continental & volcanic \\ \hline Bering & BKI & 55.194 & 165.984 & 12 & CMG-3 TB & 0.0083-40/100 & central & coastal & non- volcanic \\ \hline DaI\(\cdot\)niy & DAL & 53.031 & 158.754 & 57 & CMG-6 TD & 0.033-40/100 & southern & coastal & non- volcanic \\ \hline Institut & IVS & 53.066 & 158.608 & 160 & CMG-3 TB & 0.0083-40/100 & southern & coastal & non- volcanic \\ \hline Kamenskaya & KMSK & 62.467 & 166.206 & 40 & CMG-6 TD & 0.033-40/100 & northern & continental & non- volcanic \\ \hline Karymshina & KRM & 52.828 & 158.131 & 100 & CMG-3 TB & 0.033-40/100 & southern & continental & non- volcanic \\ \hline Kirisheva & KIR & 55.953 & 160.342 & 1470 & CMG-6 TD & 0.033-40/100 & central & continental & volcanic \\ \hline Klyuchi & KIY & 56.317 & 160.857 & 35 & KS2000 & 0.01-40/100 & central & continental & volcanic \\ \hline Kozyrevsk & KOZ & 56.058 & 159.872 & 60 & CMG-6 TD & 0.033-40/100 & central & continental & volcanic \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Seismic stations, their equipment [28], and the belonging of stations to selected groups.
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 5 Polarimetric Analysis
### Benefit of Polarimetry for Repeat-Pass Interferometry  
Both image couples of Tandem-X have been co-registered together, and orbital fringes have been removed between the two dates. Then, coherence matrices have been estimated. Since ambiguity height is very small, about 20 m, interferometric phase variations along range are very fast. Since a range average would mix together very different interferometric phases, coherence matrices have been estimated by an average only in the azimuth direction over 5 pixels. Optimization of coherence has then been applied according to [29]. This coherence leads to find very higher coherence levels. The resulting coherence distributions are plotted in Figure 9. Coherence are estimated using the same estimation of only five pixels. In HV and VH polarimetric channels, coherence are very slightly below what is obtained with co-polarimetric channels for coherence values higher than 0.7. The reverse of  
Figure 8: (**a**) Polarimetric image in the Pauli basis; (**b**) interferometric image on the right in the HSV color space.  
this can be seen for low coherence values, for which HV and VH lead to coherence slightly higher than HH and VV.  
However, what is clear on this figure is that optimized coherence is considerably greater than all original polarimetric channels. That means that for a given threshold on the coherence level, the corresponding numbers of selected scatterers will be greatly improved.  
Unfortunately, it is very difficult to prove that the differential interferometric phase is better estimated, due to the strong variations. It is nevertheless interesting to know which kind of polarimetric mechanism lead to the highest coherence. To this aim, we have represented as a colored composition the absolute value of the optimal polarimetric mechanism components in the Pauli basis. Thus, the interferometric coherence in HH polarization is represented in Figure 10 on the left, interferometric coherence obtained in the optimal polarization if shown on the middle; on the right, an intensity image is colored with the three component magnitude of the optimal mechanism, expressed in the Pauli basis.  
Figure 10: Impact of the coherence optimization and optimum mechanisms (**a**) Interferometric coherence level in HH; (**b**) Interferometric level in optimal basis; (**c**) Colored composition where colors code the component magnitudes of the optimal mechanism.  
Figure 9: Coherence optimization.  
In this figure, it appears that the optimal mechanisms are not necessarily those that lead to the maximum intensities. The strong mechanisms in HV and VH are often visible; Moreover, the optimal mechanisms vary greatly from one building to another. On the Mirabeau tower, the green color is predominant; On the Keller tower one face appears mostly in HH\(-\)VV and the other in HH\(+\)VV.
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
## 3 Principles of Tolerance-Aided Interference Degradation Algorithm  
This section provides an introduction to the principles underpinning the proposed TAID algorithm. It is important to note that TAID is exclusively designed for received signal processing, and thus, we adhere to the CEO-OFDM signal structure at the transmitter. To facilitate a comprehensive understanding of the principles governing TAID and to enable a thorough performance analysis, we sequentially delve into discussions related to the transmitted signal, the channel model, and the received signal model.  
Figure 2: An example of CEO-OFDM signal. Red and green symbols represent the amplitude of the positive and negative signal, respectively
## 5 Dynamic Firefighter Guiding
### Routing of Local Guiding Information  
The objective is to deliver local guiding information computed at the Base Station to each static node. The solution adopted follows a flooding approach modified to reduce the number of retransmissions. Nodes with no _children nodes_ in the CTP tree do not retransmit the flooding packets. Each node \(i\) keeps a list of its children. When it receives a data packet, it registers the sender ID in its children list. To cope with changes in the tree, there is a timeout for each node in the children list. If node \(i\) receives a data packet from node \(j\) it resets its timeout. If the timeout expires node \(j\) is deleted from its children list.  
The Base Station periodically computes updated local guiding information for the WSN nodes (\(\{LGI\}\)). Each version of updated \(\{LGI\}\) is tagged with a sequence number _SeqF_. When the Base Station has new local guiding information, it inserts it in _m GuidingInfo_ packets containing \(\{LGI_{i}\}\), _SeqF_ and the number of the packet in this flooding, see Table 1. Then, the packets are broadcasted. Each static node in the tree keeps a list of the _GuidingInfo_ packets if has received and their _SeqF_. When a node \(k\) receives the _GuidingInfo_ packet it checks if he has received that packet previously. In that case, the packet is ignored, otherwise it compares _SeqF_ of the packet with the sequence number of the last _GuidingInfo_ packet it received. If _SeqF_ is higher and the packet contains information for him (\(LGI_{k}\)), it updates its local guiding information and updates the version _SeqF_. Then, node \(k\) checks if its children list is not empty. In that case it broadcasts the _GuidingInfo_ packet, or ignores it otherwise.  
The Base Station triggers a flooding only if the new guiding information is significantly better than the information that is actually in the static nodes. The Base Station keeps track of the version _SeqF_ of the local guiding information used by each node. The _FireSense_ packets used in Section 4 include a field in which static nodes report the version of the local guiding information _SeqF_ each node is using. With this information, the Base Station can simulate the behavior of the actual escape paths. If  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pkt ID** & **SeqF** & **Pkt N\({}^{a}\)** & **Node ID** & **Next Room** &... & **Node ID** & **Next Room** \\ \hline
1 & 1 & 1 & 2 & 2 &... & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _GuidingInfo_ packet. Each column includes the field name and its size.  
the performance difference-cost-between the escape paths computed with the new guiding information and those computed using the actual information is above a threshold, the Base Station triggers a new flooding. This mechanism allows monitoring the guiding information that is actually in the WSN, minimizing the number of flooding.
###### Abstract  
The environmental conditions of wine regions determine the flavor characteristics of wine. The characterization of the chemical composition and sensory profiles of young Marselan wines from five wine-producing regions in China was investigated by gas chromatography-mass spectrometry (MS), high-performance liquid chromatography-triple-quadrupole MS/MS and descriptive analysis. Young Marselan wines can be successful discriminated based on concentrations of volatile compounds, but not phenolic compounds, by orthogonal partial least squares discriminant analysis according to regions. Compared to Jiaodong Peninsula (JDP) and Bohai Bay (BHB) regions, there were relatively lower average concentrations of varied volatiles (mainly including \(\beta\)-citronello, gerainol, and (\(E\))-\(\beta\)-damaseneo) and several fermentation aroma compounds (including isoamyl acetate, octanoic acid, decanoic acid, ethyl decanoate, etc.) but higher levels of acetic acid in Xinjiang (XJ), Loess Plateau (LP), and Huaizhuo Basin (HZB) regions, which were related to their characteristic environmental conditions. Marselan wines from HZB, LP, and XJ regions were characterized by lower \(L\) values and higher \(a\) and \(C_{\text{ab}}\) values. Marselan wines from XJ were discriminated from the wines from other regions due to their higher concentrations of several flavonoids. Sensory analysis indicated that Marselan wines from HZB region were characterized by relatively low intensities of floral and fruity aromas compared to other regions.  
M 202211787. [[https://doi.org/10.3390/foods11060787](https://doi.org/10.3390/foods11060787)]([https://doi.org/10.3390/foods11060787](https://doi.org/10.3390/foods11060787))  
1
Footnote 1: [[https://www.mdpi.com/journal/foods](https://www.mdpi.com/journal/foods)]([https://www.mdpi.com/journal/foods](https://www.mdpi.com/journal/foods))  
**Keywords:** Marselan; geographical origin; GC-MS; HPLC-QqQ-MS/MS; sensory
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 4 Common Attacks within MANETs
### Flood Attacks  
Flooding attacks aim to disrupt network operations by overwhelming it with a large volume of packets, essentially a denial-of-service (DoS) attack. These attacks come in various forms, each targeting a specific network protocol or service [38]:  
* Hello flood: Targets the Hello messages used by nodes to discover neighbors, exhausting bandwidth and node resources.
* RREQ flood: Spams the network with route request messages used in reactive routing protocols, overloading nodes and hindering legitimate route discovery.
* Data flood: Floods the network with irrelevant data packets, consuming bandwidth and hindering legitimate data transmission.
* ICMP/UDP flood: Targets specific protocols like ICMP (ping) or UDP to overwhelm resources.
## 2 Background  
### Collaborative Work Environments  
A collaborative work environment is referred to in this study as a common physical room with the optimal conditions to develop the project, where a group of various professionals work simultaneously in order to allow direct and synchronous interactions between them [18; 19]. In this environment, there are two communication networks: the human network that is based on the interactions between the individuals; and the electronic network that computers use to exchange information. While virtual collaboration experiences based on cloud systems do exist and are more frequent in the development of BIM projects, physical interaction between the different agents makes it possible to increase the efficiency of the design process and results in quicker problem-solving and decision-making.  
The organization of a collaborative work environment is a complex process in which diverse participants must adapt their behavior to the peculiarities of this new methodology, selectively monitor information, and continuously give feedback in order to solve problems, improve project design, and finally, decrease the associated costs. In these work environments, work is usually organized into one to three sessions per week; teams consist of a leader and engineers specialized in design sub-areas; each designer is allowed to work on their design and move freely around the room in search of the information needed to solve their problems; and when an difficulty pertains to the whole team, the session is stopped, and the entire group focuses on resolving the problem [21].  
Due to its great success, in recent years, the AEC industry has adapted and modified this methodology. In this case, the sessions are divided into cycles, which should not last more than two weeks depending on the phase of the project [21]. The work teams are comprised of a leader, designers, builders, the client, and the suppliers, with some or all of the parties participating, depending on which are necessary for each cycle. Despite high interest in the use of this methodology with BIM projects, there is limited evidence regarding the behavior and performance of BIM users in these work environments.
## 1 Introduction
Furthermore, previous studies have mostly focused on short-term [44] and site-scale [43; 45] LUE and GPP. The CL described as the fraction of solar incident radiation on the surface of the earth to the extraterrestrial radiation at the top of the atmosphere [35; 46], is an effective way to explain the circumstances of diffuse and direct light. This index prompts our motivation to explore the influences of the CI on long-term and global LUE and GPP. Machine learning methods provide a powerful tool to upscale site-observed fluxes to a larger scale with satellite-derived parameters and other explanatory variables [37]. The application of theCubist regression tree approach in vegetation carbon uptake research is a good example. The main advantage of the Cubist method is to add multiple training committees and \"reinforcement\", so as to make the weights more balanced [47]. It also provides linear equations for prediction instead of black box. In addition, Cubist is a commercial, proprietary product and has the least algorithmic documentation [48]. [PERSON] et al. prove the better performances than random forest in estimating leaf area index [49]. Previous studies use this approach to produce LUE, GPP and spatially continuous GPP in the USA [50; 51; 52; 53], and these studies gain satisfactory validation results against site-scale measured GPP. However, few studies compare this approach with the parameterization approach in terms of global LUE and GPP. The purpose of this paper is to compare and analyze the performances of different approaches for producing global and seasonal LUE and GPP. The three methods for determining LUE were based on (1) a parameterization approach without the CI, (2) a parameterization approach with the CI, and 3) the Cubist regression tree approach. We collected eddy-covariance and meteorological data from 180 FLUXNET sites as calibration and validation data and GLASS products and ERA-interim data as input data to estimate global LUE and GPP in 2014, with a spatial resolution of 5 km and a temporal resolution of 8 days. FLUXNET measurements were used to validate LUE and GPP using three approaches at the site scale. Furthermore, we compared the temporal and spatial patterns of the LUE and GPP to analyze and assess the strengths and drawbacks of each approach. It is worth noting that global LUE distributions were first presented in this study to explore the spatial variations in LUE in different areas and the temporal changes in LUE in different seasons.
## 6 Numerical Example  
### Description of Example  
This study takes the extended bridge network in Figure 2 as an example and applies it to logistics distribution from north to south in Taiwan. Taipei, Hsinchu, Taoyuan, Taichung, Chiayi, Tainan, and Kaohsiung are seven major cities with large cargo, flows from north to south in Taiwan; there are many companies, industrial manufacturers, warehouses, and ports located in these seven cities. The locations of these seven cities are shown in Figure 3[41].  
The types of roads connecting these seven cities are divided mainly into two types, i.e., highways (\(\text{Rd}_{\text{h}}\)) and slow roads (\(\text{Rd}_{\text{g}}\)). Therefore, from north to south in Taiwan, trucks can choose to drive on \(\text{Rd}_{\text{h}}\) or \(\text{Rd}_{\text{g}}\). The delivery of goods starts at the northernmost source point of Taipei, passes through Taoyuan, Hsinchu, and Taichung and then through Taichung via Chiayi and Tainan, and finally delivers the goods to the southernmost destination, Kaohsiung.  
From the cargo transportation routes of the aforementioned seven major cities in Taiwan, the extended bridge network in Figure 2 can be applied to the logistics distribution from north to south in Taiwan. The nodes s and t stand for the source node, i.e., Taipei, and the sink node, i.e., Kaohsiung, respectively. The intermediate nodes a through e stand for Taoyuan, Hsinchu, Taichung, Chiayi, and Tainan, respectively. Hence, the complete directed MFN is shown in Figure 4.  
Figure 3: Taiwan map.  
The plane coordinates of the seven cities in Taiwan are marked as Taipei (s): (4,6), Taoyuan (a): (3,5), Hsinchu (b): (2,4), Taichung (c): (2,3), Chiayi (d): (1,2), Tainan (e): (0,1), and Kaohsiung (t): (0,0), respectively, as shown in Figure 5.  
Each edge in the MFN of Figure 4 is numbered as shown in Figure 6, in which the edge via Taichung (node c) is bidirectional, containing \(\mathrm{e_{5}}\) and \(\mathrm{e_{6}}\), while the other edges of other cities are unidirectional.  
As described in Section 3, the types of roads in Taiwan's seven major cities, from north to south, include slow road (\(\mathrm{Rd_{g}}\)) and highway (\(\mathrm{Rd_{h}}\)) options. Assume that the load weight limit for a vehicle driving on \(\mathrm{Rd_{h}}\) is two (unit weight). Although there is no load weight limit of a vehicle driving on \(\mathrm{Rd_{g}}\), it is assumed that the truck weight limit is three (unit weight) no matter the vehicle driving on \(\mathrm{Rd_{h}}\) or \(\mathrm{Rd_{g}}\). The probability of the loading capacity of the two road types for each edge of Figure 6 is given in Table 2.  
Figure 4: Directed MFN of goods shipped from northern Taiwan to the south.  
Figure 5: Coordinates of seven cities in Taiwan.  
Figure 6: Edge marked in the network.
# Virtualized MME Design for IoT Support in 5G Systems
## 4 Traffic Models
### Mobile Broadband Traffic Model  
We adopt the compound data traffic and user behavior model proposed in [32] for MBB UEs. This traffic model considers three types of applications, namely:  
1. Web browsing.
2. HTTP progressive video (e.g., YouTube).
3. Video calling (e.g., Skype service).  
which are redesigned to generate the data rates predicted for future mobile networks in the METIS project [33]. Then, the mean data rates per user used are higher than the current demand. For web browsing, the mean data rate per user depends on the number of web pages visited, the main object size of each web and the number of embedded objects and their sizes. For HTTP progressive video, the mean data rate per user depends on the number of downloaded video clips, the size of each video and the video encoding rate. While for video calling, the mean data rate per user depends on the constant bit rate of the call. Consequently, from [32], the mean data rates per user obtained are 233.28 kbps, 5.25 Mbps and 142.07 kbps for web browsing, HTTP progressive video and video calling, respectively.  
Most of the signaling workload generated by UEs depends on their activity and their traffic characteristics [32]. Here, we only consider those LTE control procedures that generate the most signaling load on the MME [10]. In particular, we consider Service Request (SR), Service Release (SRR) and X2-based Handover (HO) procedures. According to [32], for each of these procedures, the MME respectively will process 3, 3 and 2 control messages.  
Let \(N_{MBB}\) be the number of MBB UEs and \(\overline{\lambda}_{SR}\), \(\overline{\lambda}_{SRR}\) and \(\overline{\lambda}_{HO}\) be the mean generation rate per UE for the SR, SRR and HO control procedures, respectively. Let \(\overline{\lambda}_{MBB}\) be defined as the mean arrival rate of control messages processed by the MME that are generated by MBB UEs. Then, it can be computed as:  
\[\overline{\lambda}_{MBB}=N_{MBB}\cdot(3\cdot\overline{\lambda}_{SR}+3\cdot \overline{\lambda}_{SRR}+2\cdot\overline{\lambda}_{HO}) \tag{2}\]
## 3 Research Methodology
### Using the CFCS Method for the Transformation of Crisp Values  
The DEMATEL method produces a total influence relationship matrix expressed as grey number intervals. To determine clear causal relationships between factors, these intervals must be converted into crisp values. The following section details the CFCS (Converting Fuzzy Data into Crisp Scores) aggregation method formulas and steps, as developed by [PERSON] et al. [57].  
* Step 1. Normalization of the grey number.  
Grey numbers are normalized to establish uniform scaling across all values. The normalization process is applied separately to both the lower and upper bounds of each grey number, following Equations (12) and (13):  
\[\underline{S}_{ij}^{\prime}=\left(\underline{S}_{ij}-\min\underline{S}_{ij} \right)/\Delta_{\min}^{\max}\text{ for }\;i,j=1,2,\cdots,n \tag{12}\]  
\[\overline{S}_{ij}^{\prime}=\left(\overline{S}_{ij}-\min\underline{S}_{ij} \right)/\Delta_{\min}^{\max}\text{ for }\;i,j=1,2,\cdots,n \tag{13}\]  
where  
\[\Delta_{\min}^{\max}=\max_{j}\overline{S}_{ij}-\min_{j}\underline{S}_{ij} \tag{14}\]
* Step 2. Calculate the total normalized crisp value. Equation (15) is applied to calculate the total normalized crisp value (N\({}_{ij}\)): \[N_{ij}=\frac{\underline{S}_{ij}^{\prime}(1-\underline{S}_{ij}^{\prime})+ \overline{S}_{ij}^{\prime}\ast\overline{S}_{ij}^{\prime}}{1-\underline{S}_{ ij}^{\prime}+\overline{S}_{ij}^{\prime}}\] (15)
* Step 3. Derive the crisp value. Equation (16) is applied to obtain the final crisp value for the matrix: \[X_{ij}=\min_{j}\underline{S}_{ij}+N_{ij}\Delta_{\min}^{\max}\] (16)  
Each matrix element is converted from a grey number range to a single crisp value by combining the normalized value (Nij) with the range boundaries.