esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 7 Digital Server
### Server Virtualization  
Servers are required to be operational at very high speed for long periods of time without interruption; servers shall be high availability and high reliability. Servers can be virtualized via sharing hardware and software resources with other virtual servers to allow a more efficient use, reduce cost while increasing reliability. Virtual Servers are normally hosted on modular blade servers managed by hypervisors that abstract the virtual server from its physical hardware and software.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technology** & **Frequency** & **Bandwidth** & **Data Rate** & **Access** & **Cell** \\ \hline GSM (2G) & 1800 MHz & 200 kHz & 270.833 kbit/s & TDMA & 7 km \\ UMTS (3G) & 1900 MHz & 5 MHz & 42 Mbit/s & CDMA & 6 km \\  & 2100 MHz & & & \\ LTE/WIMAX (4G) & 2100 MHz & 20 MHz & 299.6 Mbit/s & OFDMA & 2 km \\  & 2600 MHz & & & \\
5G & \textless{}6 GHz & 100 MHz & & & \\  & 24–86 GHz & 400 MHz & 20 Gbit/s & NOMA & 1 km \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mobile networks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Band** & **Frequency** & **Use** \\ \hline L & 1–2 GHz & GPS, satellite mobile phones and radio \\ S & 2–4 GHz & Weather radar, surface ship radar \\ C & 4–8 GHz & Satellite TV networks \\ X & 8–12 GHz & Military radar, Weather monitoring, \\  & 12–18 & Satellite Communications \\  & GHz & Direct Broadcast Satellite Services \\ Ka & 26–40 & Satellite Communications \\  & GHz & High Resolution close range Radars \\ \hline \hline \end{tabular}
\end{table}
Table 8: Satellite communications.
# Educational Organization's Security Level Estimation Model
## 4 Validation of Proposed Model
### Description of Analyzed Education Organizations  
Five examples of educational institutions were selected from Lithuania to generate five different alternatives for the model validation. The summary of alternatives data is presented in Appendix A, Table A2. The organizations were selected randomly by finding persons who work in the organization as information technology or security specialist or provides information technology infrastructure management services and can share needed data on the organization. Those persons were asked to fill in the organization definition form where all 49 criteria were listed. The short profiles of the alternatives are the following:  
* Alternative A1--higher education institution, technical university, which main security management concentration is on security policy and process formalization, most of IT infrastructure is owned, the university does not store or provide access to students under 16-years old or other persons, related to students.
* Alternative A2--secondary school, where policies and security processes are mostly in initial maturity level, has no internal management systems, IT management is mixed between owned devices and managed or used other infrastructure, therefore, has no IT security management positions, all students are under 16 years all.
* Alternative A3--the special education school for students, which is very similar to alternative A2, however, has a higher level of security policy and process maturity level, IT security management position, and a broader range of personal data and system access as students under 16 years old are its main customers.
* Alternative A4--college, where security policy and processes are in the same maturity level as in alternative A3, however, this institution has no IT security management position and provides security training and help-desk services for its teaching staff.
* Alternative A5--higher education institution, technical university, where students under 16 years old are attending, therefore, their personal data and access to all needed system are assured, all IT infrastructure is owned by the university.  
Each of the alternatives represents real examples; however, they are anonymized in this paper. Meanwhile, experts had no description for any of the alternatives to avoid some pre-judgment.
# Spatiotemporal Image Fusion in Remote Sensing
## 3 Synthesis: Challenges and Opportunities
### Future Directions  
Image fusion methods need to be flexible enough to accomplish different fusion tasks under different environmental conditions [55; 104; 105]. While most of the presented spatiotemporal image fusion methods are capable enough to capture reflectance changes which are caused by vegetation dynamics through time, i.e., vegetation phenology, not all of them account for sudden land cover changes (e.g., deforestation, flooding events) that might occur during investigated observational time [47]. More efforts are, therefore, required to capture accurate temporal changes when fusing high spatial resolution and high frequent temporal coverage from different remotely sensed data [43; 51]. By considering temporal changes, spatiotemporal fusion methods can be successfully used for mapping and monitoring applications in areas with rapid land cover changes [41; 55].  
Given the increasing number of new sensors which acquire images at fine spatial and spectral resolution, we consider that spatiotemporal image fusion methods should be extended to fuse multi-sensor images, i.e., more than two sensor types data over a specific observation period. Indeed, this is a challenging task given the diversity of images to be fused in terms of spectral, spatial, temporal and radiometric resolution. In this context, Dynamic Time Warping could offer a flexible framework for performing spatiotemporal fusion of different images accurately. This is due to the ability of this method to predict the surface reflectance pixel values by accounting for the phenological changes of vegetation and by the presence of clouds which contaminate the pixels values.  
Another important issue to be considered in the future is the computational time. As an increasing amount of data to be fused is available, the fusion methods have to be capable to scale up to regional and global scales [46]. They can be included into operational applications for various image fusion tasks by making use of advanced cloud computing technologies such as Google Earth Engine [134]. This will allow almost near-real time fusion of images, a task that is of paramount importance for disaster management activities.
# Food Processing with UHP Waterjets
## 4 Selected Cutting Applications
### Vegetables
#### 4.4.1 Lettuce and Celery Harvesting and Trimming  
[PERSON] [19] and [PERSON] [20] were perhaps the first to study using waterjets for food cutting, especially lettuce harvesting. This was inspired by using waterjets for debarking and the need for an improved lettuce harvesting method. They found that an orifice-type nozzle is better than cone-type nozzles and conducted lab tests to simulate harvesting conditions. They observed that cleanness of cut is inversely related to increased jet diffusion, i.e., jet coherency is important. They calculated that at a forward speed of about 5 km/h, 15.3 cu m of lettuce would be harvested per hour and thus waterjet harvesting is highly viable, and that power and water requirements are feasible for field harvesting systems. [PERSON] et al. [21] compared waterjet and blade cutting of lettuce and concluded that better salad-cut romaine quality was obtained with a waterjet using a food-grade nozzle. While both the blade and waterjet caused cell damage, the initial bacterial counts were lower with waterjet cutting. Figure 20 shows a comparison of cut lettuce discoloration with a waterjet and a blade. [PERSON] et al. [22] showed that because the internal liquid of injured cells is removed by the water flow, browning of the waterjet-shredded lettuce is markedly reduced compared to any commercial cutting techniques.  
Figure 19: Cutting chicken tendons with air-waterjet.  
Figure 20: Waterjet-cut versus blade-cut lettuce.  
Recently, Taylor Farms (Salinas, CA, USA) started using waterjet automated romaine lettuce harvesting with great ergonomic benefits to the operators, productivity, yield, and cut quality for enhanced shelf life. For example, an automated harvester by Ramsey Highlander, as studied by [PERSON] and [PERSON] [23], and shown in Figure 21, uses several side-firing jets powered by a KMT high-pressure pump to cut six heads of lettuce at the same time and reduces labor by 30 workers.  
Figure 21: Lettuce harvester using waterjets.  
Duda Farms in Florida ship about 1 million kg of fresh-cut celery every week, due to the ease of cutting and increased shelf life that are similar to lettuce. [PERSON] and [PERSON] [24; 25] reported on the effect of using a 0.18 mm waterjet at 207 MPa on prolonging the shelf life of fresh pre-cut celery and other vegetables like carrots. The results for celery cutting were compared to other methods shown in Table 2 below. It was concluded that waterjet slicing minimizes bruising and tissue damage which results in increased shelf life for the fruits and vegetables they tested.
# A Monopole UWB Antenna for WIFI 7/Bluetooth and Satellite Communication
## 5 Measurement and Discussion  
### S-parameter Results  
The fabricated prototype is shown in Figure 7, and the test environment for the antenna is shown in Figure 7b-c. The S-parameters were measured by AV3629D vector network analyzer.  
Figure 6: Current distribution at resonance point: (**a**) 2.4 GHz, (**b**) 5.1 GHz, (**c**) 6 GHz, (**d**) 10 GHz, (**e**) 13.2 GHz.  
In Figure 8, the actual measurement results show that the fabricated antenna can cover 2.12-2.55 GHz, 4.67-5.38 GHz, and 5.7-14.56 GHz bands. Notably, the small differences between the simulated and measured results are mainly due to the fabrication and welding errors, and in general, the results are very consistent. It is worthwhile to note that the proposed antenna can effectively cover newly released WiFi 7 standard, Bluetooth, 4G/5G bands, and C/X/Ku-bands.  
Figure 8: Simulated and measured S\(-\)Parameters.  
Figure 7: Fabrication and measurement: (**a**) front and back view of the proposed antenna; (**b**,**c**) far-field measurement in an anechoic chamber.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 3 Materials and Methods
### Formulating Factors Affecting Data Collection
Single-hop delay is involved in distributed SDVN architecture. Multi-hop delay is involved in both hybrid and centralized SDVN architectures. 5. **Cost associated with the communication channel (\(\mathcal{C}\))--** The communication channel used for data communication will depend on the SDVN architecture and the components involved in the communication. There are three candidates for the communication channel, namely: cellular, DSRC, and wired. Regardless of the architecture, we assume that data plane communication between Infrastructure and Infrastructure (I2I) (between RSUs, RSU, and control server, evolved node base station and control server, etc.) always uses wired communication [42]. Furthermore, regardless of architecture, V2V communication always uses the DSRC communication channel. However, V2I, and Infrastructure to Vehicle (I2V) communication will depend on the SDVN architecture. For example, in centralized and hybrid SDVN architectures, the control server is located at a remote place so each wireless node (OBU) has to use cellular communication to send data to the control server. However, in the distributed SDVN architecture, there is no cellular communication for V2I or I2V, and the local agent is responsible for collecting data for knowledge generation by exchanging data between the nodes using DSRC communication only. Here, we assume the communication cost as follows. We associated the least cost with the wired communication, a medium cost with the DSRC, and the highest communication cost with the cellular communication [43]. 6. **Total overhead**--If each node of the vehicular network transmits data to the centralized server, each packet will have to be encapsulated with a header and transmitted. Alternatively, a data collecting agent can collect the broadcast packets received from its neighbors and transmit them together along with the agent's data by encapsulating them with a header. The overhead will be less in the second scenario than when each node transmits data on its own to the central server. Therefore, when selecting agents, priority must be given to the nodes that contain a higher number of neighbors.
## 3 Results and Discussion
### Gel Permeation Chromatography  
For the extracted lignin fractions, molecular weight analysis and chromatograms employing the GPC method were performed to more precisely understand their fragmentation during GVL fractionation. The results for this analysis are shown in Table 3, along with the weight average (M\({}_{\text{w}}\)) and number average (M\({}_{\text{n}}\)) molecular weights as well as the corresponding polydispersity (M\({}_{\text{w}}\)/M\({}_{\text{n}}\)).  
As it is quite obvious in the table, the molecular weights ranged from 12,331 to 7245 gm/mol, which is relatively high compared to a classical lignin, i.e., milled wood lignin (MWL). In one previous study where GVL was employed as a pretreatment reagent, lignin also exhibited heterogeneous and diverse properties due to the harsher process conditions [26]. From here, it is presumable that the lignin underwent condensation during this GVL treatment. In regards to polydispersity, as can be seen from the table, the value is in the range of 2.54-2.96 and is representative of a narrow margin, which shows a relative heterogeneity of lignin macromolecules. However, this high value of polydispersity shows that there are large amounts of low molecular weight species present from the isolated lignin fractions. This same phenomenon is also supported by the behavior of the chromatograms, which is illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Mn** & **Mw** & **(Mw/Mn) P** \\ \hline Pine MWL & 5157 & 10660 & 2.06 \\ \hline GVL Lignin 140:2 & 4164 & 12331 & 2.96 \\ \hline GVL Lignin 140:4 & 4942 & 11913 & 2.41 \\ \hline GVL Lignin 160:2 & 4575 & 13162 & 2.87 \\ \hline GVL Lignin 160:4 & 3897 & 10165 & 2.60 \\ \hline GVL Lignin 180:2 & 3279 & 7939 & 2.42 \\ \hline GVL Lignin 180:4 & 2847 & 7245 & 2.54 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of molecular weights and polydispersity.  
Figure 3: Gel permeation chromatography (GPC) chromatograms.
## 3 Mathematical Formulation of LIRP with EPQ Model
The optimal value of \(Q_{i}\) can be obtained by deriving the first derivative of the cost function in Equation (16) with respect to \(Q_{i}\) as follows:  
TCI = Production setup cost + Inventory holding cost  
\[TCI = KC\sum_{i\in I}\sum_{j\in J}\left[\frac{\big{(}D_{j}-r_{j}+s_{j} \big{)}y_{ij}}{Q_{i}}\right]+\frac{h}{2P}\sum_{i\in I}\left[Q_{i}\bigg{(}P- \sum_{j\in J}\big{(}D_{j}+r_{j}+s_{j}\big{)}y_{ij}\bigg{)}\right]. \tag{16}\]  
\[\frac{\partial TCI}{\partial Q_{i}} = -KC\sum_{i\in I}\sum_{j\in J}\left[\frac{\big{(}D_{j}-r_{j}+s_{j} \big{)}y_{ij}}{Q_{i}^{2}}\right]+\frac{h}{2P}\sum_{i\in I}\left[\bigg{(}P- \sum_{j\in J}\big{(}D_{j}+r_{j}+s_{j}\big{)}y_{ij}\bigg{)}\right]. \tag{17}\]  
Let \(\frac{\partial TCI}{\partial Q_{i}}=\)0, hence  
\[-KC\sum_{i\in I}\sum_{j\in J}\left[\frac{\big{(}D_{j}-r_{j}+s_{j} \big{)}y_{ij}}{Q_{i}^{2}}\right]+\frac{h}{2P}\sum_{i\in I}\left[\bigg{(}P- \sum_{j\in J}\big{(}D_{j}+r_{j}+s_{j}\big{)}y_{ij}\bigg{)}\right]\;=\;0\;. \tag{18}\]  
\[Q_{i} = \sqrt{\frac{2P(KC)\sum_{i\in I}\sum_{j\in J}\big{(}D_{j}-r_{j}+s_{ j}\big{)}y_{ij}}{h\sum_{i\in I}\big{(}P-\sum_{j\in J}\big{(}D_{j}+r_{j}+s_{j} \big{)}y_{ij}\big{)}}}\;. \tag{19}\]  
The cost function of TCI is a convex function where the second derivative of the function is always non-negative (Equation (20)). \[\frac{\partial^{2}TCI}{\partial Q_{i}^{2}} = KC\sum_{i\in I}\sum_{j\in J}\left[\frac{\big{(}D_{j}-r_{j}+s_{j} \big{)}y_{ij}}{Q_{i}^{3}}\right]\geq 0\;. \tag{20}\]  
From the objective function in Equation (1), we determine three decision planning as follows:  
1. the number of open depots and assigned customers at each open depot. 2. the routing of vehicles delivering demands from the depot to the customers. 3. the optimal number of production quantity of each open depot. We solve the problem iteratively where the allocation of customers to each depot has been assigned at the first stage with the possible number of open depots. At this stage, the vehicle capacity limit constraint has been relaxed. At the second stage, the process of minimizing location and routing with considering vehicle capacity constraint is done. To obtain the cost of inventory as stated in the objective function, the \(Q_{i}\) is calculated by the Equation (19) where the formulation is depending on the decision variable of \(y_{ij}\) and the total demand and returned from each customer at each depot. In other words, the inventory cost will be calculated when the decision variable for location and routing are found throughout the process of improvisation at each iteration. The process of minimizing the overall cost depends on the total operating depots, delivery routing, and inventory planning.
# A Petri Net-Based Algorithm for Solving the One-Dimensional Cutting Stock Problem
###### Abstract  
This paper addresses the one-dimensional cutting stock problem, focusing on minimizing total stock usage. Most procedures that deal with this problem reside on linear programming methods, heuristics, metaheuristics, and hybridizations. These methods face drawbacks like handling only low-complexity instances or requiring extensive parameter tuning. To address these limitations we develop a Petri-net model to construct cutting patterns. Using the filtered beam search algorithm, the reachability tree of the Petri net is constructed level by level from its root node to find the best solution, pruning the nodes that worsen the solution as the search progresses through the tree. Our algorithm is compared with the Least Lost Algorithm and the Generate and Solve algorithm over five datasets of instances. These algorithms share some characteristics with ours and have proven to be effective and efficient. Experimental results demonstrate that our algorithm effectively finds optimal and near-optimalsolutions for both low and high-complexity instances. These findings confirm that Petri nets are suitable for modeling and solving the one-dimensional cutting stock problem.  
cutting problem; packing problem; Petri net; reachability tree; beam search Article
## 3 Predict and Forward Scheme Based on Node Profile
### Prelearn Message: Definition and Application  
Opportunistic network belongs to multi-hop wireless network, where the source node communicates with the destination in the poor network state of high latency, low delivery ratio and intermittent connection. Destination addresses are not always available by the message applications in OppNet. In order to overcome this strong limitation, the profile-based network model proposed that data transmission is based on node attributes instead of destination addresses, which may emerge as another problem in the data transmission process: there is no efficient delivery strategy to characterize nodes based on their attribute characteristics. Therefore, we proposed Prelearn, a novel model that allows messages to be sent to the destination profile identified by node attributes, such as their physical characteristics, places of residence, workplaces or hobbies.  
Figure 1: The description diagram of node profile.  
In order to find the nodes belonging to the destination profile, we firstly define S as the set of all nodes in an OppNet. Therefore, the set of all nodes \(S\) can be shown as  
\[S=\{node_{1},node_{2},node_{3}.......node_{n}\}(S\
eq\varnothing) \tag{1}\]  
where the label \(n\) represents the number of nodes in the networks. Similarly, we define the destination profile \(R\) as a set of the destination users, and the destination profile \(R\) can be shown as  
\[R=\{node_{1},node_{2},node_{3}.......node_{i}\} \tag{2}\]  
where the nodes \(node_{1}\),\(node_{2}\),\(node_{3}\).......\(node_{i}\) belonging to the destination profile \(R\), are a group of the destination users. Obviously, the destination profile \(R\) is a subset of \(S\), so \(R\subseteq S\). For a given attribute certification \(C_{(c_{1},c_{2},c_{3}.......c_{m})}\) defined by the source node, \(c_{1}\),\(c_{2}\),\(c_{3}\).......\(c_{m}\) are different attribute requirements for the destination profile. Consequently, the message delivery function \(pf\) is a mathematical mapping from the set \(S\) to the destination profile \(R\), and it can be shown as  
\[pf:S\times C_{(c_{1},c_{2},c_{3}.......c_{m})}\longrightarrow R\]  
For different application scenarios in opportunistic networks, various message applications may have different attribute requirements for the destination profile. Therefore, the optimal transmission process is achieved by defining different message delivery functions, such as maximum, minimum or the local optimum. Moreover, for a source node \(SOURCE\), a message delivery function \(pf\), a probability prediction matrix \(W\overset{*}{n\times n}\) that contains the probability of the future meeting between nodes in the network, and the data packets \(DATA\), we rigorously define the Prelearn message as \(L(SOURCE\_DATA\),\(W\overset{*}{n\times n}\),\(pf\) ), where the probability prediction matrix \(W\overset{*}{n\times n}\) will be detailed in the following sections.
## 3 Electromagnetic Simulation
### Q3D Extractor Theory  
Q3D Extractor solver considers the quasi-static approximations for Maxwell's equations. It can be assumed that when one of the first-order time derivatives of electric and magnetic induction fields (but not both) is different from zero, the other one is negligible. The main consequence of this technique is that electromagnetic signals cannot propagate and the scalar fields at time \(t\) in a certain point \(P\) are only dependent on the electromagnetic sources' values at time \(t\). This formulation can be used if it is established that the system length is much smaller in comparison to the wavelength associated with the maximum operating switching frequency of the power device. A general indication is that the structure dimension should be less than \(1/10\) of a wavelength that is inversely proportional to frequency. Considering that the simulations are carried out at a frequency \(f\) equal to 500 kHz and assuming \(c\) equal to the speed of the light in vacuum, the wavelength \(\lambda=\frac{c}{t}\cong 0.6\) km is higher (much more than 10 times) than the maximum package dimension (approximately 10 mm), confirming the validity of the quasi-static hypothesis.  
Moreover, the Q3D extractor solver distinguishes between \"DC\" and \"AC\" analysis [61]. In the case of DC analysis, both resistance and inductance are quite constant with the frequency. Quasi-static electric solver is employed for DC calculation. When AC analysis is considered, inductance decreases in comparison to DC value remaining not dependent by frequency. On the contrary, resistance increases with the root square of frequency and the quasi-static magnetic solver is considered. The physical reason is the eventual occurrence of the skin effect that reduces the effective cross-section for current flow, resulting in increased resistance and decreased magnetic field. A remarkable difference between DC and AC analysis is relative to the mesh. In the case of DC analysis, a three-dimensional tetrahedral mesh is needed to account for the current distribution for the DC case, meanwhile, a two-dimensional triangular mesh is considered for the AC problem to model the whole current distributed on the conductor skin. Q3D software automatically chooses between DC and AC analysis. Fixing the frequency, if the conductor thickness is enough large (i.e., three times the skin depth) AC analysis should be carried out. Otherwise, if conductor thickness is lower than the skin depth, as in the specific case study, DC analysis results are most appropriate [61].