esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
The scope of this research considers manufacturing organizations, with their supply chains, production plants, or groups of machines, that need to perform planning based on a forecast due to the lack, or incompleteness, of customer orders in relation to the available capacity for the planning period in consideration. Moreover, for the design, implementation, and control of supply chain system policies with the goal of robustness [39], system dynamics was selected. Vensim was chosen as the software, as this software enables system dynamics modeling and the simulation of complex and dynamic models with integrated decision-making. In addition, the tools used to carry out the demand data series analysis and demand pattern detection included the following:  
* R Studio was used as a programming tool.
* The forecast, zoo, series, and changepoint packages were used to evaluate and adjust the series and predictions and obtain error measurements. The novelty of the developed S&OP model is the following combination:
* Statistical treatment of potential future demand scenarios;
* Demand pattern state identification and demand planning, as well as adjusting existing demand patterns based on the deviation between real and expected data series;
* A forecasting method based on random number generation using historic data series of customer demand;
* Predictive sales and operations planning used to define the potential measures to increase efficiency based on the prediction of system's behavior and thus prevent inefficiency to avoid corrective measures;
* The integration of strategic, tactical, and operational measures, activities, and indicators;
* Applying system dynamics as a useful tool for analyzing responses to what-if scenarios within a supply chain. As a result, the proposed methodology was built based on combinations of elements. Later, a simulation was implemented and validated in Vensim. Finally, demand scenarios are used to test the hypothesis with a comparison of the predictive S&OP and classical simulation model. To achieve the objectives, the following activities will be carried out:
* A description of the current challenges of sales and operations planning;
* Designing a generic predictive methodology to optimize sales and operations planning oriented toward the customer service level based on the related decisions and actions needed to increase efficiency;
* The development of a simulation model that applies system dynamics to evaluate the impact of the developed predictive approach;
* Comparison of a predictive approach versus a classical approach.
## 3 Experiments and Results
### Generalisation Testing  
While being satisfied by the performance obtained for both SOTA-based models and custom-made NNs, we deepen their analysis, focusing on their generalisation abilities. In particular, the best performing NNs were selected and tested over few novel images of dishwashing loads, which do not belong to the test set. The images are assumed to be different from images available in the dataset, while still representing dishwashing loads. Due to time consumption issues, the number of online images available are far less than the number of images belonging to the test set. However, although being limited in size, such preliminary generalisation test is meaningful as it shows whether trained NNs can be deployed safely in real-world scenarios.  
Table 7 shows the generalisation capabilities of NNs, comparing SOTA-based and custom-made architectures. Results highlight how SOTA-based architectures generally bear higher generalisation capacity. This behaviour is understandable, and probably due to the SOTA-based model pretraining. SOTA-based models are built on a backbone--i.e., the SOTA modules--which is pre-trained on ImageNet. Therefore, those models start their learning procedure from a general knowledge, allowing them to better generalise concepts.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Memory Footprint** & **Accuracy** & **Inference Time** \\ \hline
1 \(\times\) SimpleNet & 1.54 MB & 97.57\% & 6.02 s \\
0.5 \(\times\) SimpleNet & 0.56 MB & 95.49\% & 0.99 s \\
1 \(\times\) XSimpleNet & 0.38 MB & 92.60\% & 1.84 s \\
0.5 \(\times\) XSimpleNet & 0.10 MB & 92.25\% & 0.33 s \\
1 \(\times\) Light-XSimpleNet & 0.06 MB & 92.60\% & 0.33 s \\
0.5 \(\times\) Light-XSimpleNet & 0.02 MB & 89.36\% & 0.078 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Custom models performance when deployed on the OpenMV microcontroller. The 1\(\times\) or 0.5\(\times\) represent the rescaling factor of images.
# Stimuli-Responsive Photonic Crystals
## 4 Active Tuning of the Photonic Band Gap  
Any stimulus that can modify either the periodicity or the refractive index contrast (or both) of the PhC can lead to a shift of the PBG, according to [PERSON]'s law:  
\[\lambda_{\text{max}}=\frac{2}{\text{m}}\text{d}\sqrt{n_{\text{eff}}^{2}-\sin^{ 2}\theta} \tag{7}\]  
where \(\lambda_{max}\) is the wavelength of the maximum reflection (photonic band gap) peak, \(d\) is the lattice constant, \(m\) is the order of diffraction, \(n_{\text{eff}}\) is the effective refractive index and \(\theta\) is the angle of incidence of the light with respect to the PhC [53]. It is noteworthy that the effective refractive index can be determined with different approaches [54]. A large number of external stimuli that are able to modulate the PBG are reported in the literature. Main examples include chemical, thermal, magnetic, biological, mechanical, light and electrical stimuli.  
Table 2 summarizes the main stimuli and tuning mechanisms mentioned in this article.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Table 18**. & **Approach** & **Advantages** & **References** \\ \hline \hline Co-Extrusion & Bottom-up & Control of thickness & \\  & & Low cost & \\  & & Large area deposition & \\  & & High number of layers & \\ \hline Sputtering & Bottom-up & High reproducibility & \\  & & High yield & [19; 20; 21] \\  & & High precision & \\ \hline Molecular Beam Epitaxy (MBE) & Top-down & High precision & \\  & & Instant thickness control & [22; 23] \\  & & High reproducibility & \\ \hline Pulsed Laser Deposition (PLD) & Bottom-up & High yield & [24] \\  & & High precision & \\ \hline Glancing Angle Deposition (GLAD) & Bottom-up & High reproducibility & [25; 26] \\  & & Choose the deposition angle & \\ \hline Spin Coating & Bottom-up & High reproducibility & [27; 28; 29; 30; 31; 32; 33] \\  & & Easy and fast execution & [27; 28; 29; 30; 31; 32; 33] \\ \hline Nanoimprint Lithography & Top-down & Low cost & \\  & & High throughput & [34; 35; 36; 37; 38] \\ \hline Self-Assemble & Bottom-up & No need of instrumentation & [39; 40; 41; 42; 43; 44; 45; 46] \\  & & Reaction engineering & \\ \hline Plasma Enhanced Chemical Vapor Deposition (PECVD) & Bottom-up & High uniformity & \\  & & Low temperatures & \\  & & Fast deposition & \\  & & High yield & \\ \hline Inverse Opal Template & Bottom-up & Possibility to insert active materials & [48; 49; 50; 51; 52] \\  & & in the structure & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main manufacturing techniques of photonic crystals. The type of approach and the most significant advantages are indicated for each technique.
## 4 Experimental Evaluation  
Based on experiments, we compared the proposed method with some existing popular methods. We should consider the issue of how to select the most suitable strategy for storing RDF data. For this, we investigated the balancing between two performance metrics, i.e., query response time and index load time [30]. The objective of the experiments was to show that we could achieve excellent join query performance with retrieval accuracy. We compared the learned semantic index structure, which we refer to as LearnS, with Quad [31], Darg [32], Dams [33], Midas [34], and Two-step [24]. Quad is an existing centralized approach, and Darg is a distributed approach; Dams is a recently proposed distributed approach, which combines adaptive hashing and the master-slave model; Midas is an index-based approach; and Two-step is a hybrid approach combining R*-tree and \(k\)-d trees. This evaluation is conducted on a server system with a 3.6-GHz Intel i7 CPU and 8 GB of memory. All programs were written in Python and Java languages on a server running Windows 10.  
We used the LUBM (Lehigh University BenchMark) dataset [35] to obtain realistic results. The LUBM dataset is the most widely used benchmark dataset in the semantic web community. The LUBM dataset contains 230,061 triples, 38,334 subjects, 17 predicates, and 29,635 objects, and its size is 36.7 MB. The LUBM dataset offers well-designed test queries that fully cover features of a traditional reasoning system. However, LUBM does not support all join query types, so we extended the original version to support above seven different join types. We used 12 different benchmark join queries provided by [36], and we additionally made two chain and one cycle benchmark join queries based on the LUBM dataset. Thus, we provided 15 different join queries supporting all join query types. Among the 15 queries, 6 queries (Q1, Q3, Q4, Q5, Q11, and Q13) are star queries. Chain (Q6 and Q10), directed cycle (Q2), cycle (Q15), tree (Q9), single (Q14), and complex (Q7, Q8, and Q12) queries are 2, 1, 1, 1, and 3, respectively. Additionally, we considered the LinkedGeoData dataset [37] to provide a more solid experiment. LinkedGeoData is a large spatial knowledge base, which has been derived from OpenStreetMap for Semantic Web. It interlinks geo-data with other knowledge bases in the LOD cloud. LinkedGeoData contains 2,207,295 triples, 552,541 subjects, 1320 predicates, and 1,308,214 objects. Its size is 327 MB.
# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 4 Methodology Proposal
### Stage 5: Graph Visualization  
The core of the visualization stage lies in choosing appropriate representations of the graph to support answering the questions defined in stage 1. Users can choose several visualization forms, such as tables, networks, or graphics. This stage has a strong dependence on the previous stages. We contemplate that there may be multiple iterations between the visualization stage and stage 1, 2, and 3. Allowing the incorporation of the results of previous tasks such as topological modifications, new structural attributes or other results of graph mining algorithms. Each alteration or transformation of the graph carries a visual representation that guides the ongoing analysis process. This stage consists of six tasks:  
(5.1) Apply Layout refers to configuring the shape, arrangement, and presentation of nodes, and their relationships within a graph. The objective is to enhance the legibility and comprehension of the network's structure. Some examples are Force Atlas Layout, Circular Layout, Hierarchical Layout, and Tree Layout. The reader can refer to [59] for guidance in this task. (5.2) Perform Drill-down/Roll-up Operations are two approaches for navigating and scrutinizing hierarchical or layered data structures. They are useful for graph visualization and analysis, enabling the in-depth exploration of particulars or the synthesis of information within higher levels of the hierarchy. Moreover, they offer the capability to extract pertinent insights across varying levels of granularity or abstraction. Drill-down involves a comprehensive examination of the hierarchical data structure, commencing from an upper-tier perspective and progressively delving into finer levels of intricacy. Within the field of graph analysis, this entails the increasingly specific exploration of nodes and connections, starting from an initial node. For example, one can initiate a drill-down analysis at the category level, progressively descending to individual products. Conversely, roll-up refers to condensing data from lower tiers to upper levels within the hierarchy. In a graph, it might be necessary the combination or abstraction of nodes and relationships to present a broader or more top-level perspective of the data structure. For example, it is possible to initiate a roll-up analysis, beginning with individual products, aggregating them into categories, and further grouping several categories based on specified criteria. The reader can refer to [60] for guidance in this task. (5.3) Define Graph Display Rules comprehends the description, design, and implementation of the graph representation rules using scripts and visualization tools. The choices made to implement these rules can profoundly influence the clarity and efficacy of graph analysis, effectively highlighting patterns, relationships, and facets within the dataset. These rules can incorporate an array of settings and customizations, each of which can be applied to distinct facets of the graph visualization.
# DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation
## 4 Performance Analysis  
### Experimental Data  
In order to test the proposed hybrid prediction of depth model, the data of Nantong Port in Jiangsu Province of China and Fangcheng Port in Guangxi of China were selected for analysis. Nantong (31\({}^{\circ}\) 59\({}^{\prime}\) 32\({}^{\prime\prime}\) N, 120\({}^{\circ}\) 49\({}^{\prime}\) 46\({}^{\prime\prime}\) E) is located near the east Yellow Sea, southwest of the Yangtze River, and it is an important coastal port of China. Nantong has both a natural inland river port and a deep-water coastal port. Fangcheng Port (21\({}^{\circ}\) 44\({}^{\prime}\) 58\({}^{\prime\prime}\) N, 108\({}^{\circ}\) 21\({}^{\prime}\) 41\({}^{\prime\prime}\) E) is a natural river port known as the largest port in western China. Figure 5 shows the satellite images of Nantong Portand Fangcheng Port.  
The AIS data of Nantong Portand Fangcheng Port were selected for these experiments. The entire AIS dataset contains two parts of the ships' dynamic data and static data. The waterway depth data used in the prediction of waterway depth model use the depth data provided by the Global Mapper (Blue Marble Geographics, Hallowell, ME, USA) to obtain the dataset corresponding to the latitude, longitude, and depth data through the latitude and longitude matching. Global mapper is the GIS software wherein depths are taken from ECDIS or other bathymetric sources. The waterway depth  
Figure 5: Location of Nantong Port (**a**) and Fangcheng Port (**b**) in China, and the satellite maps of Nantong (**c**) and Fangcheng (**d**).  
data at the missing latitude and longitude points are interpolated by three-dimensional interpolation using AIS data latitude and longitude data.  
The density map for the Nantong Port dataset showing the most probable positions of ship along the sea fairway is shown in Figure 6.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 2 Waveform Modulation and Demodulation  
The waveform is known as the physical shape of information represented by a signal transmitted through the channel. The transmitted signal \(x(t)\) in a pulse-shaping system is formed by modulating data symbols \(d_{n,k}\) onto time-frequency (delay-Doppler) shifted versions of a transmit pulse \(g(t)\), i.e.,  
\[x(t)=\sum_{n\in\mathbb{Z}}\sum_{k\in\mathbb{Z}}d_{n,k}g_{n,k}(t), \tag{1}\]  
with  
\[g_{n,k}(t)=(\mathbb{M}_{kF}\mathbb{D}_{nT}g)(t)=g(t-nT)e^{j2\pi kFt}, \tag{2}\]  
where \(\mathbb{M}_{\mathbb{v}}\mathbb{D}_{\mathbb{T}}\) is the time-frequency shift operator that includes a delay (time shift) \(\tau=nT\) and a modulation (frequency shift) \(\
u=kF\), \(n\) is the time index, \(k\) is the subcarrier index, \(T\) is the sampling period, and \(F\) is the sub-carrier spacing.
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 3 Methodology
### Overview of Testing Framework
#### 3.1.2 Performance Verification  
In the performance test of a SUT, the verification focuses on testing the network performance of VNFs. The main goal is to benchmark the target VNF and evaluate the performance metric. This paper adopts NFVBench [45] to evaluate the throughput of VNFs on the SUT. In NFVBench, TRex is applied as a traffic generator and a TestPMD as a target VNF to forward the received packets. When launching the performance test, TRex sends packets from the test host to the target VNF. Before reaching the VNF, the packets are transmitted throughout the TOR switch and the OpenStack cloud system. VNF then uses L2 Forward to send the packets back to the traffic generator.  
In practice, a telecom application usually deploys multiple VNFs and connects these VNFs to the SFC. When the number of VNFs in the SFC increases, the packet path of this SFC is longer. The length of a packet path has an impact on the network performance. This paper considers different traffic flows for performance test scenarios: PVP and PVVP tests. The experiment of the PVP scenario is used to estimate the performance of end-to-end traffic. As shown in Figure 6, the traffic generator installed on the test host sends out the packets to the target VM deployed on a cloud system. When the VM receives packets, the VM sends the packets back to the traffic generator through the TOR switch.  
The PVVP scenario is conducted for the multiple VNFs of a simple SFC scenario as shown in Figure 7. The traffic generator sends out the packets to the first VM. When the first VM receives packets, the first VM forwards the packets to the other VM via the east-west traffic in OpenStack. The VM then sends the packets back to the traffic generator through the TOR switch. Lengthening the packet path increases network communications in the data plane. If a component in the packet path cannot handle the packets, the throughput worsens. Therefore, this experiment involves both evaluations of the north-south traffic and the east-west traffic in the SUT.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 4 Computer Simulation Experiments
### Experimental Results  
In this section, the experimental results of the Z-OFDM system in two different environments (Fuxian Lake and Wuyuan Bay) are presented by illustrating the BER and its fluctuations as the refinement factor \(D\) increases. To ensure a fair comparison of performance, the experiments are conducted at the same transmission rate, which can be calculated using the following equation:  
\[\begin{split} R_{\text{Z-OFDM}}&=N_{s}\cdot R_{b} \\ &=DN_{s}\cdot\frac{1}{T^{\prime}}\cdot\log_{2}M\\ &=DN_{s}\cdot\frac{B}{\left(1+\frac{T_{G}}{T_{S}}\right)DN_{DFT} }\cdot\log_{2}M\\ &=\frac{BN_{S}\log_{2}M}{\left(1+\frac{T_{G}}{T_{S}}\right)N_{DFT} }\end{split} \tag{28}\]  
where \(N_{s}\) is the number of subcarriers, \(R_{b}\) is the transmission rate of a single subcarrier, \(T^{\prime}\) is the period of a single subcarrier, \(B\) is the bandwidth, \(T_{G}\) is the guard interval, \(T_{S}\) is the symbol length, and \(N_{DFT}\) is the number of DFT points. Since QPSK modulation is employed in this communication system, \(M=4\).  
In this experiment, regardless of the value of \(D\), the transmission rate is consistently maintained at 2.025 kbps.
# Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland
## 2 Materials and Methods
### Winds Model
Special emphasis was laid on dead-standing trees, which do not represent value for the natural landscape. Analysis of the accuracy of the obtained results showed that the GEOBIA method gave good results (Kappa coefficient equal to 0.8) in the automated process of generating current maps of the occurrence of dying trees based on PlanetScope satellite images and LiDAR data. Additionally, a smartphone application (Android, iOS) was developed to allow for the inputting of data by hikers who would notice unmarked trees matching the criteria.
## 3 Results and Discussion
### Attaining CLG Behavior
In case of \(\lambda>1\), which is illustrated in Figure 3b, the oxygen carrier undergoes a full redox cycle and hence the full oxygen transport capacity of the OC material (i.e., \(R_{\text{OC}}\)) is exploited. On the other hand, \(\lambda<1\) means that in equilibrium the OC leaves the AR in a partially reduced state, hence also reducing the mass specific oxygen transport of the OC (see Figure 3c). Lastly, one might also consider a process with \(\lambda<1\) and \(\phi>1\), as shown Figure 3d. In order to attain a steady-state process exhibiting these characteristics, full reduction of the oxygen carrier has to be prevented in the FR (e.g., kinetically), so that a fraction of oxygen is transported back to the AR. This means that in contrast to the former approaches, this case cannot be attained in equilibrium-like conditions. While this approach might also be feasible for CLG operation in theory, straight forward measures allowing for a controlled oxygen release in the FR are not at hand. Consequently, lowering the oxygen-to-fuel-ratio in the FR (i.e., \(\phi<1\)) is the most promising avenue to attain CLG behavior. When aiming for large syngas yields, \(\phi\) has to assume values below unity, while values exceeding unity are targeted in CLC [42,43]. In the following, different effective control strategies to achieve this reduction in \(\phi\), required for pronounced syngas formation in the FR, while at the same time achieving an autothermal process, will be investigated. In order to simplify the subsequent considerations, a standard parameter to describe gasification processes, the cold gas efficiency (CGE), \(\eta_{\text{CG}}\), will be deployed hereinafter. It describes which amount of chemical energy from the fuel is transferred to the gaseous FR product gas during gasification [6,7]. \[\eta_{\text{CG}}=\frac{\dot{n}_{gas,FR}\cdot(x_{CH4,FR}\cdot LHV_{CH4}+x_{CO,FR }\cdot LHV_{CO,FR}+x_{H2,FR}\cdot LHV_{H2})}{\dot{m}_{fuel}\cdot LHV_{fuel}} \tag{14}\]  
Figure 3: Different chemical looping modes **(a–d)** dependent on the air-to-fuel equivalence ratio \(\lambda\) and the oxygen-carrier-to-fuel equivalence ratio \(\phi\). Here, \(\dot{n}_{gas,FR}\) and \(\dot{m}_{fuel}\) denote the mole flow of the product gas stream and the fuel input into the FR, respectively. \(LHV\) is the lower heating value of the fuel (mass basis) and the gas species (molar basis) and \(x_{i}\) is the mole fraction of the gas species.