esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
## 3 Methodology
### Loss Functions
#### 3.4.2 Unsupervised Loss  
The design idea of unsupervised loss is to enable the model to utilize the information of unlabeled data during inference. For the above purposes, unsupervised loss \(\mathcal{L}_{unsup}\) is designed to consist of two items: loss of aux-encoders \(\mathcal{L}^{e}_{unsup}\) and loss of aux-decoders \(\mathcal{L}^{d}_{unsup}\).  
\[\mathcal{L}_{unsup}=\mathcal{L}^{e}_{unsup}+\mathcal{L}^{d}_{unsup} \tag{3}\]  
We first need to generate pseudo labels \(\hat{y}^{ul}\) corresponding to the input \(x^{ul}\), shown as (4).  
\[\hat{y}^{ul}=\mathrm{De}_{m}(\mathrm{En}_{m}(x^{ul})) \tag{4}\]  
Figure 4: The detailed structures of the main decoder.  
On completion of that, a copy of \(x^{ul}\) is sent to each aux-encoder \(\text{En}^{i}_{aux}\), where one certain perturbation is applied on it, and then transferred to the main-decoder \(\text{De}_{m}\) to make predictions \(\hat{y}^{ul}_{e,i}\). This progress can be formulated as below:  
\[\hat{y}^{ul}_{e,i}=\text{De}_{m}(\text{En}^{i}_{aux}(x^{ul})) \tag{5}\]  
As for the unsupervised loss of aux-decoders \(\hat{y}^{ul}_{d,i}\) it is processed through the main-encoder \(\text{En}_{m}\) and each aux-decoder \(\text{De}^{i}_{aux}\), which is mathematically expressed as follows:  
\[\hat{y}^{ul}_{d,i}=\text{De}^{i}_{aux}(\text{En}_{m}(x^{ul})) \tag{6}\]  
Following obtaining the predictions \(\hat{y}^{ul}_{d,i}\) for aux-decoders, MSE loss is calculated as follows:  
\[\mathcal{L}^{d}_{unsup}=\frac{1}{K}\sum_{i=1}^{K}\text{MSE}(\hat{y}^{ul}_{d,i },\hat{y}^{ul}) \tag{7}\]  
The loss \(\mathcal{L}^{e}_{unsup}\) is back-propagated through aux-encoders and the main-decoder, and \(\mathcal{L}^{d}_{unsup}\) is back-propagated through aux-decoders and the main-encoder. Thus, both the main-encoder and main-decoder are able to exploit information of unlabeled data during inference.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 3 Disruption Tolerant Distributed Routing Algorithm
### Failure Area Routing Calculation  
#### 3.5.1 Routing Information Transfer  
When an ISL is disrupted, only the directly connected space router will perceive and adopt the corresponding routing strategy, and the surrounding nodes will not reduce the incoming data to the node, which will cause congestion in the links around the node. If the node disseminates link failure information to the entire network, it will cause a certain amount of overhead, and secondly, the link failure information received by the space router that is far away from the node will be lagging, and it is of little significance for the space routers far away from the failed link to take countermeasures. Considering that the intersatellite link failure time is short, and the timeliness requirements are high, it is not the best choice to spread the link failure information across the network. In our routing strategy, the router directly connected to the failed link floods the link failure notification to the satellite nodes within \(n\) hops. The satellite node that receives the failure information immediately enters the failure area state (FAS) and adopts the strategy of bypassing in advance.
## 2 Results and Discussion
### STD-NMR Protein-Fragment Interaction Experiments  
The relatively weak affinity of the fragments measured by TSA directed us towards NMR methods to assess the specificity of the fragments-protein interactions. Indeed, ligand-based NMR methods [51] can be applied to weak and transient protein-ligand complexes that are difficult to study with other structural techniques and do not require protein labeling (since only NMR signals of the small molecule are detected), and only a small amount of protein is required. In particular, STD (Saturation Transfer Difference) exploits NOE effects between the protein and the ligand to map target-ligand interactions and to characterize biologically relevant complexes [52; 53].  
Considering the promising selectivity profile of the aminobenzic acids for the Rel\({}_{Seq}\) synthetase domain, we performed STD-NMR experiments with fragment **A1** as the representative chemotype. The results confirmed the binding event and showed a good interaction for the aromatic protons of **A1** with Rel\({}_{Seq}\) (1-385) (Figure 5b). Comparable overall STD intensities, suggesting a similar binding mode, were observed for Rel\({}_{Seq}\) SYNTH (79-385), confirming the specificity of the interaction (Figure 5c). On the other hand, the Rel\({}_{Seq}\) HYD (1-224) construct did not produce any magnetization transfer (Figure 5d). STD experiments performed on Rel\({}_{Seq}\) (1-385) with the two non-binding fragments **B1** and **A2** did not show any significant interaction, refuting artifacts or non-specific binding (Supplementary Figures S5 and S6, respectively).
###### Abstract  
The demand of delivering various services is driving inter-data centers optical interconnection towards 400 G/800 G, which calls for increasing capacity and spectrum efficiency. The aim of this study is to effectively increase capacity while also improving nonlinear noise anti-interference. Hence, this paper presents a state-of-the-art scheme that applies the K-means cluster algorithm in geometric shaping based on iterative polar modulation (IPM). A coherent optical communication simulation system was established to demonstrate the performance of our proposal. The investigation reveals that the gap between IPM and Shannon limit has significantly narrowed in terms of mutual information. Moreover, when compared with IPM and QAM using the blind phase searching under the same order at HD-FEC threshold, the IPM-16 using the K-means algorithm achieves 0.9 dB and 1.7 dB gain; the IPM-64 achieves 0.3 dB and 1.1 dB gain, and the IPM-256 achieves 0.4 dB and 0.8 dB gain. The robustness of nonlinear noise and high capacity enable this state-of-the-art scheme to be used as an optional modulation format not only for inter-data centers optical interconnection but also for any high speed, long distance optical fiber communication system.  
inter-data centers optical interconnection; iterative polar modulation; K-means cluster algorithm +
Footnote †: journal: Electronics Letters  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] K-means Cluster Algorithm Applied for Geometric Shaping Based on Iterative Polar Modulation in Inter-Data Centers Optical Interconnection 2021202417[[https://doi.org/10.3300/electronics10192417](https://doi.org/10.3300/electronics10192417)]([https://doi.org/10.3300/electronics10192417](https://doi.org/10.3300/electronics10192417))
# Metamaterial Integrated Folded Dipole Antenna with Low SAR for 4G, 5G and NB-IoT Applications
## 5 Conclusions  
This research provided a thorough study and assessment of a novel wideband folded dipole antenna. The structure of the antenna comprises a radiating folded arm to minimize the antenna's size. The controlling parameters are capable of tuning the performance of the folded arm and can be configured to synthesize the design for wide bandwidth requirements. The folded design is capable of covering the 5G NR n78, NR-IoT bands B1, B2, and B25, and eleven TDD LTE bands from 33 to 43. The minimum scattering loss observed for the design was \(-42\) dB with 50.22 \(\Omega\) of input impedance. Furthermore, the folded dipole designed in this research is planar, compact (\(35\times 48\times 1.62\) mm\({}^{3}\)) and simple in structure, thus enabling embedding in radio applications. In addition, the performance of the antenna design with metamaterial integration was also evaluated. A significant increase in gain was observed in antenna design for all operational frequency bands. It was observed that the metamaterial acts as a good absorber for radiation, especially at high frequency bands. The SAR investigation with and without metamaterial integration is also presented in this research. A tremendous decrease in SAR value was observed for all the operational bands, especially for the n78 5G and LTE high frequency bands. The SAR value observed for all the bands was considerably below the acceptable peak value of \(1.6\) W/kg. Therefore, the antenna was proved to be a good candidate for mobile applications in terms of bandwidth, return loss, gain and SAR. The performance characteristics were verified by fabricating and testing a prototype. An excellent agreement was achieved between the simulated and measured results. The designed antenna can be used to address the issue of band congestion on a single chip for 4G/5G/IoT applications.  
Conceptualization, H.S., A.G. and N.M.; methodology, H.S., A.G., N.M., Y.K., M.W. and A.W.; software, H.S., A.G. and N.M.; validation, H.S., A.G., N.M., Y.K., M.W. and A.W.; investigation, H.S., A.G. and N.M.; writing--original draft preparation, H.S., A.G. and N.M.; writing--review and editing, Y.K., M.W. and A.W.; visualization, H.S., A.G., N.M., Y.K., M.W. and A.W.; supervision, M.W. and A.W.; project administration, A.W. and M.W.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors acknowledge contribution to this project from the Rector of the Silesian University of Technology under a propuality grant no. 09/020/RGJ21/0007. [PERSON] would like to thank the Deanship of Scientific Research, Prince Sattam bin Abdul Aziz University, for supporting this work.  
The authors declare no conflict of interest.
# Agriculture 4.0: Is Sub-Saharan Africa Ready?
## 3 Going Forward: Opportunities for SSA to Tap into the Agriculture 4.0 Revolution
### Reasons for Optimism
The positive impacts anticipated of this agriculture 4.0 technology adoption include yield increases and a reduction of carbon footprint in line with the Sustainable Development Goals (SDGs), low cost of attracting and maintaining farmers in the supply chain, and price transparency [25]. Adoption of technologies promoted by agriculture 4.0 that lead to improvements in the environment may be particularly relevant for smallholders. For example, adopting agriculture 4.0 technologies for precision agriculture is likely to lead to fertilisers and pesticides being applied in the required doses and concentration, informed by data on the soil condition and nutrient requirements for each crop linked to the weather forecast for rainfall [12]. It is estimated that about 60% of conventional fertilisers are lost to the environment on application, leading to pollution [14], particularly of water bodies. These agriculture 4.0 technologies, therefore, enable farmers to both reduce their input costs and improve their environmental sustainability. The use of nanofertilisers, an example of nanotechnology precision agriculture, in which nutrients are released slowly resulting in exact dosages [14], also has the potential to improve farm economic and environmental sustainability. In urban areas, many agriculture 4.0 technologies are associated with the automation of agricultural operations using high-tech solutions [12] supported by well-developed ICT infrastructure, for instance, the rise in wireless communication technologies that use low power wide area network (LPWAN) such as Sigfox, LoRa and NB-IoT [78]. This is not the case in rural areas where smallholders tend to be located [79]. Nevertheless, platform-based ICT and mobile technologies are most suited to the smallholder context and are likely to support SSA with regard to food security and agricultural sustainability [25].
# Seismic Risk Assessment of Chania, Greece, Using an Integrated Computational Approach
## 4 Seismic Risk Analysis Results
### Seismic Hazard Scenarios Using a Known Seismic Source with Different Magnitudes  
As mentioned earlier, the Kythira earthquake external model is used to produce three seismic hazard scenarios with the same seismic source but with different magnitudes, i.e., \(\mathrm{M_{w}}\) = 7.5, \(\mathrm{M_{w}}\) = 8.0, and \(\mathrm{M_{w}}\) = 8.5. It has to be noted that such events are not unrealistic for this region, since a strong subsea earthquake (\(\mathrm{M_{w}}\) > 8) magnitude occurred in the west of the island on July 21 in AD 365, causing a huge tsunami. This seismic event is likely to bethe strongest in the Mediterranean Sea and affected Greece and all neighboring countries, while many towns of Crete were destroyed. Geological evidence reveals an enormous uplift of western Crete, by up to 9 m. It was considered as a \"universal\" earthquake, in terms of its both intensity and the extent of devastating consequences [51].  
Figure 7: Estimated average damage rates per building block according to the attenuation formulas of (**a**) [PERSON] et al., (**b**) CSO 2003, and (**c**) Sabettaand Pugliese.  
Figure 10 illustrates the average degree of damage that is expected for these extremely high-magnitude events to predict the risk of Chania for more adverse scenarios. Certainly, it is expected that the levels of damage and losses will be higher than the ones estimated previously. More specifically, for an earthquake with a magnitude M\({}_{w}\) = 7.5, it is obvious from Figure 10a that the high-risk zones exhibit damage levels on the order of 1.50, while the medium risk zones show damage rates approaching a value of approximately 1.20. Therefore, relatively mild damage levels are expected thatare not likely to affect much the functioning of the city. For the M\({}_{w}\) = 8.0 event, the damage rates approach a value of 1.80 in a high-risk zone, while it is 1.50 in the medium-risk zone. As expected, the M\({}_{w}\) = 8.5 earthquake has an even more detrimental impact; the high-risk areas would experience damage rates close to 2.50, the average-risk areas would experience damage rates above 2.00, while there may be buildings with damage up to the third impact level (see Table 1). Conclusively, by interpreting the above results qualitatively, it can be concluded that damage rates due to future earthquakes up to M\({}_{w}\) = 7.0 are not likely to have devastating consequences in Chania. However, more severe events, with M\({}_{w}\) = 8.0 or M\({}_{w}\) = 8.5, would result in widespread, severe damage and losses.  
Figure 8: Estimated average damage per building block according to the average values of the available QuakeIST library average attenuation relationship (generic law).  
Figure 9: Average degree of damage for the external seismic model based on the Gavdos earthquake, 2012.  
Figure 10: Estimated average degree of damage per building block under extreme seismic events: (a) M\({}_{w}\) = 7.5, (b) M\({}_{w}\) = 8.0, and (c) M\({}_{w}\) = 8.5.
## 2 Materials and Methods
### Statistical Analysis  
The consistency or level of agreement between automatic and manual scoring was assessed using a weighted kappa statistic, whose values range between \(-\)1 and 1. A kappa value of 1 and \(-\)1 represented complete agreement and disagreement, respectively. The level of agreement was further evaluated using the scales developed by [PERSON] et al. [16], Kappa values of <0.20, 0.21-0.40, 0.41-0.60, 0.61-0.80, and >0.80 were defined as poor, fair, moderate, good, and very good agreement, respectively.
# A Review of Multifunctional Antenna Designs for Internet of Things
## 2 Materials and Methods
### Smart Home Appliances
#### 2.2.2 Characteristic Mode Analysis as IoT Design Tool
However, it can indeed be used when \"Things\" are placed at known angular directions, as in that case they may offer a more efficient link. An alternative approach to achieving triple polarization involves using metasurfaces (MSs) as polarization transformation components [66] or even purely as metantennas [63]. An innovative solution features a miniaturized reconfigurable tri-polarization metantenna [63], which uniquely serves as the antenna radiator aperture itself, rather than as an auxiliary component. This metantenna (Figure 9) is composed of windmill-like units derived from square patch units, with design optimization based on characteristic mode analysis (CMA). This approach leads to a significant 46% reduction in size compared to traditional designs, while the radiation pattern presents only one wide beamwidth front lobe due to the ground plane. The metantenna shows remarkable maximum impedance bandwidths of 24.6% (ranging from 3.24 to 4.15 GHz) for circular polarization (CP) and 24.1% (spanning 3.26 to 4.05 GHz) for linear polarization (LP), accompanied by a peak gain of 5.25 dBi. However, to properly bias the diodes, a complex external network is required. A clever way to mitigate this was proposed in [66], where the authors managed to bias  
Figure 7: (**a**) A 6-port reconfigurable dual-band dual-mode microstrip ring antenna designed exploiting characteristic mode analysis: (**b**) its feeding network and (**c**) scattering parameters [7]. the diodes without using a self-bias network, significantly reducing the complexity of the overall structure, as seen in Figure 10. A notable implementation involves leveraging a metasurface to significantly enhance the CP bandwidth while also enabling LP operation, with a microstrip patch serving as the main radiator. The measurements from this design confirm an impressive overlapped bandwidth of 12.4% and a broadside gain exceeding 5.8 dBi for all polarization states. These examples highlight the versatility and effectiveness of employing metasurfaces in developing antennas capable of supporting multiple polarization states. Figure 8: Geometrical configuration of multi-polarization slotted patch antenna in (**a**) top and (**b**) side views, designed based on CMA [6]. (**c**) Photograph of fabricated prototype [6]. Figure 9: Tri-polarized metantenna designed based on CMA [63].
## 3 Results  
### The Acquisition and Analysis of Raman Spectra  
We confirmed the performance of drop-coating deposition of the tear ring. With the microscope of the confocal Raman micro-spectrometer, a part of the uniform ring for the dried tear sample was imaged, as can be seen in Figure 1a. The Raman spectra at different locations of the tear drop were recorded, including the ring, quartile and central regions. Three spectra were acquired and averaged as one spectrum for each region (Figure 1b). Raman spectra collected from the ring region provided stronger signals and more features of Raman peaks in the range of 400-1800 cm\({}^{-1}\). Therefore, the spectra from the ring region for all samples were further analyzed.  
After the calculation of the average spectrum for each tear sample, the mean spectra were evaluated and displayed for POAG, PACG and normal group. As shown in Figure 2a-c, the mean spectra of three groups possessed similar profiles, positions and bandwidths for different characteristic peaks. To figure out the specific differences between different spectra, the pairwise difference in the spectral intensities was calculated for all three groups.  
Figure 1: The acquired Raman spectra from different regions of deposited tear drop. (**a**) A part of the tear sample image after drop-coating deposition. (**b**) Raman spectra from the ring, quartile and central regions. Scale bar: 50 \(\upmu\)m.  
Figure 2: Averaged Raman spectra for POAG, PACG and normal groups. (**a**) Comparison between POAG and the normal groups. (**b**) Comparison between PACG and the normal groups. (**c**) Comparison between POAG and PACG groups. The differences in compared two groups are indicated by black lines. Peaks with significant difference are indicated by black arrows.  
Except for 1270 and 1313 cm\({}^{-1}\), most other Raman peaks had higher intensities for glaucoma patients than for the normal individuals. The changes in the peaks were basically associated with the different metabolic environment of glaucoma, leading to the changes in chemical components in tear samples. According to statistical analysis with ANOVA, some Raman peaks of the three groups showed significantly different intensities from each other (_p_ < 0.05). The peaks are indicated by black arrows in Figure 2, and the mean intensities of the peaks are displayed in Figure 3. Specifically, peaks at 447, 1003 and 1033 cm\({}^{-1}\) of the POAG group had higher intensities than those of the normal group (_p_ < 0.05). For the PACG group, the intensities of Raman peaks at 447, 760, 852, 1003, 1033, 1582 and 1664 cm\({}^{-1}\) were higher than those of the normal group, with statistical significance (_p_ < 0.05). The attribution of related Raman peaks in the range of 400-1800 cm\({}^{-1}\) for glaucoma and normal tears is listed in Table 2 based on previous reports [30; 38; 43].
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 3 Proposed Methodology
### Feature Extraction  
A feature extraction process is a systematic procedure for the preparation of cleaned data with machine learning applications in mind. After the supply chain data go through a rigorous cleansing process whereby inconsistencies and inaccuracies are eliminated, it becomes necessary to move on to an extraction-of-features phase aimed at identifying relevant information. In this systematic process, several techniques are used to crystallize the meaningful patterns and characteristics from cleaned data. This procedure entails retailfeatures, statistical feature extraction, and tracking metrics like mean variance standard deviation and skewness over time. Time series feature extraction involves many approaches, including a simple average, log-log linear regression model weighted averages, and two levels of the linear approach to reveal temporal sequences in data. With this systematic extraction of the aforementioned features, the dataset is refined and made more informative, paving the way to precise and efficient training for demand forecasting in an enriched supply chain environment.
## 4 A Method for Configuring VWSNs to Use the Properties of Brain Networks
### Human-Brain Networks
#### 4.1.2 Small-World Properties  
Small-world networks are characterized by properties, such as short average path length and a high clustering coefficient. The average path length (APL) is defined as  
\[\text{APL}=\frac{1}{N(N-1)}\sum_{i,j}sd(i,j), \tag{1}\]where \(N\) is the number of nodes and \(sd(i,j)\) is the lowest hop count between node \(i\) and node \(j\). A low APL indicates highly efficient global communication. The clustering coefficient (CC) is defined as  
\[\text{CC}=\frac{1}{N}\sum_{i}\frac{2e_{i}}{k_{i}(k_{i}-1)}, \tag{2}\]  
where \(e_{i}\) denotes the number of links between neighbors of node \(i\), and \(k_{i}\) is the degree of node \(i\). The degree of a node is the number of neighboring nodes connected with the node by wireless or wired links. A high CC indicates that nodes in local regions are densely connected, which contributes to local communication efficiency.  
In brain networks, densely-connected nodes within a module contribute to a high CC, which leads to efficient, segregated information processing and synchronization. Myelinated long-distance links with high electric conductivity (i.e., high reliability, high speed, long-distance links) are one contributor to global communication efficiency in brain networks [22]. The short communication delay of this type of link enables close cooperation between different regions.  
To model this property in a VWSN topology, we introduce a virtual long-distance connection that connects two physically-distant sensor nodes. This leads to a reduction in communication delays across the whole network because it reduces the APL while many parts of the network keep their overheads low.