esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture and Requirements
### Receive Chain
#### 2.1.1 Requirements for FDD
\[\text{V}_{\text{o, IM3}}=(3/4)\alpha_{3}\text{V}_{\text{A}}^{3}(\cos(2\omega_{1 }-\omega_{2})\text{t}+\cos(2\omega_{2}-\omega_{1})\text{t}) \tag{2}\]  
\[\text{V}_{\text{o,XM3}}=(1/4)\alpha_{2}\text{V}_{\text{A}}^{2}\text{V}_{\text{B }}(\cos(2\omega_{1}-\omega_{\text{B}})\text{t}+\cos(2\omega_{2}-\omega_{\text{ B}})\text{t}+2\cos(\omega_{1}+\omega_{2}-\omega_{\text{B}})\text{t}) \tag{3}\]  
To mitigate the cross-modulation product, which is the limiting factor in FDD mode, a feedback path (\(\beta\)) is introduced from the canceler output to its input. Hence, the canceler input signal becomes V\({}_{\text{IN}}=\text{V}_{\text{TX}}-\beta\text{V}_{\text{B}}\), and an additional cross-modulation term is generated as follows:  
\[\text{V}^{\prime}{}_{\text{o,XM3}}=(1/4)\alpha_{3}\beta\text{V}_{\text{A}}^{2 }\text{V}_{\text{B}}(\cos(2\omega_{1}-\omega_{\text{B}})\text{t}+\cos(2\omega _{2}-\omega_{\text{B}})\text{t}+2\cos(\omega_{1}+\omega_{2}-\omega_{\text{B}}) \text{t}) \tag{4}\]  
Setting the feedback factor properly (\(\beta=\lambda\alpha_{2}/\alpha_{3}\)) the two cross-modulation products cancel each other: V\({}_{\text{o,XM3}}+\text{V}^{\prime}{}_{\text{o,XM3}}\cong 0\). Notice that since both the canceler gain (\(\alpha_{1}\)) and the feedback factor (\(\beta\)) are small, the loop gain is much smaller than one and hence no stability issue arises.
## 3 Phage-Mediated Bacterial Detection Approaches
### Phage-Associated Proteins  
Phage receptor-binding proteins (RBPs) are the most variable structures of phages, which are responsible for recognizing specific receptors on the host bacterium [60]. Unlike antibodies, these proteins are relatively resistant to a wide range of pH values and heat treatments as well as protease activity, while showing analogous or even superior specificity [61]. These intrinsic features make RBPs more efficient and much-needed biorecognition elements for the specific and rapid detection of bacterial pathogens from different matrices [61]. These specialized phage binding proteins have been used for the detection of pathogenssuch as _Shigella_[62], _Salmonella_[63], and _P. aeruginosa_[64] from different food samples. Similarly, [PERSON] and co-workers designed magnetic beads by immobilizing the RBP protein Gp047, derived from the phage NCTC12673, and used them for the capture and detection of _Campylobacter_ from chicken broth and milk samples [65].  
Cell wall-binding domains (CBDs) of bacteriophage-encoded peptidoglycan hydrolases, commonly called endolysins, are the other phage-associated proteins (polypeptides) that have a high affinity and specificity towards the ligands on the Gram-positive cell wall [66]. Currently, CBD-based magnetic separation (CBD-MS) has been effectively used for detecting several Gram-positive foodborne bacteria, such as _B. cereus_[67], _Listeria_[66], and _Clostridium tyrobutyricum_[68].
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 2 Materials and Methods
### Performance Metrics
#### 2.4.6 Routing Overhead (RO)  
Routing overhead shows the number of routing packets transmitted during a simulation [32]. The routing packages in the AODV protocol are Route Request (RREQ), Route Reply (RREP), and Route Error (RRER), as shown in Equation (6). Meanwhile, in OLSR, there are HELLO routing packages and Topology Control (TC), as shown in Equation (7). In general, an increase in TC messages may cause congestion, collision, and additional energy consumption, which may decrease network performance [33]:  
\[\text{AODV Routing Overhead}=\text{RREQ}+\text{RREP}+\text{RRER} \tag{6}\]  
\[\text{OLSR Routing Overhead}=\text{HELLO}+\text{TC}. \tag{7}\]
# Implementation of Pavement Defect Detection System on Edge Computing Platform
## 2 Related Works
The weather variations or illumination changes, such as poor weather conditions, shadows cast from objects or water, and low-illumination environment, are challenging problems. In addition, there are only subjective consciousness assessments lacking objective quantitative analysis. Consequently, no effective approach is presently accessible for detecting road pavement defects, and most significantly, pavement defects that are further out cannot be detected so as to provide further application in vehicle control, such as a pothole warning system or vehicle stability control system, and to simultaneously meet all-weather requirements. Besides, for vehicle applications, the overly complicated DL network architecture is unable to meet the requirements of a real-time system and embedded hardware with vehicle specifications. Although current object recognition algorithms based on deep neural networks have outstanding performance in many applications, DL models often need high-performance computing hardware. e.g., multicore high-performance graphics processing unit and a similar processing system, and consume a lot of power; thus, several AI-based applications are difficult to apply to embedded systems with limited memory space and computing resources. Therefore, in this study, in addition to the development of an AI-based defect detection scheme, we use the model compression technology toward a lightweight model,and then an FPGA-based hardware accelerator edge computing platform is employed as an embedded system to implement the proposed pavement defect detection model. Experimental results show that the proposed system not only satisfies the real-time computing requirements while maintaining the accuracy of the original system model but also becomes a competitive product in the automotive electronics market.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 4 Photonic Devices and Components
### Technologies for ROADMs
The capability of routing wavelengths belonging to a flexible grid wavelength is called the grid-less feature [10]. Colorless, directionless, contention-less, and grid-less (CDC/G) ROADMs offer significant reductions in the network TCO. Since CDC/G nodes route wavelength in the photonic domain, rather than electrically, OpEx can be 35% lower than with conventional alternatives. Thanks to photonic operations, CDC/G ROADMs are more power efficient (about 30%-40% less power needed) and introduce less wavelength service latency. Moreover, CDC/G ROADMs offer major scalability, since they are capacity independent, and have major agility to adjust wavelengths to recover capacity. It has been estimated that this technique can allow the recovery of 30% of network capacity, and thus a 30% CapEx savings, through deferring expensive network upgrades [11]. Nowadays, CDC/G ROADMs are a solution designed exclusively for core networks but, in the opinion of the authors, some of their features could be implemented also in the next generation of metro networks. Sub-optimal solutions are already ordinarily used in metro networks, such as tunable optical add drop multiplexers (TOADMs) which employ tunable filters to select and add/drop a channel or band without the need for demultiplexing and multiplexing [47]. Metro ROADMs (especially CDC/G) are not a cheap solution, but over the long-term, they could represent the most reliable, robust, and cost effective option to reduce OpEx, increase network reliability, and manage more traffic [46].
## 4 Discussion
### Case Study: Information Gap Analysis and Demonstration Discussion  
Recent developments in both research and industry support our findings that dynamic risk and public perception are among the most common information gaps in the wicked wildfire use case. Risk assessments from commercial technologies such as Risk Factor by First Street Foundation and public offerings such as the US Forest Service's WildfireRisk.org or Fuelcast.net are integrating new models for risk [30; 90], hazard [37; 42; 58], exposure [43; 91], and vulnerability [92; 93]. Combining risk data with evacuation simulations (Figure 7) presents an opportunity for resilience planning with respect to infrastructure planning. Our case study showed that traffic is a predictable consequence of fire evacuations, and it could be mitigated with both planning and technology. Combining long-term fire hazard data with evacuation simulations could inform new road configurations that save lives by preventing entrapment as people move away from high hazard areas (e.g., Appendix A User Stories 3.6 and 3.7). Similarly, social media filtering has become more common for providing information on public perception. Research advancements using social media data show capabilities for geolocating [94; 95; 96] and identifying misinformation, deep fakes [97] and unique contributions [11]. The use of these research advancements has yet to be integrated into commercial or public technology offerings.  
R2C shows that WKID Innovation and HCD show complementary but not duplicative value for transitioning research to commercialization. WKID Innovation enabled the identification of unique information gaps spanning user types with refined requirements for how to define that information in the context of decisions. WKID Innovation did not provide iterative feedback for how someone might interact with that information in their day-to-day life. HCD provided useful feedback on how users would interact with information in the form of technology (dashboards, apps, etc.), but it neglected to capture the value of social media filtering to provide information on public perception. This is likely because users do not know to ask for something that does not already exist [20, 21, 22]. Furthermore, the feedback from users often resulted in comments that they did not need something that was already similar to a technology they already had but only offered incremental design improvements [19]. R2C demonstrates the need for both a systematic evaluation of information gaps to inform solutions-oriented science and the need for HCD to place the context of that information in a tool that can be commoditized to provide value-added analytics.
# Data Compression with a Time Limit
## 3 Lossless Compression
### Lossless Compression of Two-Dimensional Data with a Time Limit
#### 3.2.2 Compression of a Set of Animated Images with Different Scenes  
The dataset \(D\) to be compressed consisted again of 1000 images, for a total size of 659 MB (691,744,000 bytes). Single runs of the compression tools yielded the following pairs (\(b_{i}\), \(t_{i}\)):  
Table 4 shows us that the fastest compressor was BMP while the slowest was JPEG2000. From the point of view of the compression obtained, the best compressor was JPEG2000, while the worst was BMP.  
The _useful-setup_ is therefore composed of the compressors BMP, FELICS, JPEG-LS, and JPEG2000. Two time budgets were set to experiment the algorithm. With an assigned time budget, \(t_{1}\) = 571,000 ms then the optimal mix is given by the mix of the JPEG-LS and JPEG2000 setups, and it is very close to the JPEG2000 setup. All this is shown in Figure 3:  
Here, the optimal mix is at the intersection point.  
A total of 16 images must be compressed using JPEG-LS, the other 984 with JPEG2000. The compression time was 566,798 ms, while the output size was 167 MB (175,356,694 bytes).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tool** & **Size of Output in Bytes** & **Compression Time in ms.** \\ \hline PNG & 278,923,712 & 83,538 \\ TIFF & 263,128,720 & 93,542 \\ JPEG-LS & 188,777,508 & 127,575 \\ JPEG2000 & 174,971,373 & 578,176 \\ BMP & 691,254,300 & 36,992 \\ FELICS & 252,695,812 & 58,873 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Animated images with different scenes.  
Figure 3: Optimal mix, t = 571,000 ms.  
In this case, the time budget is not used completely: the compression ends about 5 s earlier: this difference is to be considered acceptable considering that a very large time budget has been used, and that the compression is performed one file at a time.  
For \(t_{2}\) = 69,000 ms, instead, the optimal mix is given by the mix between the JPEG-LS and FELICS configurations, and it is in their center. Figure 4 shows it:  
The results are consistent with all the previous tests and the optimal mix is at the intersection point.  
The number of images we compress with JPEG-LS is 147, while for FELICS it is 853.  
The compression time was 68,541 ms, while the output size was 232 MB (243,538,616 bytes).
# Developments on Perovskite Solar Cells (PSCs): A Critical Review
## 5 Fabrication Methods
### Thermal Vapour Deposition Method  
The thermal vapor deposition method involves the simultaneous deposition of the organic (e.g., MAI or FAI) and inorganic (e.g., PbI\({}_{2}\)/PbCl\({}_{2}\)) halides in the vapor phase on a rotating substrate (see Figure 10d). This method offers a uniform surface morphology due to the ease of control over precursor depositing parameters and the speed of rotating substrate [29]. [PERSON] (2013) reported a planar mixed-halide PSC recording efficiencies of up to 15.4%. The MAPbI\({}_{3\times}\)Cl\({}_{x}\) perovskite thin films were prepared from vapor deposition of MAI and PbCl\({}_{2}\) precursor powder materials at a 4:1 deposition ratio [74]. In an attempt to produce a large-surface-area PSC device, [PERSON] (2018) reported a large-scale PSC with a surface area of 100 cm\({}^{2}\) (see Figure 12a) [75]. The device showed a PCE equal to 7.73%. This study serves as a benchmark for the fabrication of commercial PSC through thermal vapor deposition. The device maintained a uniform composition throughout the film with no presence of impurities and subsequently high solar cell performance. Figure 12c depicts consistent XRD patterns at different regions of the solar cell marked A, B, C, E, and D, as shown in Figure 12b.  
Figure 12: Low resolution images (**a**,**b**) of the 100 cm\({}^{2}\) PSC prepared by the thermal-vapor-deposited method. (**c**) XRD patterns at the marked regions A, B, C, D, and E [75].
## 2 Materials and Methods
### Methodology
#### 2.2.1 Data Collection and Preprocessing  
True-color images were used for the LULC classification. They were downloaded from Google Earth Pro 7.3.2 [28]. For the study areas, the software provided images from the DigitalGlobe's satellite constellation with a resolution of up to 30 cm [33]. Three of the twenty-one available images were selected to represent the study areas during different periods. These registers correspond to the dates 24 October 2018, 22 December 2015, and 27 October 2009.  
The images were chosen based on their quality, which allowed visual recognition of small-scale urban features, such as single trees or buildings. Additionally, cloud coverage, the presence of shadows; and the sharpness, brightness, and contrast observed in the images were defined as selection criteria. Images were discarded if cloud cover was higher than 20% of the total size. Moreover, the time interval between the selected images allows a gradual evaluation of LULC change during the last decade. Images recorded between October and December were preferred to minimize annual seasonality effects, specifically due to the variation of the canopy. Wet season takes place normally during this period, which leads us to assume that the vegetation cover is higher in comparison with the dry season.  
Figure 2: Flowchart of the high resolution classification (HRC) generation, edition, and validation.  
From the nine images, seven did not show any clouds, while the other two had percentages lower than 5% of the whole image.  
After zooming to the areas of interest, at an eye altitude of 800 m and reset using the option 'Tilt and Compass,' the image was exported by default as JPG without any label. Four placemarks were placed at the corners of the viewer as geo-reference points and were saved in KML format. The software QGIS version 3.4.4 [34] was employed to preprocess the images and perform the analysis of the LULC. Each JPG file was converted into the GeoTIFF raster format using the plug-in Georeferencer GDAL Version 3.1.9 based on the KML points as Ground Control Points (GCP) for georeferencing. Finally, the area of interest was clipped to its respective boundaries.  
In all cases, exporting images from Google Earth at that eye elevation derived a pixel resolution equal to 0.2 m. That value fits our needs to contrast specific urban features, such as small patches of vegetation or vehicles. Setting them to a lower eye elevation may lead to the same or even better pixel quality. However, an additional merging procedure is needed if the dimensions of the study area are larger than the ones of the downloaded file.  
Lines which represent streets and rivers were exported from OpenStreetMap (OSM) [35] as OSM files. These data were transformed into shapefiles in QGIS. Since the lines did not match exactly, the location of their respective areas in the images, they were manually edited. The width of a street was defined by buffering the lines into polygons. Subsequently, these buffer zones were transformed into raster files.
## 3 Materials and Methods
### Selection of Similar Neighboring PV Plants Based on Composite Similarity Index  
To enhance the accuracy of photovoltaic (PV) power output forecasts, it is vital to integrate information from adjacent PV plants as input features. This integration is predicated on the premise that these neighboring plants must exhibit a significant similarity with the target PV plant. A sophisticated approach involves constructing a composite correlation index that encapsulates both the irradiance and power sequence correlations across various PV plants, reflecting the degree of similarity in PV plant data over different time scales. The mathematical formulation used to calculate this composite correlation is given by:  
\[\phi_{\mathrm{i}}=\phi_{\mathrm{Rg_{i}}}^{2}+\phi_{\mathrm{Rd_{i}}}^{2}+\phi_{ \mathrm{P_{i}}}^{2} \tag{2}\]  
where \(\mathrm{d_{Rg_{i}}}\), \(\mathrm{d_{Rd_{i}}}\), and \(\mathrm{d_{P_{i}}}\) represent the correlations of the historical global irradiance Rg component, the diffuse irradiance Rd component, and the power sequence between the i th neighboring PV plant and the target plant, respectively. \(\phi_{\mathrm{i}}\) denotes the overall composite correlation for the i th neighboring PV plant relative to the target plant.  
To ensure comprehensive similarity, it is also critical to evaluate the amplitude of irradiance and power output. Thus, a composite distance metric is employed:  
\[\mathrm{d_{i}}=\mathrm{d_{Rg_{i}}^{2}}+\mathrm{d_{Rd_{i}}^{2}}+\mathrm{d_{P_{ i}}^{2}} \tag{3}\]  
In this formula, \(\mathrm{d_{Rg_{i}}}\), \(\mathrm{d_{Rd_{i}}}\), and \(\mathrm{d_{P_{i}}}\) quantify the distances pertaining to the global irradiance component Rg, diffuse irradiance component Rd, and power sequence between the i th neighboring PV plant and the target plant, respectively. \(\mathrm{d_{i}}\) represents the composite distance for the i th plant, with smaller values indicating a closer match in irradiance and power profiles.  
From these metrics, a composite similarity index \(\Psi_{\mathrm{i}}\) is calculated as follows:  
\[\Psi_{\mathrm{i}}=\frac{\phi_{\mathrm{i}}}{\mathrm{d_{i}}} \tag{4}\]  
This index measures the relative similarity between each neighboring PV plant and the target plant. After determining \(\Psi_{\mathrm{i}}\) for all neighbors, they are ranked in descending order of similarity. The top k plants are then selected to create a set of neighboring PV plants with the highest degrees of similarity to the target plant.  
This structured methodology not only systematizes the selection of relevant input features from similar PV plants but also substantiates the inclusion of such features in enhancing the precision of PV power forecasts.  
Figure 2: Short-term photovoltaic (PV) power forecasting model based on deep learning.
## 4 Experimental Results
### Experimental Setting  
Parameter settings: Apart from setting the kernel size to \(1\times 1\) in the fusion operation, the kernel size of all convolutional layers in the network is set to \(3\times 3\). The zero-padding strategy is used to maintain the same output feature size as the input. The number of convolution output channels in the feature extraction and fusion process is set to 64. In the MSFO module, the interleaved paths are set to 4, so the number of channels traversed by each path is 16. Our network is implemented with Pytorch and trained on NVIDIA RTX2080 ti GPU. The network training model use the ADAM optimizer [31] to optimize the network parameters, where \(\beta_{1}=0.9\), \(\beta_{2}=0.99\). The initial learning rate is set to \(lr=1\times 10^{-4}\). Every 30 training epochs, the learning rate is reduced by half. The batch size is 16, and the network is trained for a total of 60 epochs. The proposed method trains different networks according to different upscaling factors (e.g., \(2\times\), \(4\times\), \(8\times\), \(16\times\)).  
Training set: We follow the work in [23], selecting 110 images from the Middlebury Stereo Datasets [32] and MPI Sintel Datasets [33] to build our original dataset, in which 95 depth images are used as the original training samples and 15 depth images are used as validation samples. To make full use of these depth images, the images are randomly rotated by 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), and horizontally flipped. During the training phase, we crop the HR depth images into small squares of size \(256\times 256\) and remove the sub-images without depth information. As a result, we obtain 10,650 images for training. Finally, LR depth images of size \(128\times 128\), \(64\times 64\), \(32\times 32\), and \(16\times 16\) are generated by downsampling the HR images using bicubic interpolation with the given scaling factors of 2, 4, 8, and 16.