esa-sceva/satcom-chunk-collection huggingface dataset:
# Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks
## 4 Results  
In this section, we analyze and discuss the effect of delays on the memory-non-linearity trade-off by performing a range of experiments. Because individual IPC components each measure independent computational properties, and only some computations are useful for a task, we made use of the task overlap metric defined in Section 3.5. We first measured the task capacities of the NARMA-30 task (shown in the bottom part of Figure 5 and on the right hand side of Figure 6). Because we knew the task equation, we could constrain the search space accordingly, namely to a maximum degree of two, a maximum window size of 30, and a maximum lag of around 30 timesteps. The task capacity determined which capacities we considered useful, and could therefore be used to drastically constrain the search space for the reservoir IPC. Because we knew that the lags and basis functions for which the task capacity was zero were not relevant, we could exclusively measure the reservoir capacities that had a corresponding non-zero task capacity.  
Next, we measured the IPC of unoptimized DDNs and their identical ESN counterparts. With this comparison, we provided a baseline quantification of the effects of distributed delays on the IPC and NARMA-30 task performance. Here, the presence or absence of delays could be isolated as the only independent variable, since all other network parameters were identical for each ESN and DDN pair.  
We then present a comparison between ESNs and DDNs in terms of task performance in the NARMA system approximation task and the non-linear memory capacity of the optimized systems. For this experiment, the ESN and DDN models required separate hyperparameter optimizations, resulting in different network structures. The co-evolution of delays along with other network hyperparameters led to different optimal network structures than those of optimized ESNs. However, by comparing evolved DDNs and ESNs, we show improved optimization flexibility for DDNs.  
Lastly, we provide another empirical proof of the superior memory-non-linearity trade-off of DDNs compared to ESNs by training and evaluating them on the delayed-XOR tasks.
## 6 Simulation Results and Discussion
### Sensitivity Analysis  
The optimization process that enabled us to find out the optimal solution (PV/diesel/battery system) used average values such as the annual average solar radiation of 5.94 kWh/m\({}^{2}\)/day, the annual average wind speed of 3.78 m/s, a primary load demand of 76.94 kWh/day, an ad fuel cost of USD 0.6/L. However, in the real system the solar radiation and wind speed vary depending on the daily environmental conditions, the load demand varies dynamically, and the fuel cost fluctuates depending on the world's fuel market. Having this in mind, the effects of the variations of GHI, fuel price, and load consumption on the optimal PV/diesel/battery system were considered in the sensitivity analysis.
## 4 Results and Discussion
### Catalytic Hydrogenation of Maleic Acid  
The catalytic performance of the SCS-derived Ni-based nanocatalysts was studied, focusing on the hydrogenation of maleic acid in the liquid phase. It was found that, when freshly prepared Ni catalyst was immediately employed in the liquid phase hydrogenation of maleic acid, it was very active and the degree of conversion of maleic acid into succinic acid was 100%. However, when the same catalyst was used, after one day it was almost inactive (Figure 7). This implies that the catalyst's composition or structure (or both) changed and so did its adsorption properties. This reveals the necessity to investigate the reasons for the severe reduction in the catalyst's activity in the liquid phase hydrogenation of maleic acid. The term \"structural changes\" means the reduction of the catalysts' specific surface area and the alteration of their structural characteristics (i.e., porosity, specific volume, and average pore radius) due to the implied aging of the catalysts. As a result of the abovementioned changes, the number and nature of the active centers were altered as well as the adsorption energy of the surface.  
Figure 7: Influence of the effect of aging on the conversion of maleic acid in the Ni nanocatalyst produced by SCS (at 80 \({}^{\circ}\)C).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**OIs**} \\ \hline  & **O:Ni2+** & **O:Ni3+** & **O:NiOOH** & **Ni2+-O:Ni3+-O** \\
25 & 529.8 \(\pm\) 0.2 eV & 531.8 \(\pm\) 0.2 eV & 532.5 \(\pm\) 0.2 eV & 4.3 \\
50 & 529.7 \(\pm\) 0.2 eV & 531.7 \(\pm\) 0.2 eV & - & 1.8 \\
75 & 529.8 \(\pm\) 0.2 eV & 531.8 \(\pm\) 0.2 eV & - & 1.9 \\
100 & 529.8 \(\pm\) 0.2 eV & 531.9 \(\pm\) 0.2 eV & - & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Binding energy values of oxygen bonded in the NiO and Ni\({}_{2}\)O\({}_{3}\) states and their respective ratios.
## 3 Results and Discussion
### Raman Spectra of Reference Biological Macromolecules
#### 3.3.2 Human Serum Albumin  
The spectra of human serum albumin (HSA) measured using different instruments exhibited evident similarity (Figure 4b). Table S1 summarizes the assignments of Raman bands observed for this protein based on the references [32; 33; 34]. With a few exceptions, the position of individual bands in the HSA spectra varies slightly. The evident shifts observed in some peaks are due to different ratios of two or more closely spaced bands depending on the sensitivity and spectral resolution of the instruments. Three strong bands detected in the DXR SmartRaman spectrum at 1655, 1449, 1337, and 1003 cm\({}^{-1}\) are characteristic of vibrations of amide I, scissoring of methylene groups, C-H deformation, and ring breathing of phenylalanine (Phe), respectively [31]. The i-Raman Plus device showed almost identical wavenumbers for these bands, while for Ahura, the corresponding bands were slightly shifted towards higher frequencies. The spectra clearly show that this band is narrowest for DXR SmartRaman and i-Raman Plus devices and much broader for Ahura FirstDefender. For the laboratory instrument, the tyrosine (Tyr) doublet appeared in the spectrum of HSA as two well-resolved bands at 827 and 851 cm\({}^{-1}\)[32; 33; 34]. In the spectra obtained with both portable devices, this doublet appears unresolved, having the second component resembling a shoulder. Further, in the case of the SmartRaman device, the Trp indole ring vibration band at 866 cm\({}^{-1}\) is well-resolved [32; 33; 34], while in the spectra measured using portable devices, it was observed as a shoulder of the adjacent band at 853-854 cm\({}^{-1}\).
# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 4 Experimental Evaluation
### Evaluation Indicators  
The primary aim of this research paper is to present an effective algorithm for detecting and recognizing tiny objects in complex backgrounds, effectively balancing the trade-off between model accuracy and speed. Accordingly, three metrics, namely, accuracy, recall, and mean average precision (mAP), are employed to assess the model's accuracy. Additionally, frames per second (FPS), model size, and billion floating point operations (GFLOPs) are utilized as performance measures to evaluate the model's speed.  
The formulas for precision (P) and recall (R) are the following:  
\[\text{P}=\frac{TP}{(TP+FP)} \tag{3}\]  
\[\text{R}=\frac{TP}{(TP+FN)} \tag{4}\]  
In object detection evaluation, _TP_ represents the count of accurately predicted bounding boxes, _FP_ represents the count of incorrectly identified positive samples, and _FN_ denotes the count of hidden objects.  
The average precision (mAP) metric is a comprehensive measure of the model's performance by considering both precision and recall. It is computed by determining the average precision (AP) for each object category at a fixed intersection over the union (IOU) threshold and subsequently averaging these AP values across all categories. The mAP values are obtained by calculating the area under the precision-recall (P-R) curve for each category, where precision is plotted on the X-axis and recall is plotted on the Y-axis. The following formulas are utilized for the calculation of AP and mAP:  
\[\text{AP}=\int_{0}^{1}P(r)dr \tag{5}\]  
\[\text{mAP}=\frac{\sum_{i=1}^{\text{C}}AP_{i}}{\text{C}} \tag{6}\]  
Figure 4: Datasets; (**a**) AI-TOD dataset; (**b**) TinyPerson dataset.  
where \(p(r)\) denotes the precision-recall curve, and \(C\) represents the number of detection categories.  
To ensure a comprehensive and rigorous evaluation of the proposed algorithm, the evaluation metric [EMAIL_ADDRESS]:0.95 was employed. This metric computes the average mean average precision (mAP) at various intersections over union (IOU) thresholds, ranging from 0.5 to 0.95 in increments of 0.05 (specifically, at IOU thresholds of 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, and 0.95). Considering a range of IOU thresholds, this evaluation metric comprehensively assesses the algorithm's performance.
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 4 Results and Discussion  
This section presents findings based on the suggested methodology described in the previous section. The models were trained on all classes simultaneously contrary to the contemporary many one-versus-all classifications. Two deep-learning pre-trained models, ResNet50 and VGG19, were used for transfer learning in the land-cover classification of remotely sensed natural scenes. We trained both models with three different modes. In the first scenario, we trained the whole structure, initializing it with pre-trained weights. In the second scenario, we used these networks as feature extractors and only trained the fully connected layers for classification. We trained only the last three layers and the parameters for the rest of the network were frozen. Finally, we trained these models from scratch, initializing the models with random weights instead of using pre-trained weights. This helped us to judge the effectiveness of the transfer-learning technique for remote-sensing image scene classification. In this way, we performed these experiments for the comparison of different training modes. We performed all experiments with 100 epochs and a learning rate of 0.0001. The train-test split was 80:20 and the training set was further divided into training and validation subsets with the same ratio. This means that 12,800 images for training, 3200 images for validation, and 4000 images for testing were used in all experiments.
## 5 Simulation Results
### Dataset Feature Importance Analysis  
Feature importance analysis, as shown in Figure 5, indicated that the pseudo-range (PD), Receiver Time (RX), and Time of the Week (TOW) were the most dominant features used in the detection process of a GPS spoofing attack; they generally showed higher scores than others. Those features in the top rank would presumably correspond to the most important spatial and temporal information that was sensitive to the inconsistencies inherent in the GPS spoofing scenarios. This indicates that the signal timing and satellite-receiver distance metrics were key parameters for distinguishing between authentic and spoofed GPS signals.  
Other features, including the CP, DO, and TCD, were likely to indicate that Doppler- and phase-related aspects had moderate contribution, giving secondary importance to the space-time features. These features should have captured the subtleties in satellite motion or changes in frequency that contributed appropriate supplementary information toward more accurate prediction by the model.  
In contrast, features relating to correlators, such as the PC, LC, EC, and in-phase/quadrature components, like the PIP and PQP, had relatively low importance scores, indicating their limited usefulness for this task of detection. It is intuitive that those with low importance may have contributed less in value above what was already captured by the more important temporal and spatial attributes. The distribution of importance scores over all features gives an indication that there was no single metric dominating the model's decisions. Rather, it was from the combination of several moderately important features that the model could benefit since, together, they captured complex spoofing attacks better than any feature alone.  
Figure 5: Feature importance scores.
## 3 Factors Affecting Oburation Protocol in Teeth with Periapical Bone Lesions  
In the best situation, teeth with periapical lesions should be observed for 12 months as the minimum period after the therapy before assessing success and functional tooth stability for the subsequent prosthetic treatment [30]. First, one should treat any inflammatory periapical lesions conservatively and plan apical surgery if the lesion persists or worsens after the treatment [31]. In the absence of anatomical-physiological constriction, chronic apical periodontitis can be healed by decontaminating the endodontic space and creating biomimetic environments for appropriate sealing [32].  
In order to analyze the factors that influence the quality of the three-dimensional obturation of root canals, they must be divided into two main groups. The first group of factors are the pre-operative ones, which include points such as adequate access to the apical area; the possibility of effective isolation with a rubber dam; the elimination of cases with common cracks and fractures, where the complications are foreseen even before the treatment is carried out; and the possibility of precise postoperative recovery, i.e., the presence of a ferrule and tissues that can bear an effective load in the chewing process.  
The second group of factors are the operative factors that directly affect the treatment result. These include the effective and comprehensive treatment of the endodontic space, both mechanically and chemically, using an evidence-based clinical protocol [33; 34], the selection of the obturation technique according to the condition of the apical third, the presence or absence of resorption processes, as well as the selection of an appropriate sealer with optimal maximum sealing qualities and the subsequent impact on the periapical zone by releasing ions, which relies on the possibility of isolating the periodontium from the oral environment. Chemomechanical preparation should sterilize the endodontic space and condition the radicular dentin walls for good adhesion of the sealer to them. Preparing the apical part and activating the irrigant's penetration into the dentin tubules with ultrasound are crucial to achieving satisfactory postoperative treatment results [35].  
The quality of three-dimensional obturation depends on two closely related points: the effective elimination of the smear layer and the effective adaptation of the sealer chosen for obturation. This provokes more in-depth scientific research to ascertain the exact irrigation protocol for the selected sealer with which the root canals will be obturated [36].
## 3 Numerical Analysis Results and Discussion under Static Loading
### Lateral Displacements at Wall Facing
#### 3.1.2 Effects of Surcharge Loads on Lateral Displacements at the Wall Facing  
The bridge and roadway approach backfill dead loads, general traffic loads, and railway loads are modelled as surcharge loads and applied uniformly on the top of the bridge abutment. In general, the _dx_ at the wall facing increases as the surcharge load increases. The effects of the applied surcharge loads on the lateral displacement at the wall facing are shown in Figure 8. Here, the lateral displacement ratio is computed as \(dxR=dx_{B,C}/dx_{A}\), where \(dx_{B,C}\) is the lateral displacements at the wall facing for models that are subjected to general traffic loadings and railway loadings (Case 1B, 2B, 3B and Case 1C, 2C, 3C), and \(dx_{A}\) is the lateral displacements at the wall facing at the end of the bridge construction (Case 1A, 2A, 3A).  
The results plotted in Figure 8a show that Case 1B had a maximum increase of 3.7 times when the additional load of 30 kPa (traffic load) was applied on the bridge after the bridge construction and that Case 1C had a maximum increase of 19 times when the additional load of 50 kPa (railway load) was applied on the bridge after the bridge construction. Here, the effects of the applied loading for Case 1A-C are great at 2.80 m above the base of the wall. The magnitude of \(dxR\) for Case 1B-C may seem great because it is relative to the dx at the end of the bridge construction (Case 1A) which is almost zero (0.002% \(dx/H\) max) (refer to Figure 7). Conversely, the magnitudes of \(dxR\) are about similar for Case 2A-C, which are between 1.06 and 1.54 (Figure 8b), and Case 3A-C, which are between 1.10 and 1.48 (Figure 8c). However, the \(dxR\) trends are opposite. The effects of the applied loads for Case 2A-C are great at the top of the wall facing, while the effects of the applied loads for Case 3A-C are great at the base of the wall. The trends of \(dxR\) shown by the models are very similar to the \(dx\) behavior shown in Figure 7. Here, the magnitude of \(dxR\) are quite small because it is relative to the \(dx\) at the end of the bridge construction; consider Case 2A (0.18% \(dx/H\) max) and Case 3A (0.03% \(dx/H\) max), which are a lot higher than Case 1A (0.002% \(dx/H\) max).
## 1 Introduction
For example, traffic congestion in one region could cause congestion in adjacent regions with comparable traffic change patterns. However, these changes in similar patterns could vary over time, indicating diverse spatial correlations. Spatiotemporal correlations between metropolitan regions are complicated within a given time frame, and precise traffic flow prediction necessitates extracting the spatiotemporal correlations of different locations at various time intervals. We suggest a seq2 seq model called multiple random walks on graphs BiAttenn (MRWG BiAtten), which combines multiple random walks on graphs and bidirectional spatiotemporal attention mechanisms for traffic flow prediction under abnormal events, to tackle these difficulties. The contributions of the manuscript are summarized as follows:  
1. A traffic flow-prediction model based on Transformer is proposed with the applied encoder-decoder structure to integrate the graph random walk and spatiotemporal bidirectional attention mechanisms, in light of the impact of traffic accidents and congestion on urban traffic conditions. 2. A positional attention module is introduced allowing simultaneous concentration on both global and local spatial links by aggregating the global spatial correlations on local spatial features. 3. To enable the extraction of intricate and dynamic temporal dependencies, a time attention module is proposed to model the interdependencies between the long-term and short-term temporal features. 4. Two baseline datasets, TaxiNYC and BikeNYC, are used in the experiments for performance comparison to verify the superiority of the proposed prediction model. The remainder of the paper is organized as follows. Section 2 introduces the problem formulation, and Section 3 describes the proposed traffic flow-prediction model in detail. In Section 4, in-depth experiments with public traffic datasets are carried out to validate the performance of the proposed model. Conclusions are provided in Section 5.
# Phase Alignment of an Array Optical Telescope System Using Balanced Detection
## 2 Theories and Design  
### The Structure of Phase Compensation  
To solve the phase deviation caused by the tilt of the optical axis of the multi-aperture receiving system, the structure of MZI was innovated, which is a high-precision detection method with a simple structure proposed. An array optical communication system based on balanced coherent detection was designed to evaluate the communication quality before and after phase compensation. It is shown in Figure 1.  
The transmitter generates CSRZ-DPSK optical signal by LiNbO3 MZM. The signal was transmitted to atmospheric space through a collimator with a diameter of 80 cm and received by a multi-aperture system. The diameter of both receiving sub-apertures were 40 cm, and the spatial optical signal was coupled into two single-mode fibers. Then, the signal was transferred to the balance detector, and the phase error was compensated by a phase shifter, to reduce the error rate and ensure the accurate demodulation of the digital signal. The key technology of the system is to integrate the phase deviation of the sub-aperture into the adjacent symbols of the DPSK demodulation, and to improve the sensitivity of the system by using the autodyne balance detection. The difficulty lies in the construction of innovative MZI to meet the input condition of the balanced detector, wherein the phase difference of the input optical signal from the same source is \(\pi\).
## 3 Results and Discussion
### Acute Toxicity Assays
#### 3.3.2 Chlorophyll Fluorescence  
PAM fluorometry has been established as a simple and rapid screening method to determine a compound's toxicity on photoautotrophs [51]. We therefore used this method to obtain additional information complementing the results of the growth inhibition assays. Figure 3 depicts the results of the two tests, displaying the concentration-dependent growth inhibition as well as the maximum photochemical quantum yield of photosystem II. Table 4 provides the EC\({}_{50}\) values calculated from both assays.  
These results demonstrate a clear negative correlation between growth inhibition and photosynthetic performance. For instance, NOECs for both parameters towards _T. obliquus_ are estimated to be 0.33 mg L\({}^{-1}\) for [P\({}_{1888}\)][HNA] and 0.52 mg L\({}^{-1}\) for [P\({}_{66614}\)][HNA]. Intermediate concentrations likewise displayed similarly pronounced impacts on both parameters. This signifies that the tested ILs simultaneously affect PSII and inhibit algal growth to equal extent, which is also reflected in the comparable EC\({}_{50}\) values of both tests (Table 4): EC\({}_{50}\) values of e.g., [P\({}_{66614}\)][HNA] were equal among the same algae species, with values of 1.76 \(\pm\) 0.17 mg L\({}^{-1}\) for growth inhibition vs. 1.81 \(\pm\) 0.03 mg L\({}^{-1}\) for Fv/Fm of _T. obliquus_. The same pattern between the two parameters for _T. obliquus_ was reported by [49] at approximately 60 \(\mu\)g L\({}^{-1}\) for IL 1-decylpyridinium bromide. In contrast, no correlation between growth inhibition and Fv/Fm has been found for explicitly non-photosynthetic inhibitors. In this respect, [PERSON] et al. [52] reported on the behavior of herbicide Alachlor: it did not affect the chlorophyll fluorescence of _R. subcapitata_, even though it displayed the highest growth inhibition of the tested compounds.  
EC\({}_{50}\) values for [N\({}_{1888}\)][HNA] were equal regarding _R. subcapitata_ and similar for _T. obliquus_, whereas a significantly stronger impact of [P\({}_{1888}\)][HNA] on chlorophyll fluorescence was observed at lower concentrations than its effect on growth inhibition. Nevertheless, the overall strong relation between both parameters qualifies the fast and non-invasive PAM fluorescence measurement as a suitable first screening of IL toxicity on photoautotrophs.