esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications
## 7 Discussion
In such a case, the expected inter-vehicle target distance, the bounds, and the resource consumption can be jointly adapted, depending on the resources availability and the road situation. However, more detailed studies on such an adaptive approach are left for future work. Furthermore, a \"less sensitive\" approach to message generation can be also considered, similar to the one proposed for cooperative perception mechanism [27], where the information update is sent only if a significant change in parameters is detected (e.g., a change in acceleration, speed, or position by more than a predefined value). Another option to maintain stability within the platoon may be a cooperative selection of the communication intervals. In such a case, the transmission intervals could be selected jointly by a central entity (e.g., co-located with the platoon leader), and then distributed to all platoon members. However, this approach would require a knowledge of the parameters of all platoon vehicles in the central entity, as well as a very reliable information distribution mechanism. Such information could be distributed in a similar way to the cooperative perception mechanism, where each platoon member would also include in its message the information on the known other members' parameters. Additionally, messages could be filtered before being relayed, e.g., based on their age or their relevance in the adaptation process, similarly to the approach proposed in [32]. The proposed approach definitely requires further evaluation in a more sophisticated scenario. The considered system model assumed perfect communications between the platoon vehicles, with only some latency being introduced. In a real platooning scenario, such an assumption is certainly not valid as communication outages may occur, especially when considering the communication links between the leader and the vehicles located at the tail of the platoon. Therefore, the next step in this work should be to extend it with a more realistic communication model, accounting for packet drops and temporary communication outages. Finally, more realistic engine and acceleration models can be considered, accounting for the so-called engine lag and different vehicle acceleration capabilities, depending on its velocity (e.g., using the linear decay model).
# Deep-Learning-Based Identification of Broad-Absorption Line Quasars
###### Abstract  
The accurate classification of broad-absorption line (BAL) quasars and non-broad-absorption line (non-BAL) quasars is key in understanding active galactic nuclei (AGN) and the evolution of the universe. With the rapid accumulation of data from large-scale spectroscopic survey projects (e.g., LAMOST, SDSS, and DESI), traditional manual classification methods face limitations. In this study, we propose a new method based on deep learning techniques to achieve an accurate distinction between BAL quasars and non-BAL quasars. We use a convolutional neural network (CNN) as the core model, in combination with various dimensionality reduction techniques, including principal component analysis (PCA), t-distributed stochastic neighborhood embedding (t-SNE), and isometric mapping (ISOMAP). These dimensionality reduction methods help extract meaningful features from high-dimensional spectral data while reducing model complexity. We employ quasar spectra from the 16 th data release (DR16) of the Sloan Digital Sky Survey (SDSS) and obtain classification labels from the DR16Q quasar catalogues to train and evaluate our model. Through extensive experiments and comparisons, the combination of PCA and CNN achieve a test accuracy of 99.11%, demonstrating the effectiveness of deep learning for classifying the spectral data. Additionally, we explore other dimensionality reduction methods and machine learning models, providing valuable insights for future research in this field.  
broad absorption line quasars; non-broad absorption line quasars; deep learning; convolutional neural networks +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000][PERSON]  
0000-0002-0002-3880][PERSON]  
0000-0002-3880][PERSON]
## 5 Deep Reinforcement Learning-Based Caching Solution
### DRL-Based Caching Algorithm
```
1:1.Initialize:
2:\(s_{t^{x}_{y}}\in\mathcal{S}\), \(a_{t^{x}_{y}}\in\mathcal{A}\), \(\alpha\), \(\gamma\), the size of minibatch \(\mathcal{G}\) and the memory pool \(\mathcal{N}\)
3:Parameters \(\theta_{t^{y}_{y}}\) and \(\theta_{t^{y}_{y}}^{-}\)
4:Parameters \(\Delta E_{TD}(t^{x}_{y})\)
5:Parameters \(V\left(s_{t^{y}_{y}};\theta_{t^{y}_{y}}^{x},\beta_{t^{y}_{y}}^{x}\right)\), \(A\left(s_{t^{x}_{y}},a_{t^{x}_{y}};\theta_{t^{x}_{y}},a_{t^{x}_{y}}^{x}\right)\),
6:2.Learning:
7:for\(t^{x}_{y}\in K_{c}\)do
8: Choose the \(\pi(t^{x}_{y})\) by \(\varepsilon-greedy\) policy
9: Observe environment, evaluate and estimate \(s_{t^{x}_{y}}\)
10: Perform \(a_{t^{x}_{y}}\) according to \(\pi\) and observe feedback
11:while\(t^{x}_{y,\varepsilon}\in T_{s}\)do
12: Observe: environment and communication state
13: Calculate: \(\mathcal{H}(t^{x}_{y})\), \(\left|h_{t}\left(t^{x}_{y,z}\right)\right|^{2}\), \(\left|h_{t_{b}}\left(t^{x}_{y,z}\right)\right|^{2}\), \(\mathcal{R}_{MEC}(t^{x}_{y,z})\), \(\mathcal{R}_{MBS}(t^{x}_{y,z})\)
14: Calculate: \(Q_{MEC}\left(t^{x}_{y,z}\right)\),\(Q_{B}\left(t^{x}_{y,z}\right)\)
15: Obtain: \(\overline{Q}_{AV}\left(U\left(t^{x}_{y,z}\right),N\left(t^{x}_{y,z}\right) \right)\), \(\mathcal{Q}_{MEC}\left(t^{x}_{y,z}\right)\)
16:endwhile
17: Calculate: \(\mathcal{R}\left(s_{t^{x}_{y}},a_{t^{y}_{y}}\right)s_{t^{x}_{y+1}}^{x}\)
18: Store: transition \(<s_{t^{x}_{y}},a_{t^{y}_{y}},\mathcal{R}(t^{x}_{y}),s_{t^{x}_{y+1}}>\)
19:if\(ERP_{t}>N_{max}\)then
20:for\(j\in N_{F}\)do
21: Calculate: \(\rho_{PER}^{i}\), sort by SumTree, and sample \(<s_{t^{x}_{y}},a_{t^{x}_{y}},\mathcal{R}(t^{x}_{y}),s_{t^{x}_{y+1}}>\)
22: Update: \(\triangle E_{TD}(t^{x}_{y+1})\) and function parameters
23: Update: \(\theta_{t^{x}_{y+1}}\) with SGD algorithm
24: Update: \(Q\), \(V\), \(A\), \(\theta_{t^{y}_{y}}^{-}\), and network parameters
25:endfor
26:endif
27:endfor
```  
**Algorithm 1** Prioritized experience replay Dueling Double DQN IoV QoE Caching (P3 DVQC)  
Figure 3: The proposed framework.
# Effects of Doped Hole-Transporting Layers on Perovskite Solar Cell Performances
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **As Prepared** & **1 Day** & **2 Days** & **3 Days** & **4 Days** & **5 Days** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Undoped & Before Microwave & 12.69 & 9.01 & - & - & - & - \\  & After Microwave & 13.20 & 10.07 & - & - & - & - \\  & Before Microwave & 14.44 & 12.10 & 11.88 & 11.57 & 11.37 & 10.71 \\  & After Microwave & 14.61 & 12.84 & 12.54 & 12.21 & 11.60 & 11.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The PCE change according to post-annealing treatment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conditions** & **VOC (V)** & **JSC (mA/cm\({}^{2}\))** & **FF (\%)** & **PCE (\%)** \\ \hline Undoped (0.09 cm\({}^{2}\)) & 0.963 & 20.40 & 69.32 & 13.62 \\
2 wt\% DIO (0.09 cm\({}^{2}\)) & 0.958 & 22.40 & 70.04 & 15.03 \\
2 wt\% + Microwave (0.09 cm\({}^{2}\)) & 0.990 & 21.35 & 70.77 & 14.96 \\ Undoped (0.36 cm\({}^{2}\)) & 0.849 & 19.19 & 61.15 & 9.96 \\
2 wt\% DIO (0.36 cm\({}^{2}\)) & 0.872 & 21.11 & 65.97 & 12.15 \\
2 wt\% + Microwave (0.36 cm\({}^{2}\)) & 0.869 & 21.25 & 66.14 & 12.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Small and large-area devices’ best performances according to different annealing methods. for 0.09 cm\({}^{2}\) cell and 4.9 h for the 0.36 cm\({}^{2}\) cell), as the size of the cell increased the extent of the lifetime enhancement through doping increased (i.e., 1.6 times for the 0.09 cm\({}^{2}\) cell and 1.8 times for the 0.36 cm\({}^{2}\) cell). Since the device lifetime was very short when the device was exposed to solar simulator light without encapsulation and to mimic a more realistic situation, the device storage method was changed. The devices were stored at a relative humidity of 35% without device encapsulation; however, Figure 7c,d show the PCE changes as a function of time for the devices stored in scattered light (Figure 7c) and in the dark (Figure 7d). Figure 7c shows the PCE changes for the 0.09 cm\({}^{2}\) cell data; LT80s were dramatically increased compared with the devices exposed to the illumination with the same device area (i.e., Figure 7a). The LT80 for un-doped cell was 15 h and that for 2 wt% DIO-doped cell was 78 h. The lifetime enhancement for doped device was about 5.2 times compare that for un-doped device. Figure 7d shows the PCE changes for 0.36 cm\({}^{2}\) cell data, the LT80 for un-doped cell was 192 h and that for 2 wt% DIO-doped cell was 540 h. The lifetime enhancement for doped device was about 2.8 times compare that for un-doped device. From these experiments, it could be concluded that the differences in the lifetime between un-doped and doped devices decreased when the devices were stored at the dark environment. Figure 7: _Cont._
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 7 Experimental Evaluation
### Detection of Sybil Nodes in the Dynamic Scenario  
When simulating Sybil attacks, we allow attack clients to access and leave APs within a certain number of times. Attack clients forge multiple nodes and periodically move in their location. Similarly, we make other clients move randomly, and the attacker also makes periodic dynamic attacks at a specific time. We collect a total of 12 datasets each for 10 s. We calculate the Euclidean distance between each feature point and all other points to get the \(\varepsilon\)-distance set and then sort them in ascending order. We use a scatter plot to show the \(\varepsilon\)-distance trend and finally determine the value of the radius MinPts based on this. Finally, we get the optimal clustering parameters (0.5, 30). At this point, the clustering effect of attacks is most obvious, and according to the dataset, we can see that the clustering clusters of motion features of a single client will not exceed 120. Then, we set the value as a threshold. When the cluster is above the threshold, it is determined that the node in the cluster is the attacking node. The number of types of motion in each dataset is fixed.  
Therefore, we also increase the variety of clients in the environment to move in different ways and increase the number of fake attack nodes, as shown in Figure 13. Experiments show that the more attack nodes, the greater the density of their clusters, so the accuracy of attack detection increases  
Figure 12: Different mobile clients in the environment.  
with the number of attack nodes. Especially when there are five clients moving in the environment and fewer than 250 forged nodes, the overall average detection efficiency is 99.5%. This is because attack nodes forged by the actual attacker fluctuate within a small area and cannot be connected to other types of clusters. The attack detection rate remains high despite the increase in attack nodes. As the number of mobile clients increased to 9, 11 and the number of forged attack nodes was less than 200, the algorithm could not cluster all the nodes accurately, but still could detect a large number of simulated nodes with the detection rate still above 75%. However, with the increase of simulated attack nodes, some large density clusters will swallow many small clusters. Algorithms are easier to cluster into one. This misjudgement makes the detection accuracy drop acutely.  
In conclusion, the dynamic detection algorithm can maintain a high detection accuracy for less than 250 attacker forged nodes and meanwhile can achieve an average detection accuracy of 99.5% when there are less than five kinds of motion in the environment.
# AxP: A HW-SW Co-Design Pipeline for Energy-Efficient Approximated ConvNets via Associative Matching
## 3 Co-Design Pipeline: Concept and Methodology
### Approximation Pipeline (AxP)
#### 3.4.2 APMA Engine  
Once the weight-set of the ConvNet model has been discretized across a lower number of clusters, the second stage encompasses the approximate pattern matching analysis (APMA) (see Figure 4). APMA takes as input the model processed by the clustering engine and returns the approximation bits (_Abit_) parameter, which is the number of bits used to implement the approximate matching function for the retrieving of the pre-computed results stored in the associative memory of the processing element. The main objective is to minimize the _Abit_ while ensuring an accuracy drop lower or equal to the user-defined constraint.  
The target data formats are the single and half-precision floating-point defined within the IEEE 754 standard. Figure 6 reports the single-precision floating-point format, composed of a sign bit, 8 bits for the _exponent_ and 23 fraction bits. During the approximate matching, the least significant bits (LSB) are obfuscated, using the most significant bits (MSB) as the search key. For instance, a possible approximate matching schema makes use of 13 MSBs, pruning 19 fraction bits. This mechanism replaces the exact matching between current input patterns (weight and activation) and the pre-computed patterns in the associative memory, increasing the hit rate at the cost of a certain accuracy drop. It is worth noticing this mechanism can be regulated with the _Abit_ knob, which affects the memory size, the energy efficiency, and the accuracy drop.
## 4 Regulation of the CA Production in _S. clavuligerus_
Although both studies reported an increase of 5.2- and 1.6-fold on CA production compared to wild-type strain, respectively, the titers are too low to be considered in a large-scale process. The summary of studies reporting the highest CA titers using _S. clavuligerus_ engineered strains is presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strain** & **Operation Mode** & **Strain Intervention** & **Supplement** & **Titers (g\(\cdot\)L\({}^{-1}\))** & **Ref.** \\ \hline _S. clavuligerus_ IDG3 & Fed-batch & \multicolumn{2}{c}{Overexpression: _cas2_, _caR_, _claR_} & Glycerol trioleate & 6.690 & [95] \\ _S. clavuligerus_ M3-19 & Batch & \multicolumn{2}{c}{Reporter gene neo fused} & - & 4.330 & [96] \\ _S. clavuligerus_ NEO & Batch & \multicolumn{2}{c}{Reporter gene neo fused} & - & 3.260 & [96] \\ _Streptomyces sp._ & \multicolumn{2}{c}{downstream of _claR_} & \multicolumn{2}{c}{H\({}_{2}\)O\({}_{2}\)/Activated} & \multicolumn{2}{c}{0.649/0.683} & [119] \\ _Mul-NRC77_ & \multicolumn{2}{c}{Fed-batch/Batch} & UV mutagenesis & - & 0.500 & [120] \\ _S. clavuligerus_ 70 & Batch & \multicolumn{2}{c}{UV mutagenesis} & \multicolumn{2}{c}{5.520 (OR/pCAS1)} & \multicolumn{2}{c}{[97]} \\ _S. clavuligerus_ OR & Batch & \multicolumn{2}{c}{and _cas1_, _caR_ and _claR_} & - & 6.010 (OR/pCCAR- & [97] \\  & \multicolumn{2}{c}{overexpression} & \multicolumn{2}{c}{CLAR)} & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies reporting the highest CA production with engineered _S. clavuligerus_ strains. Despite _S. clavuligerus_ being the strain commonly used in the large-scale process of CA production, there are other CA-producer _Streptomyces_ species, which might be further studied and possibly engineered.
## 3 Results and Discussion  
### Preparation and Characterization of Carbon Quantum dots-MSN, Pyr@M15, Pyr@M100, and Pyr@M200  
Different sizes of carbon quantum dots-MSN were synthesized by varying the proportion of sodium hydroxide and water, i.e., the pH of the solution, as shown in Table 1. It was found that the particle size increased when the pH of the solution increased, which was consistent with [PERSON]'s study [8]. The TEM images in Figure 2 show that the carbon quantum dots-MSN nanoparticles exhibited uniform and regular spherical morphology, with a relatively smooth surface. Statistical analysis showed that the average particle size of carbon quantum dots-MSN was about 15, 100, and 200 nm, respectively. After pesticide loading, Pyr@M had similar structures, sizes, and dispersion with carbon quantum dots-MSN. Herein, the pesticide loading process did not change the size of nanoparticles, since the pesticide entered the mesoporous structure of MSN, as in our previous report [29; 38; 39]. In addition, it was shown in Figure S1 that Pyr in the technical material (TC) had an irregular crystal structure and the particle size was about 3 \(\upmu\)m, much larger than the nanoparticles.  
The FTIR spectra of carbon quantum dots-MSN with different particle sizes and Pyr@M are shown in Figure 3. The 1090 cm\({}^{-1}\) broad absorption band was found in both carbon quantum dots-MSN and Pyr@M, which was attributed to the characteristic Si\(-\)O\(-\)Si (siloxane) stretching vibrations. As seen in Figure 3b, the 2950, 1700, and 1560 cm\({}^{-1}\) broad absorption band found in Pyr were attributed to the characteristic -OCH\({}_{3}\), C=O, and C-N stretching vibrations, respectively. This was also observed in Pyr@M15, Pyr@M100, and Pyr@M200, indicating the successful loading of Pyr into the carbon quantum dots-MSN.  
Figure 4 shows the nitrogen adsorption and desorption isotherms of carbon quantum dots-MSN and Pyr@M. It was seen that all of the sorption isotherms were of type IV isotherm, indicating that all samples had an ordered mesoporous structure. The mesoporous structure characterization of the prepared nanoparticles are summarized in Table 2. For carbon quantum dots-MSN, the specific surface area, pore volume, and pore diameter decreased as the particle size decreased. When the size of quantum dots-MSN reached 15 nm, the specific surface area decreased to 342.09 m\({}^{2}\)/g. After Pyr loading, the specific surface area, pore volume, and pore diameter reduced, as expected. For Pyr@M15, the specific surface area continued to decrease to 63.12 m\({}^{2}\)/g. Different sizes of carbon quantum dots-MSN were obtained by changing the pH of the solution, but the size of the pore structure in the carbon quantum dots-MSN decreased with the particle size.
# Fluorescence Masking Based Multifunctional Quantum Dots+
## 3 Results and Discussion
### Testing Protein-Ligand Interaction with and without Inhibitor  
#### 3.3.1 With Biotimulated ATP  
A 100 nM Sav-QD concentration was chosen initially as it was providing the highest fluorescence. Different concentrations of biotin-ATP were added to the spotted Sav-QDs to form attachment points for HSP90\(\alpha\). The concentrations of 1, 5, 10, 50, 100, 500, 1000, and 5000 nM were chosen because each QD particle has around 5 to 10 streptavidin molecules attached to it (this value is from the company's description of the product). After spotting, 1-5 of the streptavidin molecules will be available for attachment to the biotinylated ligand (biotin-ATP). The rest are unavailable as they are either hidden (assuming that, after spotting, only the top half of the QD sphere is available for binding) or are difficult to access (due to a steric hindrance between the protein molecules that will attach). Hence, 100 nM of ten QD spots correspond to around a 1000 to 5000 nM streptavidin binding capacity on each pad.  
The binding of recombinantly synthesized and purified HSP90\(\alpha\) via biotinylated ATP to the Sav-QDs led to the masking of its fluorescence in a concentration-dependent manner,  
Figure 2: **(A)** Sav-QDs of 10, 20, 40, 50, 60, 80, and 100 nM concentrations which are spotted and blocked. **(B)** The fluorescence intensity of spotted Sav-QDs is concentration-dependent and the blocking buffer does not cause any significant reduction in the signal. The mean fluorescence signal (signal mean–background mean) is plotted against different concentrations of Sav-QDs.  
as shown in Figure 3. There was a complete masking from a 500 nM concentration of HSP90\(\alpha\) and around 1 nM, there was signal saturation for a lower concentration of HSP90\(\alpha\). Hence, for further experiments, a concentration range of 1-500 nM will be better suited. The concentration-dependent masking of the fluorescence intensity presented an EC\({}_{50}\) value of 15.2 nM. The sensitivity of the assay lies nearby this value.
# Assessment of Ecosystem Services across the Land-Sea Interface in Baltic Case Studies
## 3 Results  
### Application of the Spatial Habitat Typology  
The application of the spatial habitat typology in the three CSS showed that it was generally applicable in Germany as well as Lithuania. Geodata for CLC types, WFD water body types, and HD habitats were easily accessible from European and national geoportals, but the years from which data was available differed between CSS. To obtain data on marine sediments and vegetation, which were needed for the spatial definition of the inner and outer coastal waters, was more difficult. While they are mostly available for open coastal waters, information on the spatial distribution of sediments in shallow inner coastal waters is often hampered by sampling and interpolation methods [50]. Therefore, geodata for sediments and vegetation cover for inner coastal waters were obtained through direct contact with persons from regional authorities and research institutes. Geospatialinformation on emergent vegetation (reed belts) and sandy beaches had to be mapped manually from satellite images.  
For the application of the spatial habitat typology to the Lithuanian CSS, the German water body classification had to be applied to the Lithuanian water bodies. As it is based on environmental parameters, such as depths and salinity, it was easily transferable. Transitional waters (LIT-004 to LIT-006) and the heavily modified waterbody Klaipeda Strait (LIT-001) were included in the inner coastal waters, and coastal waters (LIT-002 and LIT-003) were classified as open coastal waters. Almost all CLC types that were found in the terrestrial part of our CSS were covered by the spatial habitat typology. Only the CLC type 'Land principally occupied by agriculture' (243) had to be transferred to the more general type 'Heterogeneous agricultural areas' (240).  
Out of the 41 habitats included in the spatial habitat typology and following Baltic ESP Matrix, 38 were found in the three CSS. Since this study is focused on the development of a spatial ES assessment approach for land and sea that can support coastal and marine policy, our focus is on natural and semi-natural areas. Consequently, for the purpose of this study, we grouped settlement related CLC types into one category (i.e., artificial surfaces), which will not be further considered in the following ES assessments. Figure 2 lists the remaining 29 habitats and shows their spatial distribution in the CSS. In addition to the 29 distinct habitats, we added three general habitat types called 'Beach, dunes, sands (other) (3110)', 'Coastal Lagoons and estuaries (other) (5210)', and 'Open coastal waters (other) (5230)'. They were used in case no further differentiation of the CLC types was possible. Together, they make up less than 1% of the studied area and are mostly negligible. The Lithuanian CSS forms an exception, as around 7% of the terrestrial area was classified as 'Beach, dunes, sands (other)'.  
Figure 2: Spatial distribution of ecosystem types in the study areas Schlei, Greifswald Bay, and Curonian Lagoon (left to right).