esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Tier Heterogeneous Beam Management for Future Indoor FSO Networks
## 6 Conclusions  
In this paper, we provide a thorough overview of the requirements and considerations for a novel multi-tiered heterogeneous beam management strategy that promises to: (a) improve traffic distribution across technologies; and (b) improve initial access time of a steerable indoor FSO network. In particular, we introduce a high level beam refinement protocol that accounts for device characteristics and link reliability before moving to a tier with higher directionality. The protocol also aims to improve indoor FSO acquisition time by taking advantage of the unique characteristics of directional technologies operating at different tiers. To evaluate the relative impact of various system parameters, we developed a qualitative analysis framework for the proposed MTH beam management strategy. This framework was used to demonstrate tradeoffs in the design decisions and derive an optimization analysis that minimizes the worst-case search time in a three tier implementation. While the presented evaluation is intended primarily for relative comparison, the analysis motivates a tighter coupling and integration of various directional communication technologies within the future wireless communications ecosystem.  
Conceptualization, M.B.R., T.D.C.L. and M.H.; Formal analysis, M.B.R.; Writing--original draft, M.B.R.; Writing--review & editing, T.D.C.L. and M.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Conclusions  
Changes in moisture content, crust and crumb color, specific volume, baking loss, total phenolic content, antioxidant activity, and textural properties of bread were studied with respect to the addition of FPH, PHP, FGS, and GSP to wheat flour, and the results showed that the addition of by-products affected the quality characteristics of the bread.  
Figure 5: Results of the sensory analysis of the control and enriched breads. Crust color (**a**), crumb color (**b**), chewiness (**c**), flavor (**d**), taste (**e**), and overall liking (**f**). Values of each parameter with the same letters were not significantly different (\(p>0.05\)).  
The addition of PHP resulted in an increase in total phenolic content and antioxidant activity; however, the specific volume showed the lowest values among the other breads. Although PHP had a decreasing effect on the values of the specific volume of the bread samples, it was found that the overall quality characteristics were not negatively affected. The bread with FGS in the crust and crumb had the lowest value for color change for all additive levels. The lower hardness and chewiness and higher cohesiveness led to the PHP and FPH breads being of good quality. The current results show that most of the bread quality characteristics depended on the level of the additive used, and thus, it is important to control the amount of additives in the bread. The analysis of all these data suggests that pistachio hull powder is an alternative functional ingredient that can be used together with wheat flour for bread making due to its high fiber content and richness in antioxidant and phenolic substances. Although bread with pistachio hull powder caused an increase in total phenolic content and antioxidant capacity, and thus, value-added products were obtained, the sensory quality characteristics were lower than in the control and in bread with grape seeds. Therefore, considering the quality characteristics of bread, pistachio hull powder was found to be a good alternative additive to enrich bread.  
Conceptualization, B.K.; methodology, B.K.; software, B.K. and G.A.K.; validation, B.K. and G.A.K.; formal analysis, B.K. and G.A.K.; investigation, B.K. and G.A.K.; resources, B.K. and G.A.K.; data curation, B.K. and G.A.K.; writing--original draft preparation, data curation, B.K. and G.A.K.; writing--review and editing, B.K. and G.A.K.; visualization, B.K.; supervision, B.K.; project administration, B.K.; funding acquisition, B.K. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support of Gaziantep University, Council of Scientific Research Projects (project no.: GSF.YLT.19.01) and Ozmen Un Company, Gaziantep, Turkey.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Interference Automatic Recognition and Tamping Path Planning
### Automatic Optimization Path Planning of the Tamping Device  
During the movement of the tamping device, the tamping angle \(q_{1}\) and tamping path \(L\) were functions of time \(t\). By discretizing them, the point where the unit angle line intersected with the unit elongation line was determined as the movable position of the tamping device, and the path points within the movable range of the tamping device were obtained. The connection point between points was the movable path of the tamping device [25; 26], forming the theoretical workspace lattice of the tamping device, as shown in Figure 8.  
For a certain target location, the motion path of the tamping device included multiple paths. To achieve the automatic optimization planning of the tamping device path, the cost function was used to calculate the corresponding costs of multiple movable paths, and the optimal path selection was achieved by minimizing the cost. The cost function considers interference avoidance, angle adjustment, and elongation (path) adjustment.  
\[F(i)=Z*\left\{w_{q}F(q)+w_{L}F(L)\right\} \tag{7}\]  
where \(i\) is the alternative path label: \(F(i)\) is the cost function; the \(Z\) value (0 or 1) indicates whether there are obstacles or not, respectively: \(F(q)\) is the angle adjustment cost; \(F(L)\) is the elongation (path) adjustment cost; \(w_{q}\) is the angle adjustment weight; and \(w_{L}\) is the path adjustment weight.  
The value of Z was only 0 or 1, indicating whether there were obstacles or not. If there were obstacles, it was 0, indicating that the path cost was 0 and not selected. The weight was designed based on the actual impact of the working environment on the hydraulic adjustment. Due to the on-site data showing a higher cost of the angle adjustment, the angle adjustment weight exceeded the path adjustment weight.  
For example, as shown in Figure 8, the tamping device has multiple paths to choose from, such as Paths I and II, from the current position A to the target position B in the movable paths set. From Equation (7), it is evident that both paths have the same costs as angle adjustments. Path I had a pose interference, Z = 0, and this path was discarded: Path II had no interference, Z = 1, and Path II was selected.  
Based on the cost function principle, an automatic optimization planning of the tamping device motion path was achieved. Combined with the backfilling process interference automatic recognition and regulation algorithm previously constructed, as well as the control algorithm of the hydraulic cylinder, an intelligent control system of the backfilling operation movement was formed.
# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 6 Numerical Results  
### Simulation Setup  
The system hardware configuration required for constructing the experimental environment was as follows: the CPU was i9 9900K, the GPU was a NVIDIA RTX GEFORCE 2080 TI, and the memory capacity was 32 GB. The system software configuration for the experimental environment was as follows: the experimental environment was simulated through TensorFlow1.15.0 and ran under the PyCharm-integrated development environment. The parameters of COPP-DDPG are given in Table 2.  
There was an MEC server with sufficient computational resources and communication resources. Two groups of RSUs were connected to the MEC server and two RSUs were designed in each group, which included a primary RSU and a secondary RSU. As the multi-agent model, 20 ICVs were randomly driving on the proposed road segment. We set the time slot as \(t=1\). The required computational resource for completing the vehicular task \(T_{i}\) was set as \(C_{i}=0.48\). The size of all the arrival vehicular tasks' input data in each time slot \(t\) follows Uniform distribution with \(\,D_{i}^{in}\,\sim U(2.0,\ 4.8)\). The size of output data for vehicular tasks computation were set as \(D_{i}^{out}=0.05\times D_{i}^{in}\). Meanwhile, the vehicular tasks' arrival probability was set to 0.45. The maximum completion deadline of the vehicular task was set as \(t_{i}^{max}=4t\). The available uplink and downlink transmission rate of V2I communication were derived as \(r_{ij}^{UL}\,\sim U(1.1,\ 1.25)\) and \(r_{ij}^{DL}\,\sim U(1.0,\ 1.15)\), respectively. The probability of successful connection between ICV-\(i\) and RSU-\(j\) in the random time slot was set as \(\tau_{ij}=0.95\). The propagation delay was set as \(\delta=0.1t\) and the RSU-\(j\)'s bandwidth was set as \(\omega_{j}=10\). The ICV's, VEC's and MEC server's computational resources were set as \(0.5,\ 0.9\,\sim 1.75\) and \(4.0\) Gigacycles, respectively. The unit cost of the MEC server's computational resource was set as \(\rho=0.58\) Gigacycles/Mb. In terms of the priority of the arrival vehicular tasks, we set three priorities in this work as \(Q_{i}=0.6+0.3i,\ i=1,2,3\). The partial offloading ratio of the vehicular task \(\lambda_{i}\) and the initial probability of vehicle updating pseudonym \(P(N_{i(t)})\) about privacy preservation will be discussed in Section 6.3.
## 4 Remote Sensing Image Scene Retrieval
### Retrieval by Distance Measures
Whereas true RS images are hyperspectral, the rich spectral information makes them well-suited for RSISU. In [65], deep features were extracted using CNN from the proposed new hyperspectral dataset for the RS community, and \(L2\) was used to compute the similarity. From the examples mentioned above, we can conclude that supervised feature learning methods (e.g., CNN) generally outperform unsupervised feature learning methods by a wide margin. For instance, using UC Merced data sets, the precision of the unsupervised method employed in [56] is about 65%, but the precision of the supervised method used by [59] can reach more than 80%. The distance measures allow the retrieval performance conducted on the deep features to be more perfect. RSISR for SAR is not mentioned, in that deep features are scarcely exploited for SAR. However, the aforementioned methods are all vector-based; they all calculate the similarity between two scenes by \(L2\) or other metrics between two feature vectors. The intrinsic relationship between different types of features had not been excavated. They only consider the images themselves, and they become unsuitable if the feature vector is extremely hybrid. Even when multiple types of features are extracted, the \(L2\) distance is used to calculate the similarity between two super-feature vectors, which are integrated from multiple features in the Greedy Affinity Metric Fusion (GAMF) method [66]. All of these lead to a final conclusion: the distance measures are so feature-level isolated that the retrieval performance is relatively poor. Taking [66] as an example, using Local Binary Pattern (LBP) and three other features as the feature combination, 16 of the 21 classes of scenes that are retrieved by Collaborative Affinity Metric Fusion (GAMF) combined with \(L2\) have a smaller level of precision than those by the Collaborative Affinity Metric Fusion combined with a graph model.
# Advances in Organic and Organic-Inorganic Hybrid Polymeric Supports for Catalytic Applications
## 5 Metal-Catalysed Asymmetric Reactions
Subsequently, the sulfate group was hydrolysed and the borane group removed by treatment with an excess of 1,4-diazabicyclo[2.2.2]octane (DABCO, 10 equiv.). Finally, the resulting hydroxylalkyl phosphines reacted with chlorophosphate reagents yielding supported phosphine-phosphite ligands **181a-r**. The library of 16 supported phosphine-phosphite ligands **181a-r** was employed in the asymmetric hydrogenation of three substrates. The complexation was performed prior to catalysis by suspending the resin-bound ligands in dichloromethane in the presence of rhodium precursor ([Rh(COD)2]BF4). In all cases, full conversion was achieved and enantioselectivities up to 98% were observed. The reusability of **181i** was assessed. It could be successfully reused for 11 reaction cycles with only a minor loss of activity and no decrease in selectivity. The synthesis and application of magnetic Noyori-type ruthenium catalysts for ATH reactions were reported [108]. Particularly, Noyori-type ruthenium catalyst **182** (Scheme 67) was immobilized on magnetic supports constituted by carbon coated cobalt nanoparticles (Co/C) and a polymer matrix. Among the supports prepared varying the polymer matrix and the linker, the immobilized ligand **184** gave the best results. This latter was performed by further functionalization of diphenyl vinyl functionalized nanoparticles **183**, via co-polymerization with vinyl substituted diamine ligand **174** and DVB in a ratio of 1:50:50 (Scheme 67). Thanks to the complexation in situ of ligand **184** with [RuCl2(_p_-cymene)]2, the active ruthenium catalyst, denoted as **185**, was obtained. In presence of **185**, a variety of aromatic ketones was reduced to their corresponding alcohols in an aqueous medium and with formic acid as hydrogen source. Good yields (81%-100%) and selectivities (91%-99% ee) were obtained. Furthermore, the heterogeneous catalyst **185** was recovered quantitatively by a magnetic decantation and reused 10 times showing high enantioselectivities and a gradual decrease in yield after the sixth run.
## 4 Discussion
It is noteworthy that, although the drought of 1999 has received significant attention, the drought that occurred in 1920 has not been reported as extensively. Additionally, the influence of climate change on the drought characteristics is also evident, especially in zones 4 (Southern Balochistan) and 5 (Sindh Province) which have an arid climate and display a decreasing trend in SPEI. Drought characteristics averaged for every 15 years revealed worrisome outcomes for zones 4 and 5. The drought count was at a maximum for the period 2000-2015 in zones 4 (27) and 5 (24). Similarly, the mean drought duration was maximum in zone 4 at 3.815 months. For Zone 5, the highest mean duration occurred in 1970-1984 with a value of 3.33 months, which is closely followed in the post-2000 period with a value of 3.208 months. Mean drought severity, similarly, was the lowest--with a value of \(-\)4--after 2000 in zone 4. Therefore, zones 4 and 5 may display the worsening effects of climate change, which could be further studied by looking at the atmospheric circulation modes that transport water to or from these specific regions and how they have changed and are projected to change.
## 3 Scientific Methodology
#### The Knowledge Staircase as Epistemic Model  
To structure the transformation of raw data into knowledge, the _Knowledge Staircase_ model was employed (cf. Figure 4). This model defines hierarchical stages--from data acquisition to contextualized knowledge--thereby serving as a conceptual guide for aligning system architecture components with knowledge-centric goals.  
Each level of the staircase corresponds to functional blocks within the architecture:  
1. Data Acquisition: native protocol integration (e.g., OPC UA, Profibus);
2. Data Normalization: timestamping, unit harmonization, semantic labeling;
3. Information Aggregation: filtering, event detection, and data stream correlation;
4. Knowledge Representation: model generation using ML/AI frameworks;
5. Human Interpretation: dashboard interaction, parameter refinement, rule-based feedback.  
This methodology ensures that technical decisions are traceable to domain needs and knowledge-based objectives.
### Contributions
The number of points for which the corresponding symbols for two sequences, \(\mathbf{x}_{1}\) and \(\mathbf{x}_{2}\), are different is known as the Hamming metric (distance), i.e., \(d_{\mathrm{H}}(\mathbf{x}_{1},\mathbf{x}_{2})\triangleq\sum_{l=1}^{n}\delta( x_{i_{1},l},x_{i_{2},l})\), where \(\delta(\cdot,\cdot)\) is the _Kronecker delta_, defined as follows:  
\[\delta(x_{i},x_{j})= \begin{cases}1&x_{i}\
eq x_{j},\\ 0&x_{i}=x_{j}.\end{cases}  \tag{2}\]  
The Hamming cube is defined as the set of binary sequences with length \(n\), and is denoted by \(\mathbf{H}^{n}\). The \(n\)-dimensional Hamming hyper ball of radius \(r\) for integers \(n,r\) such that \(n\geq r\geq 1\), in the binary alphabet, centered at \(\mathbf{x}_{0}=(x_{0,l})|_{l=1}^{n}\), is defined as  
\[\mathcal{B}_{\mathbf{x}_{0}}(n,r)=\{x^{n}\in\mathcal{X}^{n}\,:\,d_{\mathrm{H}} (\mathbf{x},\mathbf{x}_{0})\leq r\}. \tag{3}\]  
Specifically, \(\mathcal{B}_{\mathbf{x}_{0}}(n,r)\) for alphabet \(\mathcal{X}^{n}=\mathbf{H}^{n}\), center \(\mathbf{0}\triangleq(0,\ldots,0)\) and radius \(r=nA(A\geq 0)\) is given by \(\mathcal{B}_{\mathbf{0}}(n,nA)=\{\mathbf{x}\in\mathbf{H}^{n}:\sum_{l=1}^{n}x_ {l}\leq nA\}\). The volume of the Hamming hyper ball \(\mathcal{B}_{\mathbf{x}_{0}}(n,r)\) in the \(q\)-ary alphabet is defined as the number of points that lie inside the ball, and is denoted by \(\mathrm{Vol}\big{(}\mathcal{B}_{\mathbf{x}_{0}}(n,r)\big{)}\). The set of whole numbers is denoted by \(\mathbb{N}_{0}\triangleq\{0,1,2,\ldots\}\). The \(q\)-ary entropy function \(H_{q}:[0,1]\rightarrow\mathbb{R}\) for positive integer \(q\geq 2\), is defined as \(H_{q}(e)\triangleq x\log_{q}(q-1)-x\log_{q}x-(1-x)\log_{q}(1-x)\). \(H_{q}(\cdot)\) for \(q=2\), is denoted by \(H(\cdot)\), and is defined as \(H(\varepsilon)\triangleq-\varepsilon\log(\varepsilon)-(1-\varepsilon)\log(1-\varepsilon)\). Throughout the paper, we denote the BSC with cross-over probability \(\varepsilon\in(0,1/2)\) by \(\mathcal{B}\).
# Active Equalization of Lithium-Ion Battery Based on Reconfigurable Topology
## 4 Calculation and Comparison
### Efficiency Calculation of the Ideal Working Circuit  
Firstly, an ideal series working circuit will be introduced, as shown in Figure 8. It is assumed that the battery cells in the ideal circuit are balanced all the time, and only the power loss caused by the internal resistances of the cells and the efficiency of the converter should be taken into consideration.  
\(P_{\mathrm{g}}\) denotes the output power of the series battery group (the input power of the converter). The relationship between \(P_{\mathrm{g}}\) and \(P\) is  
\[P_{\mathrm{g}}=\frac{P}{\eta} \tag{12}\]  
The output voltage of the battery group can be calculated as  
\[V_{\mathrm{g}}=nmv \tag{13}\]  
The current flowing through each cell can be calculated as  
\[I_{\mathrm{g}}=\frac{P_{\mathrm{g}}}{V_{\mathrm{g}}}=\frac{P}{nmv\eta} \tag{14}\]  
The power loss caused by the internal resistances of the cells is  
\[P_{r}={I_{\mathrm{g}}}^{2}R=(\frac{P}{nmv\eta})^{2}nmr=\frac{P^{2}r}{nmv^{2} \eta^{2}} \tag{15}\]  
The total power all the battery cells spend is  
\[P_{\mathrm{all}}=P_{\mathrm{g}}+P_{r} \tag{16}\]  
Thus, the total efficiency of the ideal circuit can be expressed as  
\[\eta_{1}=\frac{P}{P_{\mathrm{all}}}=\frac{1}{\frac{1}{\eta}+\frac{P_{r}}{nmv^{ 2}\eta^{2}}} \tag{17}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline number of battery cells \(n\times m\) & \(4\times 3\) \\ internal resistance of a battery cell \(r/\Omega\) & \(0.01\) \\ internal resistance of a MOSFET \(r_{s}/\Omega\) & \(0.001\) \\ output voltage of a battery cell \(\eta/\mathrm{V}\) & \(7.2\) \\ load voltage \(V_{\mathrm{out}}/\mathrm{V}\) & \(72\) \\ load current \(I_{\mathrm{out}}/\mathrm{A}\) & \(1\) \\ load power \(P/\mathrm{W}\) & \(72\) \\ equalization power \(P_{\mathrm{eq}}/\mathrm{W}\) & \\ efficiency of DC/DC converter \(\eta/\%\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of equalization efficiency calculations.  
Figure 8: Ideal circuit.
## 5 Case Study: Applying CVMan to Analyze Clones in Ubuntu
### Answering PQ2
#### 5.3.3 Clone Lifetime  
Recall that the clone lifetime describes the duration of the existence of clones in Linux. To facilitate comparison of clone lifetime lengths, it is customary to employ the number of versions of a clone that exist rather than the actual duration of its existence. Figure 5b presents the cumulative distribution function (CDF) of the clone lifetime in Linux in Ubuntu 18.04-21.10. A total of 53% of the clone lifetime lengths are for eight versions, indicating that the corresponding procedures are similar across the eight consecutive versions.  
**Insight 2**.: _In multiple versions of the program, most of the clones are unchanged or similar. In Linux, 53% of the clone lifetime lengths are for eight versions._
## 2 Materials and Methods
### Remotely Acquired Images and Data
#### 2.3.3 Photogrammetric Processing  
Photogrammetric processing was performed using a Laptop HP Omen 17-an002 nl with 3.8 a GHz (in Turbo Boost, 6 MB of cache, 4 core) Intel Core i7-7700 HQ, 32 GB RAM (2 \(\times\) 16 GB DDR4-2400), NVIDIA GeForce GTX 1070A (8 GB GDDR5) and SSD 512 GB PCIe NVMe M.2, running Agisoft Metashape Professional Software.  
The processing with Agisoft Metashape software was necessary to obtain a spatial resolution of \(\sim\)1 cm/pixels for virtual outcrops reconstruction. Digital photogrammetry, obtained from the sequential drone-acquired overlapping images and through the use of Agisoft Metashape software led to the following elaborations: (a) sparse point clouds, (b) dense point clouds, (c) meshes and textures, (d) 3D models, (e) tiled models, (f) DEM and orthomosaic model.  
Figure 5 shows all the processing steps carried out with Agisoft Metashape software (details in the caption of Figure 5).  
Figure 5: Workflow of the used digital photogrammetry procedure in the sample area 1 (a to g are in-sequence steps): (**a**) overlapping digital aerial photographs and ground control points are imported into the software package; features within the images are automatically detected, matched, and used to calculate locations and orientations of camera shooting points; (**b**) points that have been used for the image alignment form an initial sparse-point cloud (109.802 points); the inclusion of ground control points make the sparse-point cloud automatically georeferenced; (**c**) point densification (multi-view stereo algorithm): points that were manually classified as representing the ground surface are exported as a dense-point cloud (over 13 millions points); (**d**) 3D model construction obtained building meshes between dense-point cloud, with 1,930,896 faces; (**e**) a tiled model is obtained by interpolations of all image acquired during drone survey with faces of 3D model, the final resolution is 3.75 mm/pixel; (**f**) Construction of DSM containing all height values through rasterizing the dense-point cloud or from meshes. The obtained Digital Surface Model has a resolution of 1.00 cm/pixel; while (**g**) the georeferenced orthomosaic with a high resolution of 3.75 mm/pixel was reconstructed interpolating all the photos shooted from the drone. Additional datasets were computed for fracture mapping in a Cloud Compare, ArcGIS, and Move Petex suite software.