esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of LiDAR and Photogrammetry for 3D Crime Scene Reconstruction
## 3 Methodology
### Scanned Models' Accuracy Assessment  
The accuracy of the constructed 3D models is assessed using two methods: (1) using the reference markers and (2) using the point-cloud comparison. A simple mean absolute error (MAE) metric is computed for the reference markers, which calculates the absolute difference between predicted values from the 3D models and the actual values measured using a laser meter. Figure 3 shows the predicted values computed from CloudCompare. We repeated the predicted measurements three times for each maker pair and then calculated the average error for each repetition to enhance the accuracy. The overall MAE for each scan is calculated by averaging the MAE values across all maker pairs. We need to use the raw format to evaluate the quality of our scans, that is, e57 file format from the Recon-3D application and the LAS format from Pix4 Dmapper. These raw formats can assist us in visually inspecting the models on CloudCompare and precisely measuring the models' dimensions while ensuring that there was no editing to enhance them visually. Each scan will be assessed to confirm that it contains key elements, including the makers and AprilTags. The models must be aligned to the reference model for the point-cloud comparison. The Iterative Closest Points algorithm aligned the models, thus enabling the calculation of the point-wise errors.
## 4 Evaluation of Biological Properties  
### Antioxidant Activity  
Daucosterol is an antioxidant molecule with an essential role in the fight against free radicals (Table 3). [PERSON] et al. [9] demonstrated that this constituent has potent antioxidant properties, with significant activity using the DPPH (2, 2-diphenyl-1-picrylhydrazyl) free radical assay. Indeed, it showed a potential reduction of DPPH radicals, with 50% of inhibition (IC\({}_{50}\) = 490 \(\pm\) 2.9 ug/mL) and a percentage inhibition of 19.7% at 100 mg/mL [3] and a potential reduction with IC\({}_{50}\) = 27.3 \(\pm\) 0.015 ug/mL in another, similar study [8]. Other methods such as H\({}_{2}\)O\({}_{2}\), FTC, FRAP, and PPM showed DPPH radical inhibition equal to 31.18 \(\pm\) 0.5%, 37.12 \(\pm\) 0.44%, 122.23 \(\pm\) 0.014 ug EAA/mg ex, and 16.44 \(\pm\) 0.0012 ug EAA/mg ex, respectively [8]. Several studies have confirmed this potential for DPPH radical inhibition with varying results: IC\({}_{50}\) = 36.263 \(\pm\) 0.005 ug/mL [7], IC\({}_{50}\) = 155.0 \(\pm\) 0.5 uM [14], IC\({}_{50}\) = 224.1 \(\pm\) 8.2 ug/mL [60], IC\({}_{50}\) = 6.0 \(\pm\) 0.1 mg/L [64], EC\({}_{50}\) > 250 ug/mL [69], IC\({}_{50}\) = 11.42 \(\pm\) 0.07 ug/mL [66], IC\({}_{50}\) = 108.14 \(\pm\) 9.54 ug GAE/ mL [73], and IC\({}_{50}\) = 38.86 ug/mL [20]. However, [PERSON] and collaborators demonstrated that daucosterol does not affect the DPPH radical [6]. On the other hand, it inhibited the activity of the ABTS radical with a potential inhibition at 50% equal to 4.8 \(\pm\) 0.04 mg/mL and a percentage inhibition of 27% at the concentration of 0.04 mg/mL [110], plus EC\({}_{50}\) = 143.4 ug/mL [69], IC\({}_{50}\) = 9.02 \(\pm\) 0.11 ug/mL [66], IC\({}_{50}\) = 1.66 \(\pm\) 0.15 umol TE/g of DW [73], and IC\({}_{50}\) = 0.71 \(\pm\) 0.01 ug/mL [20].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods Used** & **Key Results** & **References** \\ \hline DPPH & IC\({}_{50}\) = 490 \(\pm\) 2.9 ug/Ml [9] \\ \hline DPPH & RSA\% = 19.7\% at 100 mg/mL [3] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antioxidant effects of Daucosterol.
# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 2 Materials and Methods
### Model Architecture Design  
ResNet-50 was chosen as the core model architecture, with the key features of the introduction of skip connections. These skip connections allow input signals to bypass one or more layers and be directly transmitted to subsequent layers through identity mappings, effectively alleviating the gradient vanishing problem [25]. The residual learning mechanism in ResNet enables the network to learn the difference (residual) between the input and  
Figure 1: Feature maps of seven disaster types.  
output rather than directly learning a complete mapping function. This approach ensures that as the network depth increases, its performance does not degrade, thereby improving training efficiency and feature extraction capability. The innovation in ResNet has made training deeper networks feasible, allowing models with hundreds or even thousands of layers to effectively maintain gradient flow and achieve stable training performance.  
Based on the residual learning mechanism of ResNet, this study adopts ResNet-50 as the core architecture and develops an improved ResNet-50 model for multi-disaster classification and recognition. The visualization of the improved ResNet-50 network model is shown in Figure 3. As illustrated in Figure 3, the proposed improved ResNet-50 model consists of a 7 \(\times\) 7 convolutional layer, a batch normalization layer, ReLU activation function, 3 \(\times\) 3 max pooling layer, four groups of residual layers, a global average pooling (GAP) layer, and a fully connected (FC) layer. In this architecture, the residual blocks utilize skip connections to directly add the input signal to the output, effectively mitigating the gradient vanishing issue in deep networks while enhancing training stability and feature extraction capability. Based on this network structure, the following sections provide a detailed explanation of the model construction and training process.  
Figure 3: Visualization of the improved ResNet-50 model architecture.  
Figure 2: Flowchart of disaster image classification based on the improved ResNet-50 model.
# An Electronically Adjustable Floating Memcapacitor Emulator Circuit Using CDBA
###### Abstract  
Memristive elements, known as memristors, memcapacitors and meminductors, have become an important topic of research in the electronics world in recent years. As there is still no efficient way to manufacture two-terminal memristive elements, many researchers have focused their efforts on designing emulator circuits that mimic these devices. In this study, a memcapacitor emulator circuit using Current Derivative Buffered Amplifier (CDBA) is proposed, which has significant advantages such as wide dynamic range, differential structure at the input port, high sloping rate and wide bandwidth. The main advantages of the emulator are that it is floating without grounding constraint, it is electronically adjustable, it has charge-controlled incremental and decremental modes and it has a simpler circuit structure since it does not contain a memristor. To ensure the integrity of the circuit theory, the results of the mathematical model and the simulation of the memcapacitor are given together. In addition, the characteristics of the experimentally investigated memcapacitor emulator are in good agreement with the simulation results. To provide an illustration of the performance of the proposed emulator, firstly the second-order active low-pass filter circuit and subsequently the amoeba learning circuit are selected as the working environment. The results show that the filtering performance of the proposed emulator at a value after the cut-off frequency in the filter circuit is 25% more efficient than a standard capacitor and in terms of power consumption, it consumes 27.93% less power than a standard capacitor. Moreover, the emulator successfully accomplishes the learning and data storage tasks in the amoeba learning circuit.  
memristive elements; memcapacitor; emulator; floating property; CDBA 2025 15 7506
# Novel Aspects of Polynucleotide Phosphorylase Function in _Streptomyces_
## 2 Organization and Expression of the PNPase Gene in _Streptomyces_
coelicolor_, that is, the same pattern of temporal regulation was observed in both of these sigma factor mutants as in the parental strain (cf. Figure 2). In marked contrast, the _PrpsO_A, _PrpsO_B, and _Pnp_B promoters were completely inactive in the \(\sigma^{\text{B}}\) mutant. _Pnp_A, however, was as active in the \(\sigma^{\text{B}}\) mutant as in the parental strain, and showed a similar pattern of temporal expression [31]. This result indicates that Pr_rpsO_A and B and _Pnp_B are dependent on \(\sigma^{\text{B}}\) for activity, and suggests that these promoters are transcribed by an RNA polymerase holoenzyme containing \(\sigma^{\text{B}}\). PNPase is a cold shock protein in many bacteria, and it has been shown that cold shock leads to an increase in PNPase levels in _E. coli_ and other organisms [9; 10; 11]. It was of interest to determine  
Figure 2: **(A)** Growth of the _S. coelicolor_ strains containing promoter probe constructs. Growth was measured as the increase in optical density at 450 nm. The arrows in the figure indicate the onset of the production of two of the secondary metabolites synthesized by _S. coelicolor_, undecylprodgiosin (red) and actinorhodin (act). **(B)** Catechol dioxygenase (CATO\({}_{2}\)ase) activity of mycelial extracts of _S. coelicolor_ derivatives containing the putative _rpsO_A and _rpsO_B promoters, cloned in the promoter probe vector pIPP2 [35]. Mycelium was harvested at the indicated times, disrupted by sonication, and following centrifugation, supernatants were assayed for catechol dioxygenase, as described previously [31; 35]. The catechol dioxygenase gene is the reporter in the promoter probe vector [35]. **(C)** CATO\({}_{2}\)ase activities of extracts of strains containing the putative _pnp_A and _pnp_B promoters. The results shown are the averages of duplicate assays from two independent experiments \(\pm\) SEM. This figure is reprinted from _[PERSON]_, 536, [PERSON], [PERSON], [PERSON], Transcription of the _rpsO-pnp_ operon of _Streptomyces coelicolor_ involves four temporally regulated, stress responsive promoters. 177–185, Copyright (2014), with permission from Elsevier [31]. whether PNPase levels increased in cold shock in _S. coelicolor_, and whether any such increase reflected changes in the activity of the _rpsO-pnp_ promoters. As shown in Figure 3C, PNPase activity increased significantly (two-fold) in _S. coelicolor_ over three hours of cold shock at 10 \({}^{\circ}\)C. This increase in activity was accompanied by an increase in the activities of all four of the _rpsO-pnp_ promoters (Figure 3A,B) as compared with their activities at the normal growth temperature, 30 \({}^{\circ}\)C.  
Thus, PNPase is a cold shock protein in _Streptomyces_, and the cold shock response involves changes in the activities of the promoters responsible for transcription of the _rpsO-pnp_ operon [31].
# Transition Analysis and Its Application to Global Path Determination for a Biped Climbing Robot
## 2 Problem Statement  
### Global Path for Traveling on Trusses  
For BiCRs moving on trusses, path planning determines a sequence of discrete gripping points along the way from the original point to the destination, and the corresponding collision-free motion wags the moving end from one gripping point to another. On a universal 3D complex truss, step-by-step blind exploration is very time- and resource-consuming, resulting in extremely low searching efficiency. In addition, the increase in the number of members will greatly reduce the efficiency of path searching. Global guidance, as a promising solution, is hence important and necessary to avoid inefficient searching. Unlike the path planning problems on 2D or 2.5D terrain, where providing the gradient from the original point to the destination may be sufficient, efficient global guidance for BiCRs traveling on trusses must indicate the entire path, member by member, as shown in Figure 3. Operational regions for performing transition between each pair of members must be also provided, in order to facilitate the subsequent grip planning.  
Since the crucial point of global path planning is to provide global guidance for the subsequent processes, it does not require details such as where to grip and how to move during each climbing cycle, but concentrates on the fast determination of feasible routes based on the overall structural environment. It should be underlined that these feasible routes will be discarded if later processing fails. However, the distinguishing merit is that the searching space is largely narrowed down to a limited number of members with step-by-step guidance by the so-called global path planner. As a result, the searching efficiency is expected to be largely increased.
# TAB Series-Resonant DC-DC Converter and Multi-Phase-Shift Based Global Optimization Modulation
## 2 Analysis of Topology and Characteristics
### Power Decoupling Method  
The coupled power between port 1 and 2 is not desired because it causes the power error. Hence, the power decoupling method is discussed in this section. From (10), the coupled power is related to the circuit parameters and is defined as  
\[k_{\rm c}=\frac{8{v_{3}}^{2}}{\pi^{2}k_{1}k_{2}X_{12}} \tag{11}\]  
It can be seen that \(\rm{kc}\) is inversely proportional to \(X_{12}\) between port 1 and port 2 in the equivalent circuit. As \(X_{12}\) increases, \(\rm{kc}\) decreases when \(X_{12}=\infty\), \(k_{\rm c}=0\), and \(P_{12}=0\). Thus, the power decoupling is realized. Furthermore, according to (4), \(X_{12}\) is closely related to \(X_{\rm{r3}}\). When \(X_{\rm{r3}}=0\), \(X_{12}=\infty\) and \(P_{12}\) is accordingly equal to zero. \(X_{\rm{r3}}\) is calculated as  
\[X_{\rm{r3}}=\left(\frac{f_{\rm{s}}}{f_{\rm{r3}}}-\frac{f_{\rm{r3}}}{f_{\rm{s}} }\right)\sqrt{\frac{L_{\rm{k3}}}{C_{\rm{r3}}}} \tag{12}\]  
where \(f_{\rm{s}}\) is the switching frequency, \(f_{\rm{r3}}\) is the resonant frequency of the resonant tank of port 3.  
According to (12), when \(\rm{fs}\) is equal to \(f_{\rm{r3}}\), \(X_{\rm{r3}}=0\). Therefore, the power decoupling is realized by setting\(f_{\rm{r3}}=f_{\rm{s}}\).  
The equivalent circuit after power decoupling is shown in Figure 3b. It can be seen that the proposed power decoupling method eliminates the flow path of the transfer power between port 1 and port 2. More importantly, the proposed method only plugs in a resonant capacitor at the common port. Any additional power decoupling control loop is not needed. Therefore, it is easy to implement and avoid increasing the complexity of the control configuration.
# Genomic and Metabolic Characteristics of the Pathogenicity in Pseudomonas aeruginosa
## 2 Pseudomonas aeruginosa  
_Pseudomonas aeruginosa_ is a ubiquitous bacterium, present both in terrestrial and aquatic ecosystems. As a result, it can be found in many foodstuffs but also in hospital environments. _P. aeruginosa_ is considered to be a bacterium of major medical importance due to its great ability to adapt to different environments, but also because of its ability to cause chronic infection in vulnerable people [8]. The first report describing the genus _Pseudomonas_ came from the German botanist [PERSON] in the late 19 th century. He visualized mobile cells with spores, such as the nanoflagellate _Monas_ spp. Thus, the name _Pseudomonas_ was generated from the identification of this false or \"pseudo\" _Monas_ spp. nanoflagellate [9]. However, the specific name \"_aeruginosa_\" was described earlier in 1872 by [PERSON] because of the color of the colonies on certain media, resembling copper rust or verdigris color, hence being green. [PERSON] attached the name to the genus _Bacterium_, to give _Bacterium aeruginosa_, and later, [PERSON] transferred the species to the newly described genus _Pseudomonas_[10].  
_P. aeruginosa_ is a gram-negative, bacillus-shaped bacterium (size from 0.5 to 3.0 \(\upmu\)m), with an aerobic metabolism and a single flagellum that helps in move. It is a non-fermentative bacterium which, in aerobic situations, uses the glycolytic pathway for glucose degradation, with oxygen as its final electron acceptor. However, under anaerobic conditions, nitrogen can be used as an electron acceptor. Furthermore, ATP acquisition is achieved by the action of several membrane ATPases reusing ADP and H\({}^{+}\) from previous reactions. It should be noted that sometimes, aromatic compounds like phenolsulfates and phenylalanine can be considered as putative substrates for this species' catabolic activity [11]. This species can survive in a wide range of environmental conditions (mostly regarding their chemical composition), it can use a variety of different carbon, nitrogen and energy sources, but it has a rather limited range of growth temperatures [10].
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 4 Smart-Contracts Vulnerabilities and Countermeasures
### Vulnerabilities and Countermeasures
#### 4.2.7 tx.origin  
The tx.origin is a unique global variable in Solidity. It stores the original caller's address of a transaction, unlike msg.sender, which is the immediate caller. Therefore, tx.origin is always an external account's address. If this variable is used as an authorization parameter, the identity of the actual owner of a smart contract could be exploited. For instance, if require(tx.origin == owner); is invoked by the attacker's contract, which is called by the real owner, the attacker could go on to execute code that follows.  
_Countermeasures_. Attackers could run their code under the name of caller's tx.origin, so never use tx.origin in identity verification or authentication [79, 82].
## 2 Polarimetric SAR: Techniques and Applications
In the first one [4], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the temporal variations of polarimetric observables derived from full-circular and dual-circular polarimetric data acquired along the rice cultivation cycle with a ground-based sensor, and assessed several variables with regard to their effectiveness in phenology retrieval. [PERSON], [PERSON], and [PERSON] developed in a study [5] on the estimation of biophysical variables in rice fields by employing X-band copolar data and electromagnetic modeling of the scene. Also related to vegetation, but with a broader environmental scope, the paper coauthored by [PERSON], [PERSON], [PERSON], and [PERSON] [6] describes the response of a large number of polarimetric observables, obtained at L-, C-, and X-bands in a tundra scene located in Canada. The use of shorter wavelength imagery (X and C) was beneficial for the characterization of wetland and tundra vegetation, while L-band data highlighted differences between the bare ground classes better. [PERSON], [PERSON], and [PERSON] [7] addressed the estimation of aboveground biomass in tropical forests by combining dual-pol data from two different sensors, Sentinel-1A and ALOS PALSAR-2, at two different frequency bands. As for the ocean application of polarimetry, [PERSON], [PERSON], [PERSON], and [PERSON] contributed in their study [8] with a comparison of quad-, compact-, and dual-polarimetry for oil spill classification, in which a new set of input features was proposed and tested. Finally, one paper was published on polarimetric SAR interferometry, coauthored by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [9], in which a truncated singular value decomposition (TSVD)-based method is proposed for forest height inversion from single-baseline PolInSAR data. With such an approach, the assumption of null ground-to-volume ratio in one of the observed channels, common in most PolInSAR algorithms, is avoided.
# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 3 Materials and Methods
### Proposed Method
#### 3.3.1 Modified CNN Models  
First, the models are fine-tuned with the public dataset using the eight pre-trained models (VGG16, Xception, Inception, ResNet-50, DenseNet121, MobileNet, EfficientNetB0, and InceptionResNnet) as indicated in Section 3.2. These models were based on the pre-trained ones using the ImageNet dataset. Fine tuning is applied to the pre-trained model to achieve a high level of accuracy for the classification of ships. The best model achieved among the tested pre-trained models was chosen for further processing steps; modification, optimization and ensemble.  
Following the successful application of transfer learning (TL) and the fine tuning of the training of eight cutting-edge CNN models on the ship classification datasets, the top CNN model is modified by adding three additional layers. Each extra layer consists of a fully connected (FC) layer, a dropout layer and a ReLU layer. The ReLU is added to learn the new properties of our training dataset. This layer was followed by average pooling. In terms of performance, they are chosen to serve as the base learners for the ensemble models presented in the following subsection. These models are then used to construct the ensemble models. The full procedure is depicted in Figure 1.
# Low-Latency QC-LDPC Encoder Design for 5G NR
###### Abstract  
In order to meet the low latency and high throughput requirements of data transmission in 5 th generation (5G) New Radio (NR), it is necessary to minimize the low power encoding hardware latency on transmitter and achieve lower base station power consumption within a fixed transmission time interval (TTI). This paper investigates parallel design and implementation of 5G quasi-cyclic low-density parity-check (QC-LDPC) codes encoder. The designed QC-LDPC encoder employs a multi-channel parallel structure to obtain multiple parity check bits and thus reduce encoding latency significantly. The proposed encoder maps high parallelism encoding algorithms to a configurable circuit architecture, achieving flexibility and support for all 5G NR code length and code rate. The experimental results show that under the 800 MHz system frequency, the achieved data throughput ranges from 62 to 257.9 Gbps, and the maximum code length encoding time under base graph 1 (BGI) is only 33.75 ns, which is the critical encoding time of our proposed encoder. Finally, our proposed encoder was synthesized on SMIC 28 nm CMOS technology; the result confirmed the effectiveness and feasibility of our design.  
5G New Radio; QC-LDPC codes; channel encoding; encoder; low latency
## 3 Underlying Graph Structure  
The communication among agents in the cooperative systems are described by the graph \(\mathcal{G}=(\mathcal{N},\mathcal{E})\), which consists of node set \(\mathcal{N}\) and edge set \(\mathcal{E}\)[41]. Edge set \(\mathcal{E}\subset\mathcal{N}\times\mathcal{N}\) is given between nodes \(i\in\mathcal{N}\) and \(j\in\mathcal{N}\) such that \((j,i)\in\mathcal{E}\) denotes node \(i\) receives information from \(j\). Let \(n_{w}\) be an arbitrary signal dimension, and then the adjacency matrix; shared signal sizes among agents are assumed to be identical and equal to \(n_{w}\). \(\mathbf{A}=[a_{ij}]\otimes\mathbf{I}_{n_{w}}\in\mathbb{R}^{N\cdot n_{w}\times N\cdot n _{w}}\) of \(\mathcal{G}\) is composed of weighting scalars \(a_{ij}\), where \(a_{ij}\) quantifies the strength of the connection from node \(j\) to node \(i\). \(N\) is the number of agents in the cooperative system (CS). Formally, \(A_{ij}\) is described by the following equation.  
\[\mathbf{A}_{ij}=\begin{cases}a_{ij}>0,&j\
eq i,\ \ (j,i)\in\mathcal{E}\\ a_{ij}=0,&\text{otherwise}\end{cases} \tag{1}\]