esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### EBOV Persistently Infects Primary Cells from M. condylurus  
To investigate whether EBOV might establish a persistent infection in _M. condylurus_ primary cells, EBOV-infected primary cells derived from lung, brain, and testicles (MoLu Prim, MoBra Prim, and MoTes Prim) were regularly passaged and visually monitored for cytopathic changes and cell death. MoLu Prim cells infected with EBOV showed division rates comparable to uninfected cells, consistently reaching 100% confluency within 7 d, and were passaged weekly until 150 dpi (Supplementary Figure S4A). Cell viability consistently ranged from 95 to 100%. EBOV-infected MoBra Prim and MoTes Prim cells were cultivated until 74 dpi with weekly changes of cell culture medium. They were only passaged twice due to their low cell division rates, while not reaching confluency (Supplementary Figure S4B,C). Cell viability ranged between 80 and 85% for both cell cultures at the time of passaging. On day 67 post-infection, MoBra Prim and MoTes Prim cells started to lose adherence and on day 74 all cells were completely detached.  
Similar amounts of viral RNA, between 2.5 and 3.0 \(\times\) 10\({}^{7}\) copies/mL in supernatants, were detected for all three cell isolates at 32 dpi (Figure 3). Continuously high RNA titers, with up to 3 \(\times\) 10\({}^{8}\) copies/mL, were detected in MoLu Prim cells out to 150 dpi. Viral RNA titers in MoBra Prim and MoTes Prim cells dropped to 2 \(\times\) 10\({}^{6}\) and 2 \(\times\) 10\({}^{5}\) copies/mL, respectively, on day 74 post-infection. For the duration of the 150 days in culture, only a small proportion of MoLu Prim cells were persistently infected (Figure 4A), surrounded by uninfected cells (Figure 4C,D), and the cultures tolerated high levels of EBOV replication with unchanged cell division rates. For subsequent experiments, this persistently infected cell isolate was termed \"MoLu Prim_EBOV\".  
Figure 3: EBOV replication kinetics in long-term infected _M. condylurus_ cells. Viral RNA copy numbers/mL in supernatants of infected cell isolates were determined by qRT-PCR for 74 (purple, cyan) and 150 (orange) dpi.
# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 2 Related Work  
In the field of autonomous machinery, unmanned excavators, while not as complex as self-driving cars, face a unique set of challenges. The character of the typical excavator working environment is static and there is a reduction in the variety of objects compared to an autonomous driving scenario. However, the terrain features of excavator scenarios are highly variable and detailed in form.  
Significant research, both domestic and international, has been conducted on environmental perception technologies for unmanned excavators. Pioneering systems developed by scholars like [PERSON] et al. [6] utilized LiDAR on excavators for environmental perception, laying the groundwork for subsequent systems. Despite their limitations in integrating driving and positioning, these early systems demonstrated the potential for stationary data collection. [PERSON] et al. [7], tailoring to specific excavation projects, developed an automated hydraulic excavator prototype based on 3D information, focusing on environmental perception through a combination of LiDAR and cameras, supplemented with gyroscopes and GPS for comprehensive data collection.  
Further advancements by [PERSON] et al. [8] introduced a mobile 3D environment recognition system for civil engineering, utilizing movable 3D laser scanners to scan and input site terrain for processing. [PERSON] et al. [9] proposed a multi-frame convolutional approach, providing a complete solution for unmanned excavators by exploiting temporal information between continuous frames and extracting richer features through CNNs. This mature technology has been tested globally, achieving recognition accuracy above 90% at 10 fps, serving as a foundational step in various implementations. [PERSON]'s work [10] on large-scale grid mapping with LiDAR for autonomous driving was notable but lacked focus on under-vehicle information crucial for unmanned excavator perception. Collaborative research by Baidu and the University of Maryland [3] developed an Autonomous Excavator System (AES) that combined cameras, positioning systems, and LiDAR for extended autonomous excavation.  
Building upon the foundation set by previous research, our method innovatively processes LiDAR data through a deep learning-based terrain processing network, which efficiently converts complex 3D point clouds into 2D pseudo-images for feature extraction, as summarized in Table 1. This method not only simplifies data processing but also focuses more on the extraction of terrain features. By integrating these features with a DRL algorithm, our system achieves intelligent trajectory generation with improved computational efficiency, signifying a significant advancement over existing research.
# Differential Ultra-Wideband Microwave Imaging: Principle Application Challenges
## 2 Signal Model for Small Time-Variant Scattering Objects
### Invariant Object in Free Space and Its Localization
 \right]^{T}\), and the uncertainties of the measurement are represented by the \([K,K]\) covariance matrix \(\Sigma\approx\sigma^{2}\mathbf{I}\), which is a diagonal matrix if the range measurements are mutually independent. \(\mathbf{I}\) is the identity matrix, and \(\sigma^{2}\) gives the variance of the range estimation, which can be taken often as equal for all measurement channels. Figure 5 depicts a simple 2D-example of the PDF illustrating the impact of the measurement uncertainty \(\sigma\) and the array structure onto the target localization. In this context, it should not go unmentioned that the measurement uncertainty is not only due to random measurement errors, but also caused by the ambiguity of the distance definition of spatially extended objects [21; 22]. Furthermore, the method is only able to estimate the position of a single target, so in multi-target scenarios, the roundtrip times extracted from the measurement data must be correctly assigned to the related targets beforehand. Figure 5: Simulated example of the probability density function (PDF) for a 2D-scenario. The standard deviation of the measurements was assumed to be \(\sigma=0.3\,\mathrm{m}\). (**Top**) the observation area is surrounded by the antenna. (**Bottom**) the observation area is illuminated only from one side. _Method 3_: The last imaging approach, at least that we will mention here, exploits the reciprocity of the transmission paths characterized by the multi-static response matrix \(\mathbf{S}_{\Lambda}\). Further it is limited to a small number of point scatterers involved in the test scenario; hence, the method is also assigned as sparse scene imaging. For details of the method, the interested reader is referred to [1; 23; 24; 25; 26; 27]. For the first trial, the reciprocity properties of \(\mathbf{S}_{\Lambda}\) were used by the DORT (Decomposition de l'Operateur de Retournement Temporel) approach [28] with the aim of concentrating wave energy at the position of the strongest scatterer under the multi-path condition. Related ideas may also be adapted for imaging purposes, which exploit Eigen-value or singular value decompositions and the MUSIC concept for target localization. It should be noted that this method assumes knowledge of the wave propagation speed within the scenario of the test.
## 5 Concluding Discussion
### A Multilevel Learning Process and Implementation
#### 5.2.3 Policy-Level Learning  
Several of the studied cases illustrate the importance of a constructive collaboration between government and industry, to raise awareness and readiness in the sector as a whole. In a report from the OECD [6], procurement is described as an ongoing dialogue between the government, frontrunner companies, and purchasing units, with the aim of continuously improving the efficiency of public procurement. A majority of the interviewees emphasized the importance of breaking the silo-thinking in the construction industry and developing methods and structures to integrate competencies between different organizations in the supply chain, as well as at an industry-level, to attain significant carbon emission reductions. In the driver cases, the projects take the lead by setting higher goals than the national level. In the frontrunner cases, the industry-level collaboration takes the form of innovation platforms focusing on innovation/product improvement. In the translator countries, in contrast, collaboration and development is happening in relation to the \"reduction ladder\" created by the carbon calculation tools, fostering an increased control of the carbon emissions of different parts of the project over time. Consequently, the structures for public-private cooperation become central for the implementation of international goals of reducing carbon emissions in the sector as a whole. The implementation pathway is not the same for all projects and showcases the importance of context-specific aspects [59].
## 4 Results
### ANOVA Gauge Repeatability and Reproducibility (R&R) Test
\begin{table}
\begin{tabular}{c|c c c|c c c|c c c} \hline \hline  & \multicolumn{3}{c|}{**Workpiece 1**} & \multicolumn{3}{c|}{**Workpiece 2**} & \multicolumn{3}{c}{**Workpiece 3**} \\ \hline  & **Op 1** & **Op. 2** & **Op. 3** & **Op. 1** & **Op. 2** & **Op. 3** & **Op. 1** & **Op. 2** & **Op. 3** \\ \hline
**Msh 1** & 0.71 & 0.60 & 0.77 & 0.78 & 0.82 & 0.74 & 0.79 & 0.84 & 0.89 \\
**Msh 2** & 0.63 & 0.71 & 0.72 & 0.79 & 0.85 & 0.87 & 0.77 & 0.81 & 0.81 \\
**Msh 3** & 0.64 & 0.61 & 0.65 & 0.81 & 0.79 & 0.82 & 0.80 & 0.79 & 0.83 \\
**Msh 4** & 0.70 & 0.65 & 0.56 & 0.79 & 0.80 & 0.80 & 0.77 & 0.80 & 0.77 \\
**Msh 5** & 0.65 & 0.74 & 0.65 & 0.79 & 0.83 & 0.74 & 0.79 & 0.86 & 0.78 \\
**Msh 6** & 0.69 & 0.68 & 0.60 & 0.84 & 0.84 & 0.83 & 0.82 & 0.76 & 0.85 \\
**Msh 7** & 0.68 & 0.67 & 0.67 & 0.85 & 0.80 & 0.82 & 0.84 & 0.77 & 0.80 \\
**Msh 8** & 0.70 & 0.74 & 0.72 & 0.75 & 0.80 & 0.81 & 0.76 & 0.78 & 0.76 \\
**Msh 9** & 0.61 & 0.67 & 0.65 & 0.81 & 0.78 & 0.74 & 0.82 & 0.81 & 0.78 \\
**Msh 10** & 0.72 & 0.64 & 0.58 & 0.75 & 0.77 & 0.81 & 0.84 & 0.79 & 0.79 \\ \hline
**Mean** & 0.67 & 0.67 & 0.66 & 0.80 & 0.81 & 0.80 & 0.80 & 0.80 & 0.80 \\
**Std. Dev.** & 0.03 & 0.04 & 0.06 & 0.03 & 0.03 & 0.04 & 0.03 & 0.03 & 0.04 \\
**Max-Min** & 0.11 & 0.14 & 0.20 & 0.10 & 0.09 & 0.13 & 0.08 & 0.10 & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Runout results (mm) of our system for 3 different cold workpieces (Figure 11), measured 10 times by 3 different operators. Figure 12 presents a boxplot of the data collected in Table 3. Each box represents the 10 measurements taken by an operator. The central marker shows the median of the samples, the bottom and top edges of the box indicate the 25 th and 75 th percentiles, respectively. The whiskers are the maximum and minimum obtained runout values. As previously discussed, the manufacturing process induces most of the variance in the form of workpieces with three different runouts (0.67 mm, 0.8 mm and 0.8 mm, respectively). On the other hand, the variance induced by each operator and our measuring system is less significant (largest standard deviation--0.06 mm--in workpiece 1, operator 3).
## 2 Deformation Rate Threshold-Based Solution for Slope Safety Factor
### Slope Destabilization Criteria  
The judgment of the critical state of the slope is a key problem of the strength discount method, and different discrimination methods will produce large differences. At present, there are mainly three kinds of slope instability judgments adopted by numerical calculation methods: calculation convergence, displacement mutation, and plastic zone penetration. These criteria have their own characteristics and applicability, and in many cases, they may not be suitable for judging the destabilization of rocky slopes. For example, the deformation and stability of rocky slopes are controlled by the structural surface, and the rock itself will not undergo a large deformation and enter into the plastic state, so the plastic zone penetration criterion is not applicable.  
Slope destabilization damage has a more complex internal mechanism, resulting from a combination of various factors. External visualization of the slope body reveals accelerated movement, the emergence of large deformations that cannot be restored or stabilized, as well as macro-expression of cracks, including those observed through whole sliding, and other damages. Many scholars have carried out extensive on-site monitoring of slopes and concluded that the evolution of slope deformation and instability has the following characteristics: when the deformation rate of the slope body is kept at a low level of fluctuation, the slope is basically stable; when the slope is subjected to external disturbances, the stability of the slope is reduced, and the deformation rate begins to grow; and when it reaches a certain threshold, the slope will not be able to maintain stability, entering into a state of instability. This law has been successfully applied to the landslide near-slip prediction [24; 25]. It can also be applied to the numerical analysis of slope stability as a criterion of slope destabilization.  
This paper introduces the deformation rate index in the landslide near-slip prediction, determines the warning threshold of slope instability deformation rate by analyzing the deformation monitoring data and trend prediction of the key points, and replaces it with the convergence criterion of the calculation that comes with the 3 DEC 5.2 software to solve the safety coefficient of rocky slopes.
# Nanoformulation of Peptides for Pharmaceutical Applications: In Vitro and In Vivo Perspectives
## 6 Mechanism of Nanoformulated Peptides in Disease Theranostics
### Self-Assembled Peptide  
Surface PEGylation is widely used to improve the effectiveness of drug delivery toward targeted cells and genes, as it delays phagocytic clearance and leads to a longer circulating time [139]. However, the major challenges in these surface PEGylated particles are the loss of therapeutic efficacy and an increment in the side effects after repeated administration of the anti-PEG antibody after immunization with PEGylated proteins [140]. Several studies have identified that the self-assembled peptides were highly beneficial in inhibiting the growth of targeted cells affected by a disease, especially cancer cells. [PERSON] et al. (2013) demonstrated that self-assembled peptides designed using human CD47 have the ability to minimize the phagocytic uptake, prolong circulation time, enhance tumor accumulation, and improve the delivery of nanoparticles toward the tumor site, compared to PEGylation-modified particles in non-obese diabetic (NOD) acid gamma (NSG) mice [141]. Further, [PERSON] et al. (2019) successfully designed a tumor microenvironment-adaptable self-assembly (TMAS) of short peptides via the synthesis of a pentapeptide FF-Amp-FF (AmpF). This pentapeptide AmpF has been identified to possess the ability to assemble them into super-helics in a neutral solution and transform them into nanoparticles at low pH conditions due to the cis/trans isomerization of proline amide bonds. This adaptability of the peptide-based nanoparticles has enhanced their circulation times and improved their accumulation and retention at tumor sites [142]. Furthermore, [PERSON] et al. (2014) designed a TMAS nanoformulation of hybrid peptide nanoparticles via conjugation of functional 3-diethylaminopropylisothiocyanate (DEAP) molecules with short peptides, which transform based on the pH of the tumor environment [143]. In addition, several reports showed that nanoformulated self-assembled peptides can be potential therapeutic agents in the treatment of various diseases in the future [144; 145; 146].  
Figure 3: Mechanisms of peptide-decorated polymeric nanomedicines in the blood stream, brain tumor therapy, and cellular and intracellular environments. Reproduced with permission from [PERSON] et al. (2018) [138], ©Elsevier, 2018.
## 3 Circuit Implementation
### The Integrated Bias Circuit  
The linearity of the power amplifier is closely related to its bias voltage. CMOS integrated bias circuits can be employed to optimize the power amplifier's linearity, which is one of the advantages of CMOS technology. Additionally, the implementation of an integrated bias circuit eliminates the necessity for ESD protection circuits due to external bias voltages, thereby improving system integration and simplifying the design.  
In this work, the bias voltages of the power amplifier are adjustable and are shown in Figure 17. The common-source transistor voltage can be adjusted within the range of approximately 400 mV to 680 mV, while the common-gate transistor voltage can be tuned between approximately 1.9 V and 2.9 V. By adjusting the bias voltages, it is ensured that under all PVT conditions, the voltage across each power transistor remains within its maximum tolerable limit, thereby ensuring the reliability of the power amplifier and long-term operational stability of the design. Additionally, at different bias voltages, the output impedance of the common gate bias circuit undergoes significant changes. To prevent the impact of output impedance variations on the PA's linearity, this paper adds a voltage buffer following the adjustable resistor in the common-gate bias circuit (Figure 17b) to stabilize circuit output impedance.  
Figure 16: Comparison of IMD3 with and without MGTR.  
Figure 17: Schematic of integrated circuits. (**a**) Common source. (**b**) Common gate.
## 2 Dynamic Multiple Receiver  
The different possible combinations of multiple Rx's with single Tx WPT system schematic model is shown in Figure 1. The Tx is supplied using high frequency inverter/power amplifier and the Rx coils are connected to the load. Both the Tx and Rx coils are configured in series-series topology of resonant capacitor and inductor. Parasitic resistance of all the elements are combined with the source and load port resistance of the respective coil. Figure 1a represents the four Rx's are in a static condition with same resonant frequency and placed at a fixed distance between Tx and Rx [32]. In this case, the cross-coupling among the Rx's will be a major concern. In Figure 1b the multiple Rx's designed with dissimilar resonant frequency and placed at a different distance is illustrated. Similarly, the Figure 1c shows the identical coil with same resonant frequency placed at a different distance between the Tx and Rx [33]. The main issue in identical resonant frequency configuration is the cross-coupling effect among the Rx's will be more and its minimum in the non-identical resonant frequency configuration, however, the bandwidth of the frequency among the Rx must be large. The third configuration which represents the dynamic condition of the Rx's will produce both cross coupling and frequency splitting whenever the coupling is increased more than the critical coupling point.
## 3 Results and Discussion
### Combined Process
#### 3.3.2 Total Organic Carbon Monitoring
In other words, it was proven that H\({}_{2}\)O\({}_{2}\) presented a lower mineralization efficiency, probably because it is not possible to produce strong oxidants during the entire reaction. However, TiO\({}_{2}\) has a higher cost than H\({}_{2}\)O\({}_{2}\). Thus, it is possible to conclude that the TOC decay results obtained in the present work are satisfactory. The present work proposed the application of the Fenton technique combined with adsorption, which proved promising in reducing the level of total organic carbon in the final treated solution when compared to the results reported in the literature.
## 4 Hardware Description
### Description of Hardware Components
#### 4.2.5 ESP 8266 Wi-Fi Modem  
The Wi-Fi Module ESP8266 is an autonomous chip system (SOC) [41]. TCP/IP protocol stack is interlaced with which the microcontroller has Wi-Fi connectivity. AT command set firmware is already in place for the module. The processing and storage capabilities are solid. It can be coupled via GPI0s with the sensors. An external logic level converter is required for the ESP8266 module. There are 8 pins in the module. The pins of the module are VCC: Power 3.3 V, GND: Common Ground, RX (Receiver): It receives data transmitted by host, TX (transmitter): It transmits data received by the host, RESET: connected to the ground while flashing.  
GPI00: It is used as the program pin by the firmware. It must be low while flashing the ESP 8266 module, GPIO2: It is free GPIO and CH_PD: It is the power down signal which pulled to VCC (3.3 V).
## 3 Analysis of TEE Usage in Edge Intelligence and IoT
### Analysis of SGX in Edge Intelligence and IoT
#### 3.3.3 Performance Degradation Issues  
In resource-constrained edge scenarios, the issue of SGX's performance degradation has drawn significant attention. SGX's performance degradation primarily arises from two sources, the overhead of data encryption and decryption, and the performance cost associated with entering and exiting the enclave during system calls or EPC page fault [66]. Encryption and decryption operations are performed by hardware, leaving limited room for optimization. In contrast, various approaches have been proposed to optimize the performance overhead caused by context switching. Hotcalls [28] based on a synchronization spin-lock mechanism significantly improve performance compared to Ecalls and Ocalls. The work in [67] achieves exitless system calls by delegating system calls to threads running outside the enclave, and performance overhead due to page faults is mitigated by implementing paging within the enclave. Another more general solution involves leveraging a LibOS, such as Gramine [35] and Occlum [36]. The LibOS approach can streamline interactions between the enclave and the application, potentially reducing the overhead associated with traditional system calls and improving overall performance. Next, we will provide a detailed discussion of the performance overheads of different SGX implementations in edge intelligence scenarios through a qualitative analysis and a quantitative assessment.
## 5 Experimentation and Results
### Dataset--Cavity  
The cavity training set also has four cavity sizes, 1.2 m \(\times\) 1.0 m \(\times\) 0.8 m, 1.3 m \(\times\) 0.8 m \(\times\) 0.8 m, 1.5 m \(\times\) 1.0 m \(\times\) 0.9 m, and 1.6 m \(\times\) 1.3 m \(\times\) 1.0 m, and the number of training/validation samples is 320 and 20. Samples for test were built based on the four cavity sizes which were used for training the dataset, but the shape is different with training data.  
The cavity dataset generated by cutting the volume arbitrary based on the rectangular-box-type cavity is shown in Figure 9. Figure 10 shows the rectangular-box-type cavity finite element model and one sample of the arbitrary shape in the first column. The first to the fifth mode shape and natural frequency are arranged in the order of columns. Cavity natural frequency is inversely proportional to the boundary length of each direction, as explained in Equation (5), and the mode shapes are expressed by Equation (6) [18]. Where  
Figure 8: Flat/morphed panels natural frequency and mode shapes 1 st to 5 th mode: (**a**) panel size 0.4 m \(\times\) 0.6 m and 0.5 m \(\times\) 0.6 m; (**b**) panel size 0.6 m \(\times\) 0.6 m and 0.7 m \(\times\) 0.6 m.  
\(L_{x}\), \(L_{y}\), and \(L_{z}\) are the cavity boundary lengths in each direction, \(i\), \(j\), and \(k\) are the number of nodal points of the cavity in each direction, and c is the speed of sound.  
\[f_{ijk}=\frac{c}{2}\sqrt{(\frac{i}{L_{x}})^{2}+(\frac{j}{L_{y}})^{2}+(\frac{k}{L _{z}})^{2}} \tag{5}\]  
\[\Psi_{ijk}=cos\frac{i\pi x}{L_{x}}cos\frac{j\pi y}{L_{y}}cos\frac{k\pi z}{L_{z}} \tag{6}\]  
The cavity dataset is augmented by cutting the volume from the box-type cavity, so the cavity dataset has a higher frequency than the box-type cavity. But the cavity mode shape does not change significantly compared with the morphed panels.  
The first natural frequency range of each size of the cavity dataset is 143-187 Hz, 132-173 Hz, 115-143 Hz, and 108-127 Hz, respectively.