esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### CpoxeRNA Is an Erythroid-Specific Enhancer RNA  
Among the eRNAs induced in erythroblasts (Figure 1B), we identified an eRNA transcribed upstream of the _Cpox_ gene (Figure 2A). This eRNA is located within the same super enhancer as the _Cpox_ gene. ENCODE H3K27 ac ChIP-seq data from mouse B cell (CH12.LX), mouse embryonic stem cells (mES) and erythroid cells (MEL), and ATAC-seq data from mouse erythroid cells (G1E) and mouse erythroblasts indicate that this genomic region has chromatin characteristics consistent with being an erythroid-specific enhancer (Figure 2A).  
RNA-seq data from mouse E14.5 fetal liver erythroid progenitor cells (FLC PROG) and erythroid cells (FLC ERY) [24] reveal that this eRNA is predominantly expressed from the antisense strand in fetal liver erythroblasts, while a much lower level of transcription occurs from the sense strand (Figure 2A, bottom), consistent with the bidirectional transcription feature of active enhancers [19; 70]. In earlier work, we had named this uncharacterized transcript _CpoxeRNA_[71]. The 5\({}^{\prime}\)-RACE experiment confirmed the bidirectional transcription of _CpoxeRNA_ (Supplementary Figure S1A). A 3\({}^{\prime}\)-RACE experiment was conducted to determine the full length of _CpoxeRNA_ (Supplementary Figure S1B). Two isoforms of _CpoxeRNA_ (1262 nt and 1656 nt) were identified in induced MEL cells with alternative 5\({}^{\prime}\) ends, each containing four exons (Supplementary Figure S1C).  
RT-qPCR analysis in both mouse MEL cells [71] and ex vivo differentiated E14.5 mouse fetal liver cells showed that _CpoxeRNA_ is induced during terminal erythropoiesis (Figure 2B). Tissue expression analysis further indicates that _CpoxeRNA_ is an erythroid-specific eRNA [71]. CPC2 analysis confirms that _CpoxeRNA_ is a non-coding RNA (Supplementary Figure S2A), and vertebrate 60-way conservation analysis shows that it is rodent-specific (Supplementary Figure S2B). Cell fractionation RT-qPCR experiments and fluorescence in situ hybridization demonstrate that _CpoxeRNA_ is primarily localized in the cell nucleus (Figure 2C,D). Thus, _CpoxeRNA_ is a predominantly nuclear, non-coding RNA specific to erythroid cells that is transcribed antisense to the neighboring Cpox gene.
## 3 Methodology
### Structure Deterioration Identification
#### 3.2.4 Establishment of Bridge Model Update Process
A reference fitting plane is selected for the point cloud data, and basic points of detection are chosen based on the distance difference between corresponding points and the plane, as well as the distance to the center point. In the overall deformation analysis of bridges, rigid body transformations can be represented as \(\mathrm{T(x)}=\mathrm{R_{x}+t}\), where R is the rotation matrix and t is the translation vector. The objective function to be minimized is as follows: \(\mathrm{E(R,t)}=\sum_{i=1}^{n}{\left\|{\mathrm{R}\mathrm{x}_{i}+t-y_{i}} \right\|^{2}}\). This calculation of the overall deformation can be further completed using algorithms and models in Matlab or Python. For the hierarchical deformation analysis of bridge components, the deformation amount can be calculated according to the following formula:  
\[\mathrm{\Delta L}=\sqrt{(X_{2}-X_{1})^{2}+(Y_{2}-Y_{1})^{2}+(Z_{2}-Z_{1})^{2}} \tag{14}\]  
In the algorithm, the bending theory formula of beams \(\mathrm{k}=\frac{1}{R}=\frac{\mathrm{M}}{\mathrm{EI}}\) and the strain formula \(\mathrm{\epsilon}=\frac{\mathrm{\Delta L}}{\mathrm{L}}\) are combined for component deformation analysis. Here, k is the curvature, R is the radius of curvature, M is the bending moment, E is the elastic modulus, I is the moment of inertia of the cross-section, \(\varepsilon\) is the strain, \(\Delta\)L is the length change, and L is the original length. Thus, the analysis of the overall bridge deformation and component-level deformation can be achieved. The overall deformation analysis primarily focuses on rigid body displacement and rotation, while the component-level deformation analysis is more concerned with local bending, compression, and tensile deformation. Utilizing these analysis results, the health status of the bridge can be assessed, and scientific bases for maintenance and reinforcement can be provided. Compared to manual methods, the automation of point cloud data processing and deformation analysis offers advantages in efficiency, precision, and repeatability.
## 3 Experimental Results and Discussion  
When the pump power is above the mode-locking threshold value of about 75 mw, mode-locking can be easily established with an output power of around 0.6 mw. The optical spectrum of output1 from adjusting the PC3 is shown in Figure 4. The fiber laser operates at the wavelength of 1552.9 nm,  
Figure 1: The experimental setup of the proposed mode-locked fiber laser with CVB generation based on CNT-SA.  
Figure 3: Measured reflection spectra of the few-mode FBG under the different values (0–5 \(\upmu\)m) of the OSS.  
Figure 2: (a) Sketch map of offset splicing the SMF and TMF, (b) micrograph of the OSS after splicing the SMF and the TMF.  
coinciding with the fundamental mode of the TM-FBG reflection spectrum. Due to the narrow reflected spectrum of the TM-FBG added into the cavity, the 3-dB spectral width is less than 0.02 nm. The mode-locked pulse train which is shown in Figure 5 has a period of 91.7 ns, as expected from the cavity length. The fundamental repetition rate is around 10.8 MHz.  
When the mode-locked fiber laser operates at the wavelength of 1552.9 nm, the intensity distributions from Output2 were monitored with a CCD camera. After the PC3 in the laser cavity was carefully adjusted to ensure the mode-locking operation and to obtain pulsed CVBs, different vector modes with radial and azimuthal polarization states could be reached by controlling the orientations of PC1 and PC2 to eliminate the degeneracy of the second-order mode from the TM-FBG. The doughnut-shaped intensity profiles of both radially and azimuthally polarized beams were recorded by CCD camera, as shown in Figure 6a,f. In order to discriminate between radially and azimuthally polarized beams, a linear polarizer that can be rotated in a circle was inserted between the collimator and the CCD camera. The results with an arrow, indicating the transmission direction of the polarizer, are showed in Figure 6b-e,g-j. Two lobe-shaped intensity patterns are in the same direction as the linear polarizer in Figure 6b-e, indicating that the generated beam was radially polarized. In contrast, the directions of the two-lobe-shaped intensity patterns are always perpendicular to the orientation of the polarizer in Figure 6g-j, which indicates that the output laser beam is azimuthally polarized. The purity of both radially and azimuthally polarized beams is estimated to be about 90%.  
Figure 4: Reflection of the TM-FBG and mode-locked spectra.  
Figure 5: The pulse train of the mode-locking state.
## 3 Results and Discussion
### Development of HPLC-FD
#### 3.2.3 Selectivity and Chromatographic Parameters  
The method selectivity was assessed by carrying out blank experiments using a synthetic mixture of the pharmaceutical inactive ingredients (25 mg of each of starch, lactose monohydrate, microcrystalline cellulose, and hydroxypropyl cellulose). The chromatogram of this blank sample did not show any chromatographic peaks at the retention times of either DUV or SEL, indicating the selectivity of the HPLC-FD method. The peak areas, retention times, capacity factors, and other chromatographic parameters were calculated. The formula used to calculate the retention factor (k\({}^{\prime}\)) for DUV and SEL is: k\({}^{\prime}\) = (t\({}_{\rm R}\) - t\({}_{0}\))/t\({}_{0}\); where t\({}_{\rm R}\) and t\({}_{0}\) are the retention times for the peak of interest and the solvent front,  
Figure 10: A representative chromatogram of standard solutions of DUV and SEL. The concentrations of DUV and SEL injected into the HPLC-FD system were 1.6 and 0.8 \(\upmu\)g/mL, respectively. Both solutions were in methanol.  
respectively. The equation \(\alpha\) = k\({}^{\prime}\)2/k\({}^{\prime}\)1; where k\({}^{\prime}\)1 and k\({}^{\prime}\)2 are the retention factors of DUV and SEL, respectively, was used to compute the separation factor or selectivity (\(\alpha\)). At 10% of the peak height, the peak asymmetry factor was computed. The resolution factor (R\({}_{\rm s}\)) was computed using the formula: R\({}_{\rm s}\) = (t\({}_{\rm 2}\) - t\({}_{\rm 1}\))/0.5 (w\({}_{\rm 2}\) + w\({}_{\rm 1}\)); where t\({}_{\rm 2}\) and t\({}_{\rm 1}\) are the retention times of the DUV and SEL peaks, and w\({}_{\rm 2}\) and w\({}_{\rm 1}\) are the peak widths that were detected by intersecting the tangent with DUV and SEL base line.  
Using the equation N = 16 (t\({}_{\rm R}\)/w)\({}^{\rm 2}\); where w is the peak width, the column plate number was computed. The chromatographic parameters were measured, along with the system's suitability and efficacy; the findings are shown in Table 2.
## 2 Knowledge Representation for VSP Wavefield Separation
### Directional Difference Knowledge Representation  
In the VSP records, the upgoing and downgoing waves exhibited significant directional differences. The zero-mean characteristic of the VSP wavefield is shown in the VSP wavefield diagram of the layered model in Figure 5. There is a notable difference between the upward and downward waves, which can be represented by the angle between them; the wavefield mean approaches zero. Based on this observation, we introduced cosine similarity to characterize this knowledge representation. Cosine similarity is defined as the cosine of the angle between two vectors, with values ranging from [-1, 1].  
When two vectors are orthogonal, the cosine similarity is 0, and when two vectors are identical or opposite, the value is 1 or -1. However, because the VSP wave-separation problem scenario includes negative signals, we retained the cosine similarity in the interval [0, 1] through an absolute value operation. Let \(\widetilde{U}\) be the predicted upgoing wavefield and \(\widetilde{D}\) be the predicted downgoing wavefield. Then, the cosine similarity is defined as  
\[\cos(\widetilde{U},\widetilde{D})=\frac{\widetilde{U}\cdot\widetilde{D}}{\left\| \widetilde{U}\right\|\left\|\widetilde{D}\right\|}, \tag{9}\]  
where \(\widetilde{U}\cdot\widetilde{D}\) represents the dot product of the two wavefields, and \(\left\|\widetilde{U}\right\|\) and \(\left\|\widetilde{D}\right\|\) represent the L2 norms of the two wavefields, respectively. In the knowledge representation of wavefield difference, we used the absolute value of the cosine similarity as a measure of the directional difference:  
\[L_{direction}=\left|\cos(\widetilde{U},\widetilde{D})\right|. \tag{10}\]  
Figure 5: Diagram illustrating the wavefield directional differences and zero-mean characteristics. The mean value of the VSP wavefield is \(9.199\times 10^{-8}\), where \(\alpha\) represents the propagation difference between upgoing and downgoing waves, which is characterized by cosine similarity.
# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 4 Case Studies
### Step 2: Agent Configuration
#### 4.2.4 Classification Agent  
The classification agent is responsible for filtering irrelevant clusters and assigning an appropriate label to each relevant cluster. As shown in Figure A5, this agent allows the configuration of a zero-shot classifier based on an LLM. The classification agent is critical for further processing of the clusters. It uses a pre-trained language model driven by JSON-based grammar. This grammar defines the output parameters and allows the model to determine the relevance of the clusters based on prompt instructions.  
* Grammar and JSON schema--Grammars define the outputs of the language model. They rely on a prompt template adopting its fields. Grammars were created as JSON schema and translated by ODET into a format that can be processed by an AI model. Both case studies used a schema that included a Boolean value for detecting disaster relevance. An example is shown in Figure A4.
* Prompt template and objective--This template provides specific instructions for creating the headline, rating the relevance, and identifying the location. The goal of the agent is to determine the relevance of the clusters by answering the question of whether the content is disaster-related. It is possible to answer multiple questions simultaneously to save steps. In the context of the case studies presented in this paper, the agent was also asked for a possible location for the described context as well as for a headline summarizing the events. The results are output in a predefined format and structured based on the defined JSON grammar. An example is shown in Figure A9.
* Mapper and object filter--The classification agent has a mapper that maps objects and a filter that removes irrelevant clusters based on the classification results. If a cluster was marked as irrelevant (\"false\"), it was removed from the pipeline. An example is shown in Figure A7. The mapper and object filters can be used to create complex filter rules that remove unwanted objects or process them in a targeted manner. Onthe other hand, objects can be grouped or processed in a targeted manner. This was made possible by the generated JSON output.
* Input and Output--The agent receives its input data from the embedding agent and outputs filtered and classified clusters with associated headings. The data are then passed to the next agent.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.25 Transmitting Heading Device (THD)  
The true heading of a ship is given through the Transmitting Heading Device (THD). The \"True Heading\" is defined by the IMO as _\"horizontal angle between the vertical plane passing through the true meridian and the vertical plane passing a through the craft's fore and aft datum line. It is measured from true north (000\({}^{\circ}\)) clockwise through 360\({}^{\circ}\)\"_[82]. The THD produces a signal for the true heading to other equipment onboard the ship [83]. The ships which are not equipped with the gyro-compass, and are of gross tonnage between 300 and 500 are required to carry a THD or alternative equipment which transmits heading information [82].
## 4 Conclusions  
The possibility of using passive or active control for reducing self-excited vibrations of a mechanical system subjected to friction-induced vibration is nowadays a crucial point. In the present  
Figure 11: Influence of the control gain \(\gamma\) on controlled \(X_{1}\). (**a**) \(\mu=0.4\). (**b**) \(\mu=0.7\).  
Figure 12: Influence of the signal-to-noise ratio (SNR) on control performances on \(X_{1}\) ( uncontrolled; controlled). (**a**) \(\mu=0.4\) (**b**) \(\mu=0.7\).  
study, an active control design is tested to determine whether it is possible to reduce drastically nonlinear vibrations due to mono or multi-instabilities for a simplified four-degrees-of-freedom model. Some non-exhaustive interesting conclusions can be addressed:  
* The use of active control makes it possible to significantly reduce periodic or quasi-periodic self-sustained vibrations (i.e., in the case of mechanical systems subjected to one or more unstable modes);
* The control gain \(\gamma\) has a significant influence on system control performances. This is particularly important in a practical case where the gain has to remain on physically feasible nominal values in terms of design particularly for the amplification and the actuation part;
* The robustness of the control to the signal-to-noise ratio is proven. For the proposed study, the control remains effective up to a minimum value equal to 15 dB;
* Using nonlinear active control seems useless in the present case. Effectively, due to the fact that the control is started at the beginning of the self-excited oscillations, a simple active control based on the linearized system dynamics at the equilibrium point is sufficient. Finally some non-exhaustive interesting further studies can be considered:
* Robustness of the control in the case of other inaccuracies possible in real systems, for example time delay, time discretization;
* Extension of control efficiency to a more real industrial brake system;
* Experimental validation of the potential of an active control design based on full and partial linearization feedback;
* Active control algorithm based on the receptance method to control the system without the knowledge of mechanical properties including nonlinearities in the case of experimental validation;
* Extension of the method in the case of stick-slip vibrations.  
Conceptualization, J.-J.S.; Data curation, B.C. and J.-J.S.; Investigation, B.C.; Methodology, J.-J.S.; Validation, B.C.; Visualization, B.C. and J.-J.S.; Writing--original draft, B.C. and J.-J.S.; Writing--review and editing, B.C. and J.-J.S.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
J.-J.S. acknowledges the support of the Institut Universitaire de France.  
The authors declare no conflict of interest.
## 3 Linear Stability Analysis
### Stability Analysis of MTF with CAVs and RVs  
Based on the traditional common car-following model, [PERSON] [23] studied the analytical method for the stability of heterogeneous traffic flows, deriving the following criterion on the stability of heterogeneous traffic flows:  
\[\sum_{n}^{N}\left(\left[1/2(f_{n}^{v})^{2}+f_{n}^{\Delta v}f_{n}^{v}-f_{n}^{s} \right]\left[\prod_{m\
eq n}f_{m}^{s}\right]^{2}\right)<0 \tag{33}\]  
Later, [PERSON] et al. [11] extended the above formula to the car-following model for MTF, as follows:  
\[\begin{array}{l}N(1-p)\left[\frac{1}{2}(f_{K}^{v})^{2}-f_{K}^{\Delta v}f_{K}^ {v}-f_{K}^{s}\right]\left[(f_{K}^{s})^{N(1-p)-1}(f_{A}^{s})^{N(1-p)p}(f_{C}^{s} )^{Np^{2}}\right]^{2}\\ +N(1-p)p\left[\frac{1}{2}(f_{A}^{v})^{2}-f_{A}^{\Delta v}f_{A}^{v}-f_{A}^{s} \right]\left[(f_{K}^{s})^{N(1-p)}(f_{A}^{s})^{N(1-p)-1}(f_{C}^{s})^{Np^{2}} \right]^{2}\\ +Np^{2}\left[\frac{1}{2}(f_{C}^{v})^{2}-f_{C}^{\Delta v}f_{C}^{v}-f_{C}^{s} \right]\left[(f_{K}^{s})^{N(1-p)}(f_{A}^{s})^{N(1-p)p}(f_{C}^{s})^{Np^{2}-1} \right]^{2}<0\end{array} \tag{34}\]  
Simplifying Formula (34) yields the following results:  
\[\left\{\begin{array}{l}(1-p)F_{R}+(1-p)pF_{A}+p^{2}F_{C}<0\\ F_{R}=\frac{\frac{1}{2}(f_{K}^{v})^{2}-f_{K}^{\Delta v}f_{K}^{v}-f_{K}^{s}}{(f _{K}^{s})^{2}}=\frac{ff_{R}}{(f_{K})^{2}}\\ F_{A}=\frac{\frac{1}{2}(f_{A}^{v})^{2}-f_{A}^{\Delta v}f_{A}^{v}-fabscissa axis (velocity) at points 0.72 and 5.424, which are the critical speeds at which the RV reaches stability. When \(p>0.104\), that is, the penetration rate of CAVs exceeds 10.4%, the MTF can reach stability at any equilibrium speed. When the speed is less than 0.72 m/s or greater than 5.424 m/s, the MTF is stable regardless of the penetration rate of CAVs.
# On Transparency and Accountability of Smart Assistants in Smart Cities
## 4 User Studies
### User Study 1
#### 4.1.1 Design  
Android Applications are typically distributed through app stores, and users download them to meet different needs [63]. The responsibility of assessing an app's risk lies in the users [44]. At this stage, app stores present app descriptions and their permission requirements to potential app users. These users are supposed to read and understand the descriptions of an app and review its permission requirements to decide whether to select and install a particular app or not [64]. Users install an app only when its perceived benefits exceed perceived risks [65]. Thus, the download stage becomes the first line of defense for users to protect their privacy by weeding out privacy-intrusive apps [66].  
Since manipulation of information presented to app users in app stores can affect user privacy and device security, regulations such as GDPR require app providers to take into consideration user expectations [7]. Therefore, we can reasonably assume that a privacy respecting app provider shall take into consideration user expectations. Such behaviors of app providers can be assessed by determining whether app behaviors are within user expectations. Moreover, user expectations can be assessed via their perception in combination with their judgment while reading app descriptions and reviewing its permission requirements for making choices at the download stage [67; 68]. A gap between users' expectations and actual data access by an app indicates the possibility of misuse of access to sensitive data [69].  
Therefore, we designed an online user experiment to measure the users' expectations regarding the data access needs of GA by reviewing its description. We compared user expectations with actual GA permission requirements to determine the gap in expected versus actual data access requirements (permissions). The first user study was conducted to seek an answer to the following question.  
_Do GA permission requirements match user expectations?_  
We designed an online user survey to collect data from smartphone users. We collected these data anonymously. The purpose of data collection was clearly conveyed to the data subjects. Participation in the study was voluntary. Since no sensitive data collections were involved in this study, and all the potential participants were adults, the study did not need ethical approval.
# Trajectory Tracking of Autonomous Vehicle Using Clothoid Curve
## 2 Related Work
### Control System Delay and Hysteresis  
The steering control command generated by the control module needs to go through multiple nodes, such as the processing of the interface driver and the CAN bus transmission, which will bring in the communication delay of the control command. It also takes a certain amount of time for the steering servo system to receive the steering control CAN command and drive the actuator to achieve the target angle. This response time is called the response hysteresis of the steering system. The communication delay of the control system and the response delay of the steering system result in a situation where the control command cannot be realized immediately. If proper compensation is not made, it would cause the system to become unstable or limit the speed of the autonomous vehicle [19]. Therefore, this paper proposes a vehicle future state prediction method to deal with the control system delay phenomenon, and proposes a preview control method based on the clothoid curve combined with the response hysteresis problem of the steering system.
## 5 Analysis of 1-LSB, 2-LSB and 3-LSB Schemes in Embedding Stage
On the contrary, the PSNR-HVS-M values of the 3-LSB method are slightly higher than the result from 2-LSB. However, the 2-LSB results are still acceptable, and, in general, their PSNR and SSIM values are superior to the other implementations in this study. Consequently, the 2-LSB variation was utilized for the proposed SR-HTR method considering the results given in Tables 1-3.