esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Transcription-Coupled Repair (TCR): RNA Polymerase as the DNA Damage Sensor
### Does UvrD Mediate an Alternative TCR Pathway?  
UvrD has recently emerged as a potential key player in TCR [29]. Although UvrD also participates in the removal of excised damaged oligonucleotide, a feature common to both GGR and TCR, it was observed that cells lacking UvrD could still support GGR but were defective in TCR [148], suggesting that the major role of UvrD is in TCR. UvrD has been reported to mediate RNAP backtracking at the site of UV lesions, which facilitates the repair of the lesion by the core NER machinery [28]. We note, however, that it has not been explicitly shown that UvrD-mediated RNAP backtracking results in the preferential repair of the transcribing strand.  
During the proposed UvrD-mediated TCR, the stalled RNAP is pulled backwards to expose the lesion so that is can be detected and repaired by UvrABC [138]. Although the recruitment of UvrA\({}_{2}\)UvrB\({}_{2}\) complex to the lesion by UvrD has not been directly documented, it has been suggested in the study by [PERSON] lab [28], based on the observed physical interaction between UvrD and UvrB [149; 150] and between NusA and UvrA [151]. In contrast to Mfd-mediated repair, which causes transcription termination and release of the nascent transcript, the backtracking of RNAP and repair mediated by UvrD can occur without causing termination [28].  
UvrD belongs to the SF1 superfamily of helicases and translocates on ssDNA with a 3\({}^{\prime}\)\(\rightarrow\)5\({}^{\prime}\) directionality [152; 153]. Monomeric UvrD can translocate on DNA, however, only the dimeric UvrD has helicase activity [154]. UvrD is a highly abundant protein, with copy number comparable to RNAP [24] and it form a stable complex with RNAP [28].  
Based on these observations, it has been proposed that RNAP might be \"pre-loaded\" with UvrD, even before the lesion is encountered [24]. UvrD uses its C-terminal domain [155] to interact with the \(\beta\) and \(\beta^{\prime}\) subunits of RNAP and binds to the upstream edge of the transcription bubble [28]. Notably, although UvrD and Mfd appear to bind to non-overlapping surface of RNAP, they cannot engage RNAP simultaneously [155]. Upon encounter of DNA damage, levels of UvrD rise as part of the SOS response [156; 157]. This could promote dimerization of UvrD on RNAP, resulting in polymerase backtracking and TCR.  
UvrD-mediated RNAP backtracking is assisted by the transcription elongation factor NusA and ppGpp. NusA binds to the flap region of the \(\beta\) subunit of RNAP [158; 159; 160] as it leaves the promoter region and could compete with Mfd for binding to RNAP [161]. ppGpp is a bacterial 'alarmone' whose cellular concentration increases during SOS response [162]. Binding of ppGpp to RNAP causes it to adopt a conformation that is prone to backtracking [163]. Taken together, these interactions could facilitate the alternative UvrD-mediated TCR pathway [29].
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 5 ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
### Discussion of Design Details
In our algorithm, we can't conduct a frequency-hopping mechanism because ACE uses connectionless communication, which has no time synchronization between nodes so that a frequency-hopping sequence cannot be maintained. Scanning on a fixed channel will reduce the communication reliability of nodes when selecting a poor-performing channel. Therefore, adaptively selecting a good link quality channel for each node is always a challenging problem. The BLE specification [15] foresees two mechanisms to improve the link-layer reliability: adaptive frequency hopping with channel blacklisting and physical (PHY) mode adaptation. However, they can only be used for connection-oriented BLE communication, which is not suitable for our low overhead routing algorithm. Besides, using a mechanism like retransmission can make up for the temporary poor link quality to ensure network reliability. For this reason, we do not add additional link quality estimation strategies to keep a low communication or management overhead for timely communication to our first algorithm version. In future work, we may filter the bad link quality channel for a higher reliability communication through blacklisting bad channel adaptively. **Tradeoff between compatibility and performance:** A standard Bluetooth Mesh node conducts broadcast and scanning on three ADV channels, which is incompatible with ACE for its specific broadcast and scanning channel mechanism. To enable our routing algorithm to be compatible with standard Bluetooth Mesh nodes, we should add at least one of the three ADV channels to each node's scanning and broadcast channel lists to send or receive messages from standard Bluetooth Mesh nodes. However, doing so can improve compatibility but significantly reduce network performance because multi-channel listening and broadcasting will sacrifice the advantage of low delay and reduce reliability. Therefore, better compatibility with existing Bluetooth Mesh devices remains future work.
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 7 Experiment in Laboratory Environment
### Experiment Facility and Room Configurations  
The experiment system can be generally separated into two independent subsystems: the audio playback system and the binaural signal recording system. The general structure and connection relationship of the systems is shown in Figure 10, and the setup of the experiment is shown in Figure 11a. In this system, the source signal is played by the audio playback system via spatially placed loudspeakers. Then, the emitted signal then propagates through the space and is captured by the recording system. Notably, The recorded signals are pre-processed and delivered to another computer for the localization. The later content includes the more detailed configurations of the two systems.  
In the audio playback system, 30 loudspeakers are placed on a regular dodecahedron. The loudspeakers are fixed on the middle of the edges and face to the center of the dodecahedron, as shown in Figure 11a. Figure 11b demonstrates the ground truth loudspeaker array placement positions and their corresponding labels in the Cartesian coordinate system. All loudspeakers are driven by the Dante audio network system, as shown in Figure 12a, which is controlled by a computer. As for the recording system, the signals are captured by two Type 40 AG polarized pressure microphones planted in the ear canals of the dummy head. The captured signals are then converted via the U-PHORIA UMC20 HD audio interface and transferred to another computer via a USB connection.  
Figure 10: The experiment outline: The audio playback system and binaural recording systemIn the experiment, we first switch on the recording system, and then play the source signal with one of 30 loudspeakers with selected positions. The signals captured by the recording system are converted and delivered to the localization systems, and the estimated source location can be obtained. In such a system setup, the generation of directional audio signal and the process of localization are physically isolated, so the performance of the localization entirely relies on the recorded signals, and no prior knowledge about the source signal and environmental acoustic configurations can be obtained by the localization system. Therefore, the robustness and flexibility of the proposed methods can be examined.
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 3 Proposed Method
### Saturation Component Enhancement  
Generally, the saturation component enhancement algorithms are simple linear transformations. In this paper, we use the segmentation exponential enhancement which is a nonlinear algorithm that can deal with the saturation of different regions separately to achieve better visual effects. According to the saturation component, three regions called as high, medium, and low are defined. If the saturation component image is enhanced globally, the region with high saturation in the original image will be over-saturated, which will cause color distortion. Therefore, when we increase the saturation, we need to consider the low, medium and high areas. For cases, \(x>0\), \(e^{x}-1>x\), and \(\underset{x\to 0}{lim}\frac{e^{x}-1}{x}=1\), when \(x\) is rather small, \(e^{x}-1\) is equivalent to \(x\). When the pixel value of the saturation component image is below 0.3, we can think of it as low saturation, which is stretched by exponential transformation; When the pixel value is in the range [0.3,0.7], we can think of it as medium saturation, which needs  
Figure 4: The flow of our method.  
Figure 3: The typical structure of Convolutional Neural Network (CNN).  
only proper adjustment; When the pixel value is greater than 0.7, we can think of it as high saturation, which needs to be reduced. The segmentation exponential enhancement algorithm is expressed specifically in following:  
\[\boldsymbol{S}^{{}^{\prime}}(m,n)=\left\{\begin{array}{ll}a\Big{(}e^{ \boldsymbol{S}(m,n)}-1\Big{)},&\boldsymbol{S}(m,n)\leq 0.3\\ e^{\boldsymbol{S}(m,n)}-1,&0.3\leq\boldsymbol{S}(m,n)\leq 0.7\\ b\Big{(}e^{\boldsymbol{S}(m,n)}-1\Big{)}&else\end{array}\right. \tag{6}\]  
where \(\boldsymbol{S}\) and \(\boldsymbol{S}^{{}^{\prime}}\) are the saturation before and after enhancement; \(a\) and \(b\) are used to adjust the transformation scale. In this experiment process we found that the best enhancement effect can be achieved when \(a\) takes 1.1-1.3 and \(b\) takes 0.5-0.7. In this paper, we decide to use the parameters in the literature [12], that is, \(a=1.3\), \(b=0.7\).
## 4 Results and Analysis
### Experimental Results  
#### 4.2.1 Experimental Setup  
To verify the effectiveness of proposed VSME method for pipeline detection, an experiment was carried out using the experimental equipment shown in Figure 7 The experimental device consists of cross array with five magnetic sensor elements, a data acquisition card, a GPS positioning system, and a data acquisition system. Sampling frequency of the experimental instrument is 1 kHz, and each sensor is three-axis differential output. Testing equipment can locate these lines and accurately measure the size and specific location of all defects.  
Figure 6: Error analysis under different SNR. (**a**) The values of RMSE; (**b**) the values of 2-Norm.  
Figure 7: \(Cont.\)  
Array element adopts the triaxial sensor of Tunnel Magnetoresistance (TMR) produced by MultiDimension Technology, and the model is TMR2905. To prevent the acquisition system from interfering with the array, we use digital signal communication to transmit signals by two Universal Serial Bus cables. Data acquisition system is responsible for receiving signals for display, storage, and related control functions. Global Positioning System (GPS) is integrated into the experimental instrument for precise positioning and trajectory tracking. Its P3 mobile terminal and I70 carry out differential conversion of satellite signals through digital radio communication with accuracy up to centimeter level, which uses the GPS Fix Data in the current international NMEA0183 standard format for conversion and preservation. GPS data can be directly converted into Keyhole Markup Language format file through Google map API, which can directly display the actual map, and can mark the location of pipe body damage abnormally.  
The laboratory experimental pipeline is made of Q235 steel material with a wall thickness of 3 mm, and a diameter of 75 mm. We fabricated six defects at different locations of the pipe to study the magnetic anomaly signals of different defects, as shown in Figure 8.  
The shapes of these six defects are axial crack, transverse crack, square groove, through hole, blind hole, and 45-degree crack in turn, as shown in Figure 9. There were four different lift-off distance detection experiments, which are 0.5 m, 1.0 m, 1.5 m and 2.0 m, respectively.  
Figure 8: Locations of defects in the laboratory pipeline.  
Figure 7: Laboratory experiment of pipeline inspection. (**a**) Picture of experimental setup, 1—Global positioning system, 2—Data Acquisition System, 3—Acquisition Card, 4—Magnetic Sensor; (**b**) Laboratory scheme design; (**c**) Field test; (**d**) Defect location.
## 3 Experimental Results and Analyses
### Experiment in Waters with Complex Seabed Topography
#### 3.2.2 Experiment in Constant-Depth and Fixed-Point Modes
Through the combination of constant depth direction and constant depth fixed point, this set of experiments fully validates the survey line planning principle's strong applicability in complex terrain sea areas. Two main challenges were faced in the experiment where the AUV's depth and point were controlled. Firstly, the reliability of empirical depth data and safety considerations led to the determination of submersion depth without accounting for the side-scan sonar and DVL ranges. Thus, there was a lack of data in certain deeper areas, as illustrated in Figure 24. Second, after submersion, the performance of seabed tracking beyond the DVL range was suboptimal. To complete the experiments, the DVL alarm information was overlooked, reducing the precision of position information. As shown in Figure 25, regions A, C, and E in the left strip image exhibit similarities and share features with regions B, D, and F. This observation indicates that seabed information from the same location appeared twice in the left and right strips, albeit with varying position information. To address these challenges, it is crucial to provide accurate empirical depth data and improve the planning of submersion depths. Additionally, removing the restrictions of maintaining a constant depth for each survey line is essential to mitigate the risks imposed by the shallowest point along a single survey line. Moreover, it is recommended to adjust the depth of individual survey lines in stages, considering the specific seabed topography. This approach forms the foundation for a robust scanning design along each survey line. By addressing the limitations of the side-scan sonar range, the proposed framework can also rectify inaccuracies in position information. Based on the comprehensive analysis of the final results data from the experiment, it is evident that the survey line planning method incorporated in the technical design of AUV-mounted side-scan sonar operation is not only feasible but also highly effective. In addition to assessing and analyzing the selection of movement modes, the experiment also carefully compared and evaluated several key factors including the survey line direction, survey line spacing, survey line coverage overlap degree, and the deployment principles of extension lines and auxiliary lines in the survey line planning method. The ex  
Figure 24: The expansion of the side-scan sonar’s measuring range. periment's findings strongly support the practicality and effectiveness of the survey line planning method content present in the technical design scheme proposed in this chapter.
# Automated Vulnerability Detection in Source Code Using Minimum Intermediate Representation Learning
## 3 Preliminary
### Hypothesis  
Programming language can be regarded as a language of communication between human beings and computers. It has many similarities with natural language. For example, they are both composed of tokens and can be resolved into a syntax tree. Success in the field of natural language processing enlightens us to borrow concepts from natural language processing (NLP) for vulnerability detection.  
In natural language processing, a sentence is represented as a sequence of the token. The context of a word is its preceding and succeeding words. Therefore, distributed representations are based on an assumption: Words that occur in the same context tend to have similar meanings [36].  
In vulnerability detection, code can be converted into a sequence by some rules, such as dependency or sequence of program execution. After the transformation, it has the same form as a natural language. Therefore, we make assumptions for vulnerability detection:  
**Hypothesis 1**.: _In a programming language, the context of a token is its preceding and succeeding tokens, and tokens that occur in the same context tend to have similar semantics._  
**Hypothesis 2**.: _The same types of vulnerabilities have common semantic characteristics. These characteristics can be learned from the context of vulnerabilities._
# A Multi-Robot Formation Platform based on an Indoor Global Positioning System
## 1 Introduction
What is more, effective formation and coverage control of mobile robots also require a reliable and powerful wireless communication infrastructure for exchanging information among themselves [27]. Since the high-performance wireless local area network (WLAN) technology is relatively low cost, its use for wireless control of multi-robot systems has become a practical proposition [28]. In this paper, the formation of wheeled mobile robots is selected as the research object, and we propose a general and low-cost multi-robot formation experimental platform to facilitate the application validation of theories and methods on the multi-robot formation research. Our multi-robot formation platform contains three key parts--the indoor global-positioning system, the multi-robot communication system, and wheeled mobile robot hardware. The real-time and precise pose of every robot is achieved by the indoor global-positioning system, where the position is obtained by the Marvelmind Indoor Navigation System based on the ultrasonic system and the orientation is obtained by MPU-6050. The mobile robots are made by us, based on an embedded microcontroller STM32 and stepper motors. In addition, a wireless communication network is established for exchanging information among robots based on the ESP8266 Wi-Fi communication module. The control is distributed in the sense that each robot makes, by itself, the decision of when to transmit its state, and the control law is computed locally. Finally, we validate the platform using a formation control leader-follower strategy and complete a series of experiments of formation forming, switching, and maintaining with the external disturbance. The rest of the paper is organized as follows. Section 2 introduces the indoor multi-robot formation platform. Section 3 explains the leader-follower formation control and setup of the experiment platform. Experimental results will be presented and discussed in Section 4. In Section 5, the main contributions of the paper are summarized, and future research directions are highlighted.
# Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments +
## 4 Experiments
### Experiments in Local Mode  
In these experiments, the mobile node was configured around the Gas and EP nodes, including sensors for gas concentrations and environmental parameters, providing measurements for concentration of O\({}_{2}\), CO\({}_{2}\), VOC as well as temperature, humidity, dust, luminance, and noise. GPS data were obtained from the Receiver node.  
The embedded Gas and EP nodes worked acquiring and processing the respective data and sent them to the embarked Receiver node. Once all sensors had provided data, a full set was completed adding localization via the Receiver node's GPS. The internal database was updated and synchronized with the external database at the server.  
The experiments consisted in deploying the test vehicle along several routes in the city of Malaga. Figure 12 shows a screenshot of the HMI at the end of one of the routes in the city center. The followed path has been signaled with a red line. Numeric labels have been added to mark the locations where an enhanced frame was completed according to the local working mode. This particular experiment had a length of 5 km and took 47 minutes to be completed.  
Table 1 presents the gathered data for the route showed in Figure 12. The item number matches with the labels in the Figure mentioned before. In the local mode, an enhanced frame is constructed once data from all present nodes are acquired, annexing GPS data provided by the Receiver node. Thus, every row in this Table displays the data gathered up to that location. This strategy produces as many frames as the slowest installed sensor. In this particular experiment, the limits were imposed by the dynamics of the CO\({}_{2}\) sensor. This can be noticed in Figure 12, where locations for frames 1 to 4 are closer than for frames 4 to 7, because the test vehicle could move at a slower velocity in the former, due to traffic jams. Preliminary results from this working mode were described in [58].  
Figure 11: Vehicle used in experiments showing the top case with the UIS Mobile node installed.
## 3 Results
### Study Characteristics  
The research details of all included articles are summarized in Table 2. Most studies applied artificial intelligence methods based on deep learning techniques, such as CNNs [30; 32; 34; 39; 50; 52; 55; 56], fully convolutional networks (FCNs) [35; 61], and network structures based on CNNs. For example, U-Net [28; 29; 33; 36; 43; 44; 45; 46; 47; 49; 52; 55] and V-Net [37; 38; 39; 40; 42; 48; 50; 53; 54; 58; 59; 61] are applied to segment two-dimensional and three-dimensional images, respectively. Some studies adopt multi-stage or multi-task strategies based on CNNs to divide the tooth segmentation problem into several sub-problems [34; 36; 48; 50; 52; 54; 58]. Other approaches used priori knowledge or morphological information to determine the center, shape, or border of teeth [48; 49; 50; 52; 55; 58; 59]. Still, some researchers applied the attention mechanism to capture the dependencies and contextual information among the teeth [32; 48]. Moreover, some other models integrate machine learning techniques, such as LSM [28; 35; 49], conditional random field (CRF) [55], region proposal network (RPN) [29; 52], and feature pyramid network (FPN) [29; 51; 52] to extract and utilize feature information from CBCT images for tooth detection, segmentation, and identification.  
Figure 3: Percentage for risk of bias and application concerns of the selected articles under QUADAS-2.  
The number of CBCT images included in the studies ranged from 5 to 4938 [36; 38]. Two of these studies used other data besides CBCT to improve the performance of the tooth segmentation models. One study used Micro-CT data of the teeth to enhance the outcomes of the U-Net model for tooth and pulp cavity segmentation in CBCT images [29], while another study used intra-oral scanning data to replace the surface morphology of the crowns in CBCT images and generate high-resolution tooth models [41]. Besides tooth segmentation, some studies have also explored the segmentation of the pulp cavity, jaw-bone, or small edentulous areas [29; 37; 39]. In this paper, we only consider the applications of tooth segmentation from CBCT images. To train and evaluate deep learning models, the location and extent of teeth need to be extracted and labeled from the patient's maxillofacial 3D images which are obtained directly in the clinic. Manual annotation can provide accurate ground truth (GT) but often requires significant time and effort. Therefore, it is common to augment the dataset by cropping, rotating, and mirroring after the data has been normalized. Two of these studies have created publicly available datasets for use by other researchers [45; 46]. The homogenized CBCT data were divided into training and test sets for AI model training and testing, and the remaining data were used for measuring the performance of tooth segmentation methods.