esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Track-Side Real-Time Monitoring for Track Slab Deformation  
We established a real-time monitoring system that facilitates track slab deformation detection, as illustrated in Figure 2. The proposed track-side monitoring system consists of eight fiber optic accelerometers, an interrogation based on the phase generation carrier demodulation technique, an optical cable, and a desktop computer. The selected sensors are interferometric fiber optic accelerometers [56] with a suitable bandwidth (0.1-1000 Hz) and high sensitivity (500 rad/g). The sampling frequency of the proposed system is 1000 Hz. The monitoring system was installed in a CRTS II HSR on viaduct in China. The red box in Figure 2 is the cross section of the CRTS II ballastless track structure on viaduct, and the components from top to bottom are the rail, track slab, CA mortar layer, supporting concrete layer, and viaduct. The blue box in the cross section is the cable trough, which is the transversely nearest place to the deformation-prone area in the safety-securing zone allowed for sensor placement. The field image of the cable trough where the sensors were installed is shown in the blue box in Figure 2. The eight sensors in Figure 2 are illustrated by eight blue circles with a spacing of 3.2 m, which is approximately half the length of the track slab (6.45 m). The red lines between the sensors represent the transmission optical cable. The sensor is connected to the demodulator via the optical cable, and the data acquisition and data demodulation are performed by a computer continuously. The deformation of the track slab occurred near a certain sensor in the monitoring section during the monitoring time, so the proposed real-time monitoring system captures the vibration data of the normal and deformed states of track slab. Thus, a solid foundation for the subsequent intelligent identification is laid.  
Figure 1: Flowchart of the deformation identification of a track slab.
## 3 Dielectric-Based Passivation Layers
### Contacting Approach  
In this sub-section, several contacting approach techniques which are mentioned in Table 1 will be discussed for the Al\({}_{2}\)O\({}_{3}\) passivation layers. Due to the short diffusion length of CIGS carriers, the dimensions of the openings and the distance between them should be in the sub-micron range [9].  
In the following studies [12; 13; 14; 21; 25], the contact openings were realized by chemical bath deposition (CBD) of CdS. The size, shape, and density of these particles are determined by the CBD parameters. The alumina layer is deposited on top of the particles. The CdS particles are then removed, leaving holes behind in the alumina layer. As the characteristics and morphologies of the particles are different from each other for the different studies, the outcomes of these studies are also different in terms of the effects on and/or enhancements of the electrical characteristics of the solar cells (Table 1).  
Mo NPs generated by highly ionized pulsed plasma (HIPP) were also used as contacts in the Al\({}_{2}\)O\({}_{3}\) passivation layer. Here, the alumina was deposited by DC sputtering. Because of the one-directional growth of the alumina layer, the Mo nanoparticles were not completely covered with alumina, leaving space for contacting. Although there is a minor improvement in power conversion efficiency of the ultra-thin CIGS solar cells, this approach is still open to optimization [15] (Table 1).  
To generate the contact openings in the Al\({}_{2}\)O\({}_{3}\) passivation layer, advanced techniques such as e-beam, nano-imprint, nano-sphere, or photolithography were used in the following studies [5; 9; 11; 16; 17; 22; 23]. Lithography is one of the most advanced techniques that is used for this purpose. It has advantages like creating the holes in a controlled shape, size, and density. However, it is an expensive technique for using it as an industrially viable technology. Using passivation layers with advanced opening techniques, i.e., lithography, results in improvements in electrical characteristics of both ultrathin and conventional CIGS solar cells (Table 1). In [11], different from the other studies, MgF\({}_{2}\) (100 nm) was used as a passivation layer together with Al\({}_{2}\)O\({}_{3}\) and resulted in significant optical improvement due to enhanced reflectivity at the back contact.  
Using dielectric nanoparticles between back contact and absorber layer is another approach for the rear surface passivation of CIGS solar cells. In [19] and [20], the dielectric patterns were applied by the technique described in [19] to passivate the back surface but at the same time allow current to pass between the rear contact and the absorber layers. Hence, the geometrical requirements to have a good electrical contact were satisfied in these studies [12].  
Ultimately, one of the different techniques explained above can be a suitable strategy to use for contacting. It is only necessary to decide the length and the cost of the experiment before choosing the strategy.
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 4 Methodology
### Indirect Jump Instrumentation and Monitoring  
With the seed tests generated above, we execute the target program and monitor it at runtime to discover indirect jumps relations. First, in order to record indirect jump relations, the target program is instrumented at each indirect jump instruction and the entry of each function. We define a global value _Is_indirect_jump_ to represent whether a function call is an indirect jump. At each indirect jump instruction, when the instruction is executed, _Is_indirect_jump_ is assigned to _True_ and the basic block that the indirect jump instruction belongs to is recorded as the source of the indirect jump, represented as _bsrc_. At the entry of a function, we check the value of _Is_indirect_jump_. If it is _True_, the first basic block of this function is recorded as the target of the indirect jump, represented as _bdst_. A indirect jump relation is represented as a pair \((b_{src},b_{dst})\). Then, we provide all seed test cases generated in the previous stage (Section 4.2) to the instrumented program and resolve as many indirect jump relations as possible.  
It is worth mentioning that we perform two different instrumentations for the original target program. One is to calculate the seed distance, while the other is to record the indirect jump relations. Theoretically, both of them can be performed in the process of fuzzing. However, inserting too many instructions in fuzzing may cause a lot of runtime overhead. Therefore, our approach separates the two instrumentations to minimize the overhead.
# A Personal Microcomputer as an Access Control Management Platform in Road Transport
## 6 Comparative Analysis, Recognition Problems and Future Development  
### Comparative Analysis of the Similar Devices Available on the Market  
The idea of using OCR technology in road transport is not new. Devices specialised in licence plate recognition (LPR) have been on the market for a long time and are becoming more popular each year. Most modern systems are much more powerful and accurate than the solution presented in this paper. Many of them can detect multiple objects at once and read the licence plates of vehicles moving at speeds up to 200 km/h. Examples of such systems are the LPR/ANPR cameras for vehicle access control delivered by the Supervision company, solutions from Neural Labs (e.g., Neural Edge Urban), or the products and services from the company Milesight. Many models of surveillance cameras developed by various companies feature LPR.  
The problem with the solutions mentioned above is their high overall price. The cost of a single LPR camera is usually around USD 500 and rises depending on the model's capabilities, accuracy, and overall quality. For example, the Amcrest LPR camera available on Amazon costs above USD 700. With more powerful and complex solutions, the licence could cost even more (e.g., a licence for the Neural Edge Urban solution for one camera costs EUR 1029, and the camera itself is not included). Some companies do not even show their prices because they take every transaction individually and calculate the cost.  
Compared to the devices mentioned above, the system presented in this paper is much cheaper. As previously mentioned, its overall cost is less than USD 200 and could be even lower, while the price of the single LPR camera available on the market is usually more than twice that (without the cost of the computer). The system presented in this paper is certainly not as powerful as the devices mentioned above. Still, it fulfils its purpose and proves that it is possible to maintain a simple access control platform on a microcomputer.
## 3 Results and Discussion
### Acoustic-Assisted CCS Experiments: \(\sigma_{diff}\) = Fixed  
Experimental series C covers a total of seven CCS experiments where \(\sigma_{\rm diff}\) (i.e., \(\sigma_{1}-\sigma_{2}\)) was kept constant at 45 MPa (calcischist, marble, limestone, quartzite, and quartzschist) or at 10 MPa (silt- and mudstone). Table 4 presents the physical properties of the various core samples used. Figure 7 shows the P-wave velocity as a function of increasing \(\sigma_{1}\) where \(\sigma_{2}\) is 45 MPa less than \(\sigma_{1}\) (and 10 MPa less for the silt- and mudstone). The S-wave velocity, as a function of increasing \(\sigma_{1}\), is shown in Figure S5 (see Supplementary Materials).  
Figure 6: P-wave velocity as a function of axial stress, and thus depth below the surface, for all eight CCS tests conducted within experimental series B. After the samples were loaded until \(\sigma_{1}=\sigma_{2}=\) 55–60 MPa (dashed lines), unloading took place during which several acoustic measurements were taken to check for potential hysteresis effects (circular data points). Axial stress–depth relationship is taken from [35]. The graph in the inset presents the P- (dashed lines) and S-wave (continuous lines) velocities for the marble (black), limestone (purple), and calcchsist (blue) samples used. \(\rm{Vp/Vs}\) ratios at respectively 10 and 60 MPa of 1.65 and 1.69 (timestone), 1.72 and 1.82 (marble), and 1.62 and 1.77 (calcischist) were found.  
Figure 7: P-wave velocity as a function of axial stress, and thus depth below the surface, for all seven CCS tests conducted within experimental series C. After the samples were loaded until \(\sigma_{1}=\) 65 or 95 MPa (dashed lines), unloading took place during which several acoustic measurements were taken to check for potential hysteresis effects (circular data points). Axial stress–depth relationship is taken from [35].  
The laboratory results presented in Figure 7 demonstrate less variation in the P-wave velocity as a function of increasing \(\sigma_{1}\) compared to experimental series A (Figure 5) and B (Figure 6). The latter is due to the fact that here the initial measurement was taken at \(\sigma_{1}=46\) MPa and \(\sigma_{2}=1\) MPa (except for the silt- and mudstone, as previously mentioned). Due to aforementioned initial conditions, any potential closure of microcracks already occurred prior to reaching \(\sigma_{1}=46\) MPa. The data in Figure 7 appear to extend the trends shown in Figure 6, except for the quartzite sample. The reason for this might be the fact that another sample, drilled from the same block, was used here compared to experimental series B (Tables 3 and 4).  
The same division, i.e., soft and hard materials (discussed in Section 3.3), can be made here as well. The soft silt- and mudstone separate themselves from the other formations by revealing significantly lower seismic velocities. Considering the measurements done during the unloading stage (at the end of an experiment), one may conclude again that the loading process did not permanently alter the internal structure of the rocks that controls its velocity.
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
## 4 Analytical Characterization of the Sidelink Operations
### Sidelink Operations  
The detailed description in terms of physical and MAC layers and other parameters can be found in the previous Section 3. Here, we characterize the behavior of the underlying SB-SPS scheme of LTE-V2X Mode 4 and NR-V2X Mode 2. The fundamental objective of the SB-SPS algorithm is to identify a candidate group of sub-channels for the transmission of V2X. The resources are selected using channel sensing and then reserved for later use. Mode 2 allows for the possibility to dynamically re-select resources for retransmission. The general procedure of the SB-SPS system is illustrated in Figure 2.  
The steps that each vehicle takes to find possible resources for transmission using SB-SPS are listed below.  
* Channel sensing: the sidelink received signal strength indication (SL-RSSI) for each sub-channel is used to calculate the interference level for each sub-frame for each vehicle. This sensor information is obtained over a specific period (sensing time \(T_{s}\)).
* Selection of potential resources: every vehicle organizes its available resources (\(N_{A}\)) according to sensing measures, and the resources are chosen by the following rules: (i) the SL-RSSI of the selected resource must be lower than a predetermined threshold \(P_{th}\) agreed by the vehicle); (ii) if the resource is not detected during \(T_{s}\), it must not be chosen; (iii) out of all the resources accessible, no resources may be chosen that are already reserved; (iv) a vehicle cannot sense the channel when transmitting following a half-duplex transmission; and (v) the threshold \(P_{th}\) is raised by 3 dB and the two-step exclusion process is repeated if the remaining candidate resources \(N_{C}\) represent less than 20% of the total amount of resources \(N_{A}\).
* Resource selection: the vehicle randomly chooses transmission resources from those it has identified as the best 20%. For an arbitrary number of successive transmissions with the same transmission interval, the vehicle may reserve the same resources. The SPS implementation of LTE-V2X and NR-V2X specifies the minimal and maximal number of successive transmissions before reallocation.
* Resources re-selection: each time a packet is transmitted, the SB-SPS counter, which shows how many more consecutive packets are left, is decreased. The vehicle can either keep the resources with a probability of \(p_{k}\) or re-select the resources with a probability of \(1-p_{k}\) when the counter reaches zero.
## 4 Example Verification
### Experimental Data Annotation and Enhancement  
Data annotation involved structured labeling of the collected data. Initially, labels corresponding to the dataset were defined--\"helmet\", \"head\", and \"vest\"--to identify unsafe behaviors associated with the failure to wear safety protective equipment. Additionally, the label \"person\" was defined for the personnel dataset to identify unsafe behaviors related to unauthorized entry into hazardous construction zones. Annotations were implemented using the LabelImg tool in Pascal VOC format (XML files containing bounding box coordinates).  
To enhance model generalizability and small-target detection performance for hydraulic engineering applications, mosaic data augmentation was used. The mosaic augmentation process is initiated by randomly selecting a base image and partitioning it into four non-overlapping quadrants (sub-regions). Three quadrants are stochastically sampled and spatially recomposed into a synthetic mosaic image, which replaces a corresponding section of the original input. This geometric transformation induces discontinuous contextual variations, compelling the model to adaptively process targets within fragmented spatial distributions. For detection tasks, bounding box coordinates within the mosaic are proportionally recalibrated using affine transformations to maintain annotation fidelity. Specifically, target positions are remapped according to their relative spatial offsets in the composite image, ensuring consistent geometric correspondence between the augmented data and ground truth annotations.  
The adoption of mosaic augmentation is strategically motivated by its unique capacity to enhance small-object detection robustness in complex construction environments. By compositing four training images into a single contextual mosaic, this technique inherently addresses two critical challenges: (1) It simulates real-world occlusions and scale variations through naturalistic object arrangements, where safety gear fragments may appear adjacent to machinery or obscured by structural elements, and (2) it amplifies contextual diversity beyond conventional single-image augmentations, like rotation and color jittering. The approach particularly benefits construction site monitoring, where camera perspectives and worker densities vary dynamically, creating natural analogs to mosaic-generated variations. Compared to alternative multi-image augmentations, like MixUp or CutMix, mosaic augmentation better preserves original object aspect ratios and lighting conditions  
Figure 5: Training set data.  
critical for maintaining detection fidelity under the harsh illumination variances typical of outdoor construction sites.
## 3 Application Domain
### Driving Data Collection  
Figure 12 shows our settings for the CCD system. We selected a U-shaped route with light traffic and good weather conditions, with average driving patterns for general roads. We selected a straight highway route, with 10% light and 80% heavy traffic. The details of data collection are explained below. We first connected the Tobii eye tracker to the computer, then used Open Broadcaster Software (OBS) 29.0.2 to record the coordinate information of the eye tracker through the computer screen, and finally turned on the CCD system to determine whether the eye tracker was correctly displayed on the screen. When all of the above equipment and subjects are ready, we further confirm whether the data collection of the eye tracker and hand and foot pressure are synchronized. Finally, data collection was officially carried out. The entire process was screen-recorded, and the time was recorded until the simulation ended.  
The following explains how to synchronize the data collected through eye, hand, and foot sensors. We note that the data collection of hands and feet was performed through Arduino. These two data were synchronized. The main problem is synchronizing eye movement and hand and foot data. The current approach was to find the screen recording data based on when the hand and foot pressure values were obtained. When the image data were found, the HoughCircles function provided by the open-source computer vision library was used to find the coordinate values displayed by the eye tracker, which completed the integration of specific eye, hand, and foot data at a particular time.  
To maintain the consistency of data collection, this study invited the same subjects to collect data ten times in the same driving environment. Each time, the driver drove on the entire route. The data on eye movements, braking, finger pressure, and accelerator were collected at intervals of 0.5 s. The subject was asked to drive the same route every time, but the traffic conditions varied (due to different traffic lights, pedestrians, and traffic volume). It took about three to five minutes from the beginning to the end for a drive test. In terms of driver behavior, this study assumed two types of driving: everyday driving and distracted driving. The former means the driver is relatively focused, while the latter means the driver has wandering eyes and changes lanes at will.  
Figure 11: Evolutionary learning process at the level of control neurons. (**a**) Cytoskeletal neurons controlled by each reference neuron are activated sequentially to evaluate their performance. (**b**) Assume the cytoskeletal neurons controlled by R2 achieve better performance. The pattern of neural activities controlled by R2 is copied to R1. (**c**) R1 controls a slight variation of the neural grouping controlled by R2, assuming some errors occur during the copy process.
## 3 Results and Discussion
### Measurement
\begin{tabular}{l l l}   \(z\) & system heat loss coefficient & (-), \\   \(\theta_{cold}\) & intake water temperature & (\({}^{\circ}\)C), \\   \(\theta_{hot}\) & hot water temperature & (\({}^{\circ}\)C), \\   \(\rho\) & density & (kg\(\cdot\)m\({}^{-3}\)), \\   \(c_{p}\) & specific heat capacity & (J kg\({}^{-1}\) K\({}^{-1}\)). \\ \end{tabular}   
The following example is very simplified. However, it can show some of the possibilities of using the waste heat. We want to know the average volume flow during four days of these conditions: intake water temperature is about 15 \({}^{\circ}\)C, outtake temperature is about 40 \({}^{\circ}\)C and the heat loss coefficient of the ideal system is 0. \[\dot{V}_{\rm DHW}=\frac{Q_{\rm DHW}\cdot 3600}{(1+z)\rho c_{p}(\theta_{hot}- \theta_{cold})}=0.084\ \rm m^{3}\ d^{-1}. \tag{4}\]  
The results show that the waste heat could be used for preheating DHW from 15 \({}^{\circ}\)C up to 40 \({}^{\circ}\)C with volume flow about 84 L per day. The value corresponds to the amount of DHW consumed by two persons in the ordinary household per day. Another possibility is using the waste heat for heating of a swimming pool. In case of the standard swimming pool with volume of about 10 m\({}^{3}\), the theoretical maximum temperature difference is about 0.7 \({}^{\circ}\)C. This value is quite small, but it is better from the point of view of the almost constant temperature and flow.