esa-sceva/satcom-chunk-collection huggingface dataset:
_Appl. Sci._**2013**, [PERSON]3\), 214-250; doi:10.3390/app3010214  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**Ultra-Intense, High Spatio-Temporal Quality Petawatt-Class Laser System and Applications**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{2}\)**  
* [1]Kansai Photon Science Institute, Japan Atomic Energy Agency, 8-1-7 Umemidai, Kizugawa, Kyoto 619-0215, Japan; E-Mails: [EMAIL_ADDRESS] (T.S.); [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (Y.N.); [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (S.K.); [EMAIL_ADDRESS] (S.K.); [EMAIL_ADDRESS] (A.S.P.); [EMAIL_ADDRESS] (T.Z.E.); [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (K.O.); [EMAIL_ADDRESS] (H.K.); [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (I.D.); [EMAIL_ADDRESS] (H.O.); [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (Y.F.); [EMAIL_ADDRESS] (M.N.); [EMAIL_ADDRESS] (M.K.); [EMAIL_ADDRESS] (S.V.B.); [EMAIL_ADDRESS] (K.N.); [EMAIL_ADDRESS] (M.Y.); [EMAIL_ADDRESS] (K.K.); [EMAIL_ADDRESS] (A.S.); [EMAIL_ADDRESS] (P.R.B.)
* [2]Development Bureau, Hamamatsu Photonics K. K., 5000 Hirakuchi, Hamakitaku, Hamamatsu-city, Shizuoka 434-8601, Japan; E-Mails: [EMAIL_ADDRESS] (S.M.); [EMAIL_ADDRESS] (H.K.)
* [3]Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +81-774-71-3329; Fax: +81-774-71-3316.  
_Received: 10 December 2012; in revised form: 15 February 2013 / Accepted: 19 February 2013 / Published: 7 March 2013_  
This paper reviews techniques for improving the temporal contrast and spatial beam quality in an ultra-intense laser system that is based on chirped-pulse amplification (CPA). We describe the design, performance, and characterization of our laser system, which has the potential for achieving a peak power of 600 TW. We also describe applications of the laser system in the relativistically dominant regime of laser-matter interactions and discuss a compact, high efficiency diode-pumped laser system.  
keywords: ultra-intense laser; chirped-pulse amplification; Ti:sapphire laser; high field science
# Influence of Non-Uniform Rail Loads on the Rotation of Railway Sleepers
## 2 Movement Equations and Their Solutions
### Solution for Track Response as a Two-Layer Structure  
The method of solving the track response as a two-layer structure, with an uneven rail load and the assumption of sleeper rotation as stiffblocks, is as follows:  
1. For the given functions of rails loads \(\mathrm{q}_{1}\) and \(\mathrm{q}_{2}\), determine the symmetric \(\mathrm{q}_{av}\) and antisymmetric \(\Delta\mathrm{q}\) components of the load according to Formula (1).
2. Isolate from every form of the load a time-invariable component and a time-variable component, i.e., \[\begin{split}& q_{av}=q_{av}^{n}+q_{av}^{o}\\ &\Delta q=\Delta q^{n}+\Delta q^{o}\end{split}\] (26)
3. Using symbol \(\mathrm{y}_{r1}\) for the rail displacement for which the quasi-static load (time-invariable) is greater than for the other rail, which will be designated as \(\mathrm{y}_{r2}\), respectively, the sleeper displacement under both rails, \(\mathrm{y}_{p1}\) and \(\mathrm{y}_{p2}\), determine them, using Formulas (8)-(15): \[\begin{split}& y_{r1}(q_{av}^{n})=y_{ra}(q_{av}^{n})+y_{rA}( \Delta q^{n});\\ & y_{r2}(q_{av}^{n})=y_{ra}(q_{av}^{n})-y_{rA}(\Delta q^{n});\\ & y_{p1}(q_{av}^{n})=y_{pa}(q_{av}^{n})+y_{pA}(\Delta q^{n});\\ & y_{p2}(q_{av}^{n})=y_{pa}(q_{av}^{n})-y_{pA}(\Delta q^{n})\end{split}\] (27)
4. Using Formulas (17)-(24), determine the rail displacement and sleeper displacement for the time-variable load \(\mathrm{q}^{o}\) (oscillating with a given frequency \(\omega\)):\[y_{r1}(q^{o}_{av}) =y_{ra}(q^{o}_{av})+y_{r\Lambda}(\Delta q^{o}); \tag{28}\] \[y_{r2}(q^{o}_{av}) =y_{ra}\Big{(}q^{o}_{av})-y_{r\Lambda}(\Delta q^{o});\] \[y_{p1}(q^{o}_{av}) =y_{pa}(q^{o}_{av})+y_{p\Delta}(\Delta q^{o});\] \[y_{p2}(q^{o}_{av}) =y_{pa}(q^{o}_{av})-y_{p\Delta}(\Delta q^{o})\]
5. If a variable load is polyharmonic (more than one frequency \(\omega\)), the calculations expressed with Formula (27) should be repeated as many times for as many harmonic components of time-variable force there are.
6. The total rail displacements and total sleeper displacements are a sum of these displacements resulting from the constant load (Formula (27)) and variable load (Formula (26)), whereas--in the case of a polyharmonic load, where there are m harmonic components--Formula (28) should be applied m times, and the results should be added.
## 4 Invariant Multi-Mode Vectors Pushover Analysis
### Multi-Mode Pushover for Deformation Demand Estimates of Steel Moment-Resisting Frames  
The procedure proposed by [55] is a method for estimating the deformation demands of steel moment-resisting frames during seismic events [56]. The multi-mode pushover procedure accurately predicts seismic demands of steel moment-resisting frames by calculating the target interstory drift and using modal combinations. It considers frequency content, higher mode effects, and the interaction between modes. It offers superior response predictions with minimal computational effort. This approach allows for more accurate prediction of the deformation demands of the structure. The multi-mode pushover procedure involves the following steps:1. Modal analysis: Perform a modal analysis to determine the natural frequencies, mode shapes, and modal participation factors of the structure. This can be done using a general-purpose nonlinear static analysis tool.
2. Response spectrum: Obtain the response spectrum for the design earthquake. This spectrum represents the maximum response of the structure at different periods.
3. Mode combination: Combine the modal responses to obtain the total response of the structure. The modal responses are combined using a modal combination rule, such as the complete quadratic combination (CQC) rule or the square root of the sum of squares (SRSS) rule.
4. Target interstory drift: Estimate the target interstory drift, which is the maximum interstory drift that the structure is expected to experience during the earthquake. This can be done using one of three procedures: the modal pushover procedure (MPP), the adaptive capacity spectrum method (ACSM), or the multi-mode pushover procedure (MP3).
5. Generalized force vectors: Calculate a set of generalized force vectors using the modal analysis results. These force vectors represent the instantaneous force distribution acting on the structure when the interstory drift at one story reaches its maximum value during dynamic response to the seismic excitation.
6. Maximum response: determine the maximum value of each response parameter by taking the envelope of the results obtained from the generalized force vectors.
## 2 Materials and Methods
### Selected Landside Causal Factors
From the climatic point of view, an analysis of extreme precipitation on a regional scale was performed, with daily maximum precipitation data obtained through the \"Regional Weather-Idro-Pluviometric Information System\" database. 131 rain gauges in the most recent standard period 1991-2020 were taken into account; the data were then processed using the GEV (Generalized Extreme Values) technique and subsequently interpolated with GIS software. This led to the creation of a raster over the entire study area that allowed a detailed analysis of the influence of precipitation on landslide susceptibility. There were eight classes obtained in this case:  
* 100-115,
* 115-130,
* 130-145,
* 145-160,
* 160-175,
* 175-190,* 190-205, and
* 205-250. The NDVI is a vegetation index obtained from satellite images to assess the presence of photosynthetic activity. The index is calculated from satellite images produced by sensors that acquire in the red (R: 0.7 \(\upmu\)m) and near-infrared (NIR: 0.9 \(\upmu\)m). It assesses the presence of photosynthetic activity, as it relates the red spectrum, where there is absorption by chlorophyll, and the near-infrared spectrum where leaves reflect light to avoid overheating. The NDVI value ranges from \(-1\) to 1, and the presence of vegetation takes values above 0.2 [45], and the formula for calculating the index is as follows:  
\[NDVI=\frac{NIR-R}{NIR+R}\]  
The classes created according to the NDVI value were divided into:  
* no-vegetation (\(-1\) to 0.199);
* low vegetation (0.2 to 0.5);
* high vegetation (0.501 to 1.0). Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6 data downloaded from the NASA website were generated every 16 days with a spatial resolution of 250 meters (m). The MYD13Q1 product provides the NDVI, indicated as a continuity index to the current NDVI derived from the National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR).
# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 1 Introduction  
Object detection in complex traffic scenarios is paramount for the safety and efficacy of autonomous driving systems. Accurate, high-FPS identification of diverse road users and obstacles, particularly complex-shaped or occluded targets, is crucial for robust decision-making [1, 2]. Key challenges include detecting small objects (often \(<\)32\({}^{2}\) pixels [3, 4]), handling irregularly shaped targets (e.g., pedestrians and cyclists [5]), and those with significant occlusion [6], all while meeting demanding processing speeds (typically \(>\)30 FPS [7, 8]). Addressing these interconnected issues is vital for dependable autonomous navigation [1].  
While deep learning has significantly advanced object detection, traditional methods after such complexities. Two-stage algorithms (e.g., the R-CNN family [9; 10]) achieve high accuracy but often at lower FPS, limiting their suitability for autonomous driving [1; 11]. Conversely, single-stage detectors like SSD [12] and the You Only Look Once (YOLO) series [13; 14; 15] offer improved speed by performing classification and localization concurrently, making them more appropriate for high-FPS applications. The YOLO series is particularly recognized for balancing speed and accuracy [16], with the recent YOLOv8 demonstrating enhanced accuracy and adaptability [17; 18], making it a strong baseline for challenging traffic environments [19].  
This study enhances modern detection by specifically targeting small, occluded, and irregularly shaped objects in dynamic urban settings. We propose DGSS-YOLOv8s, an advanced model built upon the efficient YOLOv8s framework. Its core novelty is the synergistic integration of optimized techniques tailored to these challenges, aiming for competitive accuracy while maintaining high processing speed.  
DGSS-YOLOv8s integrates the DCNv3_LKA_C2f module, an Optimized-GFPN structure, and the Detect_SA module, alongside an Inner-Shape IoU loss, to significantly enhance the detection of challenging targets and ensure high FPS. The main contributions are as follows. First, the DCNv3_LKA_C2f module (combining DCNv3 and LKA) improves feature representation for non-rigid objects with efficiency. Second, an Optimized-GFPN for the neck reduces latency and improves multi-scale feature fusion. Third, the Detect_SA module in detection heads captures global spatial dependencies with reduced complexity. Fourth, the Inner-Shape IoU loss, using shape-aware penalties [20] and adaptive inner bounding box overlap [21], improves regression for occluded and elongated objects.  
DGSS-YOLOv8s is comprehensively evaluated on BDD100K [22] and KITTI [23] datasets, demonstrating superior performance in complex traffic scenarios. Section 2 reviews prior work. Section 3 details the DGSS-YOLOv8s architecture. Section 4 presents comparative benchmarking, and Section 5 concludes and outlines future research.
# Cleaning of Phototrophic Biofilms in a Show Cave: The Case of Tesoro Cave, Spain
## 1 Introduction
The climate is semiarid and mesothermal, with small surplus of water in winter. The water that reaches the karst is scarce; a significant quantity is lost by evapotranspiration, and the remainder is lost in the urbanized area surrounding the cave entrance. The only water supply to Cantal karst is reduced to direct rain infiltration with a few dripping points in the cave; the response time to rain has 2-3 h of delay. This explains why the generation of speleothems is so scarce in the cave [16]. The cave measurements of CO\({}_{2}\), temperature and relative humidity showed particular features. In Sala del Lago and Sala del Volcan, the CO\({}_{2}\) concentrations were from 200 to 550 ppm, the temperature 14.8-16.8 \({}^{\circ}\)C, and the relative humidity 80% to 90%, all of which are normal values for a karstic cavity [5; 6; 7; 8; 13; 14; 15]. However, the non-visited area (Galeria de Breuil) shows anomalous concentrations of CO\({}_{2}\)[16], such as 20,000 ppm, the temperature rises to 22.7 \({}^{\circ}\)C, and the relative humidity reaches 100% saturation. These values are characteristic of environments with poor ventilation; in other words, the karstic system seems to behave, in this Galeria, as a confined and almost impermeable enclosure that presents a small renewal of air. Tesoro Cave has experienced severe anthropic alterations. On the surface, the area was urbanized with roads, parking plots and buildings. Inside the cave, strong alterations for facilitating visits are observed, including the removal of sediments, building materials extraneous to the karst, artificial lighting, the construction of an artificial lake, an elevator, and a wide staircase [16]. Surface corrosion was favored by the microenvironmental conditions in some galleries. The most noticeable effect of lighting was the growth of a dense phototrophic community of cyanobacteria, green algae and bryophytes on the speleothems, walls and ground, near the lighting points and wet areas. The remains of wood and an old electric installation are also evident. For study purposes, the cave was divided into three sectors: T1, mainly formed by a wide hall, Sala de Lagos, with an artificial lake; T2, centered by the main entrance to the cave with an elevator and staircase as well as halls and galleries at both sides to give access to zones T1 and T3; and T3, ending in a deep hall, Sala del Volcan (Figure 1). Here, we report a study on the phototrophic community in Tesoro Cave, the cleaning of biofilms and the used protocols.
## 3 Materials and Methods
### Methodology  
The research workflow is divided into three distinct phases, as illustrated in Figure 4. The initial step involved identifying the slums within the study area, which included refining the slum reference map through the utilization of remote sensing imagery (RSI), street view imagery (SVI), and additional data sources. The next phase focuses on implementation, which includes three main components: experimental model design, data pre-processing, the implementation and mapping of the design of the slum area. Finally, the third phase entails a comparative analysis of the accuracy values derived from various models, including a comparison with the results of the field survey mapping design.  
Figure 3: Example of street view imagery (SVI) captured from a slum area in Bandung City, illustrating ground-level conditions.  
The present research is constrained by the Bandung City Government's 2020 designation of slum areas and the 2024 field survey results, due to the limited legal slum area mapping database inventoried by the Bandung City Government.  
The chosen deep learning framework is a Fully Convolutional Network (FCN), selected on the basis that it is considered to be appropriate for the available dataset. The FCN framework is capable of processing images of various dimensions, with no limitation to a specific dimension. The primary function of this model is to perform segmentation and generate predictions at the pixel level. This render is highly suitable for the segmentation of urban areas based on the Sentinel imagery [19; 20]. Furthermore, the FCN model is distinguished by its comparatively modest parameter count, a feature that stands in contrast to architectures such as fully connected networks. This design choice is intended to guarantee the model's enhanced efficiency and processing speed.
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 4 Near-Crash Event Identification Model  
### Model Architecture  
The architecture of the near-crash event identification model, i.e., CNN-LSTM-Attention, is shown in Figure 4. For the spatiotemporal relationships in time-series data that previous studies have often overlooked, the model uses the CNN layer to perform convolution operations on the time series to capture features adjacent in time. This is followed by processing long-term dependencies in the sequence through the LSTM layer to capture global features. This design enables the model to better understand the spatiotemporal structure of the input data. Furthermore, we introduced an attention layer embedded within the LSTM layer. This attention mechanism is used to capture global dependencies in the time-series data, allowing the network to dynamically adjust the weights of each time step during the learning process. This design helps better capture long-term dependencies and global patterns. Through the collaborative operation of these three layers, the entire model can more quickly and accurately capture key information related to near-crash events in naturalistic driving data. We also added a Dropout layer to the model to randomly drop some neurons, reducing inter-neuron dependencies and preventing overfitting. Additionally, we used regularization techniques by adding a penalty term for the sum of the squared weight parameters in the loss function to limit the complexity of the model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Threshold Setting** \\ \hline
1 & Lateral acceleration greater than or equal to 0.7 g \\
2 & Longitudinal acceleration greater than or equal to 0.58 g \\
3 & Longitudinal deceleration less than \(-\)0.75 g \\
4 & Emergency event button triggered \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial thresholds of near-crash event screening.  
Figure 3: Process of near-crash event screening.
# A Forecast Model of the Number of Containers for Containers by Voyage
## 4 Example Analysis
### Data Samples  
To establish a model for forecasting the number of containers allocated to a container ship for one voyage, factors influencing the number of allocated containers must be analyzed and an index system for forecasting the number of allocated containers must be established. Numerous factors influence the number of containers allocated to a container ship for one voyage; such factors include the port of call, the company (fleet) to which the ship belongs, and the ship itself. A predictive index for forecasting container allocation is outlined as follows (\(i\) represents the \(i\)th influencing factor, \(i=1,2,\cdots,m\)):  
1. \(X_{1}\), local GDP of the region in which the port of call is located, which can be calculated on the basis of the formula actual amount/100 million yuan;
2. \(X_{2}\), changes in port industrial structures, which can calculated according to the percentage occupied by the tertiary industry;
3. \(X_{3}\), completeness of the collection and distribution system, which can calculated according to the actual annual throughput of containers per million twenty-foot equivalent units (TEU) at the port of call;
4. \(X_{4}\), company's capacity, which can be calculated according to the actual number of containers/10,000 TEU;
5. \(X_{5}\), inland turnaround time of containers, which can be calculated according to the actual number of days;
6. \(X_{6}\), seasonal changes in cargo volume, which can be calculated as a percentage;
7. \(X_{7}\), quantity of containers handled by the company, which can be calculated according to the actual number of containers/10,000 TEU;
8. \(X_{8}\), transport capacity for a single ship, which can be calculated according to the actual number of containers/TEU; and
9. \(X_{9}\), full-container-loading rate of the ship, which can be calculated as a percentage.  
For different shipping lines, ports, and container ships, collecting actual data pertaining to the nine aforementioned factors is difficult. Moreover, information on some of these factors is treated as confidential by company or ship management teams. To verify the practicality of the model, this study simulated a set of data. To ensure that the sample data were reasonable and approached real situations, this study sought information from the literature [8; 9], in addition to consulting the department heads of shipping lines and stowage operators.  
The selected training samples are presented in Table 1.
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 4 Experiments
### Performance on Common Datasets
#### 4.4.3 The UCSD Dataset  
The UCSD dataset [30] is taken from a stationary camera. Similar to Reference [30], we use frames 601-1400 as the training set and the rest of them as the testing set. The results in Table 4 show that FDCNet gets better results than other methods in MSE. However, the result of MAE is a little worse than CSRNet [11], reflecting that our network does not do very well in the UCSD dataset. Seen from Figure 3, this dataset is a sparse dataset, which may get relatively lager errors when generating density maps due to the large intervals between heads. We therefore consider the need to experiment more on sparse datasets for better generalization.
## 4 Contribution
### Construction of Low-Altitude SULA Model of Urban Environment
#### 4.2.2 Design of Doppler Frequency Shift Compensation Algorithm  
The influence of Doppler shift on the performance of UAV communication channel cannot be ignored. It causes a large number of continuous bits to be destroyed, data packet loss, and signal distortion problems. Assuming that the signal is only affected by the multipath effect and there is no Doppler effect, the received signal is:  
\[s(t)=H(\omega)e^{i\omega t} \tag{64}\]  
where \(H(\omega)\) is:  
\[H(\omega)=\sum_{u=1}^{U}a_{u}e^{-i\omega\tau_{u}} \tag{65}\]  
When the multipath effect and Doppler effect exist at the same time, suppose the Doppler angle frequency shift of the \(u\)th ray is \(\omega_{u}=2\pi f_{u}\), and \(|f_{u}|\leq f_{\mathrm{D}}\). Then the received signal is:  
\[s(t)=H(\omega,t)e^{i\omega t} \tag{66}\]  
where \(H(\omega)\) is:  
\[H(\omega,t)=\sum_{u=1}^{U}a_{u}e^{-i\omega\tau_{u}+i\  
When there is no Doppler frequency shift, the received signal is still a harmonic signal with time \(f=10\) Hz, and there is no distortion. When there is a Doppler frequency shift, the received signal is distorted and changes continuously with the increase of time, resulting in a decrease in communication reliability. The results show that the frequency shift caused by the Doppler effect degrades the BER performance of UAV communication. In order to meet the communication reliability required by emergency management, it is necessary to correct the data by compensating the Doppler frequency shift.  
The Doppler frequency is separated based on the AOAs signal, and the Doppler frequency of different paths is compensated. When the maximum Doppler frequency \(f_{D}\) and the arrival angle \(\theta_{u}\) of the path \(u\) are obtained, the compensation frequency of the path is \(f_{D}\cos\theta_{u}\).  
The Doppler frequency shift compensation of path \(u\) is:  
\[\tilde{s}^{(u)}(n)=e^{-j2\pi f_{D}nT\cos\theta_{u}}s^{(u)}(n) \tag{68}\]  
Without linear interpolation and fast Fourier transform, the received signal is:  
\[S^{(u)}(k)=\frac{1}{\sqrt{n}}\sum_{n=1}^{N-1}\tilde{s}^{(u)}\big{(}n+N_{g} \big{)}e^{-j2\pi nk/N} \tag{69}\]