esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Mechanism and Structural Analysis of Chain Slings with Non-Standard Connections
## 2 Overview  
Figure 1 presents a case of an industrial accident in which two lifting chains fractured simultaneously during the unloading of a heavy load. The incident occurred under non-standard rigging conditions, where the chain slings were directly wrapped around and connected to a hook and an eye bolt, instead of using the standard connection methods involving master links or shackles. Such non-standard practices, although clearly deviating from recommended safety guidelines, are frequently observed in industrial settings due to spatial constraints or for the sake of operational convenience.  
As illustrated in Figure 2, wrapping a chain directly around an eye bolt or hook introduces abnormal bending loads and circumferential stresses on the chain links. Furthermore, this type of connection leads to unbalanced load distribution at the contact points, resulting in localized deformation and stress concentration. Particularly, when the chain is subjected to repeated torsional stresses in combination with bending, rapid failure may occur even when the applied load does not exceed the design tensile strength or rated breaking load.  
In this accident case, the lifting chains had been reused without undergoing appropriate maintenance procedures such as visual inspection or dimensional checks. Due to deformation and reduced durability caused by the non-standard connection method, the chains ultimately fractured under the weight of the load. Therefore, to ensure the structural stability of chain slings and prevent failure, it is essential to use standard connection devices that distribute loads evenly during rigging operations. Specifically, methods involving direct wrapping of chains around cargo, eye bolts, or hooks distort the intended load transfer paths and should be avoided, as they can lead to premature failure well before the chain reaches its expected service life.  
Figure 1: Case overview of simultaneous chain sling failure.  
Based on this accident case, the present study aims to analyze the root causes of chain failure and quantitatively investigate the structural issues that may arise under non-standard usage conditions.
## 3 Results and Discussion
### Scenario 1  
In this scenario, BER and throughput performance of an SU are investigated before and after pairing it with the interfering MU. To measure the performance, for simplicity, a single SU is considered as a high-gain UE with a single interfering MU (interfering signal strength = 20 dBm). Further, the unpaired SU contains ICI effects produced by the interfering MU. To manage the situation, SU pairs with the MU using power domain NOMA suppressing the ICI. The results are generated using power allocation as SU (SCU) = 0.4 and MU (SEU) = 0.6, where the total power is equal to 1, i.e., \(P_{SCU}+P_{SEU}=1\). The scenario is shown in Figure 5a and the results are presented in Figures 6-13.  
\begin{table}
\begin{tabular}{c c} \hline \hline Bandwidth & 1.4 MHz \\ \hline Radio access scheme & OFDM \\ \hline Subcarrier separation & 15 kHz \\ \hline Number of Subcarriers & 600 (50 RBs) \\ \hline FFT size & 2048 \\ \hline Subframe length & 1.0 ms (14 OFDM symbols) \\ \hline Symbol Duration & 66.67 \(\upmu\)s + CP: 4.69 \(\upmu\)s \\ \hline Data modulation for SCU & BPSK, QPSK, QAM, 8-QAM \\ \hline Data modulation for SEU & BPSK, QPSK, QAM, 8-QAM \\ \hline User Power for NOMA & SCU = 0.4, 0.2, SEU = 0.6, 0.8 \\ \hline Channel/Noise & Rayleigh fading channel/ AWGN \\ \hline Maximum Doppler frequency & 5.55 Hz \\ \hline FFT timing detection & Ideal \\ \hline Inter-Cell Interference (ICI) & 1 interferer = 20 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Parameters.  
Figure 5: Simulation Scenarios: (**a**) Interfering MU connected to SBS using NOMA; (**b**) two NOMA paired SUs with one interfering MU.  
Figure 6: BER performance using BPSK modulation; NOMA vs. WHT–NOMA, power allocation: SU (SCU) = 0.4 and MU (SEU) = 0.6.  
Figure 7: BER performance using QPSK modulation; NOMA vs. WHT–NOMA, power allocation: SU (SCU) = 0.4 and MU (SEU) = 0.6.  
Figure 8: BER performance using QAM modulation; NOMA vs. WHT–NOMA, power allocation: SU (SCU) = 0.4 and MU (SEU) = 0.6.  
Figure 10: Throughput performance using BPSK modulation; NOMA vs. WHT–NOMA, power allocation: SU (SCU) = 0.4 and MU (SEU) = 0.6.  
Figure 9: BER performance using 8–QAM modulation; NOMA vs. WHT–NOMA, power allocation: SU (SCU) = 0.4 and MU (SEU) = 0.6.  
Figure 11: Throughput performance using QPSK modulation; NOMA vs. WHT–NOMA, power allocation: SU (SCU) = 0.4 and MU (SEU) = 0.6.  
Figure 12: Throughput performance using QAM modulation; NOMA vs. WHT–NOMA, power allocation: SU (SCU) = 0.4 and MU (SEU) = 0.6.  
Figure 13: Throughput performance using 8–QAM modulation; NOMA vs. WHT–NOMA, power allocation: SU (SCU) = 0.4 and MU (SEU) = 0.6.
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
###### Abstract
The kinematic model, EKF state evolution model and the solution of the Jacobi matrix are firstly derived for radar-assisted beam tracking in curvilinear mobility scenarios. * We propose to use the more realistic extended Saleh-Valenzuela (eSV) channel model in the DFRC system and consider the delay between different paths to expand the narrowband channel to the wideband channel.
## 2 Materials and Methods
### Data Sources
#### 2.2.1. MOD17A2  
MOD17A2 is an 8 day composite GPP product at a 1 km spatial resolution. It provides a measure of the growth of the terrestrial vegetation using daily MODIS land cover, fAPAR, LAI, and surface meteorology for the global vegetated land surface [17]. MOD17A2 was acquired from the National Aeronautics and Space Administration (NASA) website ([[http://modis-land.gsfc.nasa.gov](http://modis-land.gsfc.nasa.gov)]([http://modis-land.gsfc.nasa.gov](http://modis-land.gsfc.nasa.gov))) and covered 2014-2015, representing 90 composited data points. While this pixel was at a coarser scale  
Figure 1: The Sapelo Island, Georgia, USA. The top inset shows Sapelo Island’s location within the state of Georgia. The bottom inset shows a zoomed-in view of the location of the EC flux tower, white triangle, as well as the MODIS pixel used in this study.  
than the focal 500 m surface reflectance pixel we used to build our salt marsh GPP model, the 1 km scale was the only resolution available for the MOD17 product at the time of this study and still samples _S. alterniflora_ marsh close to our EC tower. In addition, the marshes surrounding the tower is coved my multiple 500 m pixels and depending on the wind speed, the flux footprint varies in size and direction, and may very well contain fluxes from adjacent areas beyond the pixel.
# Parallelization of the Bison Algorithm Applied to Data Classification
## 4 Experimental Setup
### Dataset Description
#### 4.1.3 Class Selection  
For the purpose of this study, only the instances belonging to the class 'behavior' were used. This choice was driven by the specific objectives of our classification task, focusing on accurately predicting behavioral outcomes based on the provided features.
# Metallurgical Effects of Niobium and Molybdenum on Heat-Affected Zone Toughness in Low-Carbon Steel
## 2 Materials and Methods
### Heat Affected Zone Simulation and Characterization  
The simulation of heat affected sub-zones was performed in a HAZ simulator (Vatron GmbH, Linz, Austria) using electric current for fast conductive heating to a peak temperature and an air-water mixture applied via four coaxially positioned nozzles for rapid cooling (Figure 4). A thermocouple fixed in the center of the sample provides feedback for the time-temperature control software (Lab-View). The samples for this test were machined from the laboratory-rolled 13 mm thick plates to a size of \(10\times 10\times 95\) mm\({}^{3}\). These samples were instrumented with thermocouples and clamped into the electrodes leaving a free length of 60 mm. This setup results in a sample volume of approximately \(10\times 10\times 15\) mm\({}^{3}\) having a homogeneous heat affected microstructure. For hardness measurements (Vickers HV10) and metallographic analysis, the homogeneous HAZ was cut from the sample. For tensile testing, the bar was machined to a round standard tensile sample (\(\lx@sectionsign\)10 mm) with the homogeneous zone being centered in the gage length. Likewise, Charpy test bars of \(10\times 10\) mm\({}^{2}\) were machined having the V-notch centered to the homogeneous HAZ.  
Figure 4: (**a**) Setup for HAZ simulation and (**b**) sample geometries for mechanical testing.  
Figure 3: Light optical micrographs (Le Pera etching) of the as-rolled materials: (**a**) V2-a (polygonal ferrite); (**b**) V2-d (mostly acicular ferrite); (**c**) V4-a (coarse acicular ferrite); (**d**) V4-d (fine acicular ferrite).  
HAZ simulations were performed for the inter-critical zone (ICZ), i.e., the peak temperature being in the ferrite-austenite two-phase region, and for the coarse-grained zone (CGZ) using a peak temperature of 1300 \({}^{\circ}\)C. For defining the peak temperature in the ICZ, dilatometer tests were done identifying the transformation points in the up-heat cycle at a heating rate of 600 K/s. The peak temperature should be set to in between the maximum transformation points A\({}_{\text{clp}}\) and A\({}_{\text{c3p}}\). This condition was for all alloys fulfilled for a peak temperature of 900 \({}^{\circ}\)C. The fine-grained zone requires a peak temperature being approximately 50 \({}^{\circ}\)C above the transformation finish temperature A\({}_{\text{c3e}}\). This condition was fulfilled at 1100 \({}^{\circ}\)C for the current alloys. For obtaining the characteristic tensile properties across the entire HAZ, samples were produced at peak temperatures of 300, 600, 800, 900, 1100 and 1300 \({}^{\circ}\)C and machined into tensile bars. The heating rate was always set to around 600 K/s and the cooling rate was adjusted to yield an 800-500 \({}^{\circ}\)C temperature interval (\(\Delta\)T\({}_{\text{8/5}}\)) of 25 s corresponding to typical values obtained by submerged arc welding in practice.
# Seismic Reliability Assessment of Water Supply Systems Considering Critical Paths
## 3 Application and Results  
### Target Water Supply Network  
The area selected for application of the methodology proposed in this study is the industrial water supply system of A-City, a representative industrial city in South Korea. The water supply network diagram of the area, corresponding to the water supply system, is shown in Figure 3a. There was a magnitude 5.0 earthquake in the vicinity in 2016, and no damage to the water supply system was reported. The target area is a city located near the southern sea, as shown in Figure 3b, which is more likely to be seismic than the rest of Korea. In addition, the area is located near the Trans-Pacific Earthquake Zone, where earthquakes are likely to occur in the future. The total demand for water in the A-City industrial water supply system is about 2,039,765 m\({}^{3}\)/day, and the total length of the pipeline is 279 km. The network system is composed of 1220 nodes and 1300 pipelines. The system supplies water to the main supply from a total of nine independent water catchment areas. Fourteen emergency-linked supply lines and double track lines have been established in preparation for any abnormal situations. The seismic reliability assessment in accordance with the operation scenario of the emergency pipeline is not considered in this study. Under normal supply conditions, this system supplies water at an average pressure of 27.34 m, which is an appropriate water pressure that is higher than the design standard of 15.3 m (Figure 3c).
## 3 System Model and Methodology
### Receiver Block  
Figure 1 highlights the receiver block of the proposed approach. The channel-received data are passed via the built-in dwt MATLAB function, which (depending on the wavelet type and including the approximation and detailed coefficient vectors cA and cD, respectively) returns the one-dimensional wavelet transform. The type of wavelet used in the transmitter is also used here. Since cA and cD are both the same length, only cA is reshaped and cD is disregarded. The reshaped data are then demodulated using 16 QAM demodulation. The biterr function is applied to the demodulated data to determine the BER, and the results are shown using the SNR range. The findings are represented in terms of BER and lead to reductions in BER, which improves system performance.  
When employing the wavelet transform, it is vital to recover the original signal from the wavelet coefficients. To achieve perfect reconstruction, certain conditions must be met by the analysis and filters [26]. Let G(z) and, \(G^{*}(\texttt{z})\) represent low pass and synthesis filters,  
Figure 1: Overview of the system model.  
respectively, and H(z) and, H\({}^{*}\)(z) represent high pass and synthesis filters, respectively. The filters must then meet the following two requirements:  
\[\mathrm{G(-z)G^{*}(z)+H(-z)H^{*}(z)=0} \tag{4}\]  
\[\mathrm{G(z)G^{*}(z)+H(z)H^{*}(z)=2Z^{-d}} \tag{5}\]  
According to Equations (4) and (5), the reconstruction is aliasing-free and the amplitude distortion has an amplitude of one. When the analysis and synthesis filters are switched, the perfect reconstruction condition does not change.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### MAC Layer--LoRaWAN
#### 2.4.3 MAC Message Formats  
LoRaWAN frame format is given in Figure 13. MAC Header field (MHDR) has MType field to distinguish different MAC messages given in Table 4.  
Figure 11: LoRaWAN Downlink Frame Format.  
Figure 12: DL receive window.  
Figure 10: LoRaWAN Uplink Frame Format.  
Join Request and Accept messages are used by the Over The Air Activation (OTAA) process. Data messages can be either MAC Commands or Data messages. Unconfirmed messages do not require any Acknowledgment (ACK) mechanisms to check message delivery.
# Re-Understanding the Sedimentary Environment of the Wufeng-Longmaxi Shales in the Sichuan Basin
## 2 Evidence That Marine Source Rocks Developed in a Shallow-Water Restricted Environment
### The Salinity of Marine Shales and the Closure Condition of Water Bodies  
A substantial body of evidence indicates that marine shale layers rich in organic matter are characterized by medium-to-low salinity, a phenomenon that appears to be universal. [PERSON] et al. (1987) (26), [PERSON] (1987) (27), and [PERSON] and [PERSON] (1991) (28) demonstrated that the low salinity observed in Wufeng-Longmaxi shales is likely related to the influence of onshore freshwater. Furthermore, the formation of oil shales during the Middle and Late Carboniferous occurred in a shallow, low-salinity marine environment (29). The Badong Formation of the Middle Triassic in Chongqing Youyang and Banan contains cephalopod fossils, suggesting that the depositional environment was a closed bay-lagoon system, potentially influenced by nearby freshwater sources (30). Many oil and gas-bearing basins exhibit similar relatively closed depositional environments for their source rocks. For instance, the Middle and Lower Cambrian source rocks in the western section of the Bachu  
Figure 2: The relationship between the TOC content of lake surface sediments and the lake area and water depth. (**a**) Relationship with water depth; (**b**) relationship with area. TOC exhibits a negative correlation with both the lake area and water depth.  
Uplift in the Tarim Basin are characterized by lagoonal facies [31]. In the Bonaparte Basin of Australia, plankton-rich mud shales predominantly developed in a confined marine sedimentary environment [32]. Additionally, Upper Pennsylvania Cline shales in the Midland Basin of the United States formed in a highly restricted, closed or semi-closed marine environment [33]. The Caucasus Basin, situated between the Black Sea and the Central Caspian Sea, functioned as a restricted semi-closed sea basin during the Jurassic, where black shales and coal-measure strata developed [34].  
Earlier scholars consistently recognized and demonstrated the significance of environmental closures through extensive data analyses. [PERSON] (1937) [35], [PERSON] (1941) [36], and others suggested that water bodies in closed basins, silted basins, or restricted basins are insulated from the effects of seawater waves. In these environments, disturbances and stagnation occur, allowing for the preservation of organic matter. Notably, most oil and gas basins globally are classified as closed or semi-closed types. Scholars such as [PERSON] (1954, 1957) [4; 5] and [PERSON] (1986) [6] also contend that organic matter is enriched in closed environments. Furthermore, [PERSON] (2003) [37] posits that the conditions for oil-shale formation are analogous to those of sapropel coal, primarily occurring in a closed water flow environment.  
Therefore, the water environment in which black organic-rich shales form must be somewhat isolated from the open sea and must possess favorable closure conditions to facilitate the accumulation of organic matter and minerals. This conclusion holds universally.
## 3 Simulation Studies
### Some AUVs Break Down  
To prove the robustness of proposed approach, some AUV failures are added in this part of simulation. When search in real surface-water workspaces, it is likely that two AUVs suddenly break down due to mechanical problems. and then it is an important index for measuring the proposed algorithm's cooperation to see whether the multi-AUV work system could complete its search task through internal adjustment. In this case, the simulation deals with AUV failures in the same simulation environment as that in the Section 3.2. There are six AUVs involved in search task for one target. At the beginning, six AUVs are normal search target in the surface-water workspaces. After a period of time, the target \(T1\) is found by the AUV \(R3\). The AUV \(R3\) will track the target \(T1\) and send the location information of the target to other AUVs. According to the location of the target \(T1\), the multi-AUV system selects the four AUVs closest to the \(T1\). \(R1\), \(R2\), \(R3\), and \(R6\) are assigned to the target \(T1\). Since \(R4\) and \(R5\) fail in the competition, they will not join in the pursuing task but keep search target. One of the AUVs, \(R3\), breaks down in time, but the remaining AUV members still function properly (shown as in Figure 6a). Despite the breakdown of \(R3\), AUV \(R4\) replaces AUV \(R3\) by reassigning tasks, the whole team is not paralyzed but keeps working on for their hunting task. When coming to the 40 th second, the AUV \(R6\) also fails (shown as in Figure 6b). Since a distributed architecture is adopted, the rest one AUV \(R5\) will not be affected but go on with their hunt. And at last, the AUVs \(R1\), \(R2\), \(R4\), and \(R5\) got the target \(T1\). The final trajectories of the AUV team (see Figure 6c) show that the proposed algorithm can work satisfactorily in the case of unexpected events, and it does not need any added changes for different situations. From this simulation, it shows that the improved potential field algorithm has the ability to complete search task in the case of AUV mechanical failures through dynamical allocation. This also demonstrates an excellent cooperation of the proposed algorithm.  
Figure 5: Simulation of hunting process with two targets. (**a**) The initial state; (**b**) AUVs’ hunting trajectory for the first target; (**c**) AUVs’ search trajectory for the first target; (**d**) Final trajectories of the AUVs.
# CPAM: Cross Patch Attention Module for Complex Texture Tile Block Defect Detection
###### Abstract  
Due to the little variation in defect points, tile block defect detection typically detects subtle defects in large-format images, allowing defective characteristics to be displayed regionally. Traditional convolutional neural network architectures that extract regional features take into account the connection between regional features simply, resulting in the presence of region-specific bias, which makes tile block defect detection still a challenging task. To address this challenge, this paper divides feature information into patches that can represent different regional features. Additionally, the relationship between different patches and tile block defects is studied; as a result, this paper proposes a new attention mechanism called the Cross Patch Attention Module (CPAM). Since the regional performance of patches is consistent with the tile block defect characteristics, CPAM can distinguish various regional features by patches. Then, in order to create reliable one-dimensional patch information, CPAM provides a method to connect patches linearly in two spatial directions. This takes into account the correlation of adjacent patches in various spatial directions. Finally, by extracting the regional characteristics of patches, CPAM can successfully assist the model in distinguishing the importance of different patches. The experimental results demonstrate that CPAM has excellent performance for tile block defect detection, and plugging CPAM into different end-to-end models can have a good gain effect, which can effectively and stably help the model to complete the task of tile block defect detection.  
attention mechanism; deep learning; tile; defect detection +
Footnote †: journal: Medical Image and Vision
## 1 Introduction
established a mechanical model for GSER with roof cutting and derived the relationship between the tensile stress in the uncut portion and the roof-cutting height, angle, and roof thickness [35]. [PERSON] et al. explored the relationship between key parameters such as roof-cutting height and angle and the abutment pressure of the surrounding rock [36]. [PERSON] et al. analyzed the mechanical model of the failure mechanism of the surrounding rock of the short-arm beam roof and concluded that the roof-cutting and pressure relief method for GSER can effectively cut off the transmission path of the roof stress from the working face to the roadway roof, thereby reducing or eliminating the impact of the roof pressure on the stability of the roadway [37]. [PERSON] et al. established a mechanical model for the fracture of the short-arm beam roof with roof cutting and derived a calculation method for the support resistance when the short-arm beam fractures at the critical position [38; 39]. Scholars at home and abroad have conducted extensive and in-depth discussions from various perspectives such as theoretical analysis, numerical simulation, and field measurements. However, the differential distribution of virgin rock stress and the stress concentration in the roof caused by the unique geological characteristics of karst mountainous areas are crucial factors affecting the stability of the surrounding rock in gob-side entry retention. This paper aims to address the challenge of controlling the stability of surrounding rock in gob-side entry retention in karst mountainous areas worldwide. Based on specific engineering practices in coal mines, this paper adopts theoretical analysis, numerical simulation, and field measurements to determine the optimal roof-cutting parameters for different burial depth sections in karst mountainous areas and verify their rationality. The main roof-cutting parameters include the cutting angle and the cutting height. This research provides solutions and theoretical guidance for gob-side entry retaining operations in mining areas worldwide facing geological conditions similar to those in karst mountainous areas.