esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper proves the feasibility of the LoRaWAN transmission from a sensor node submerged in salt water at a depth of 6 cm, reinforcing the results already presented in [8] for the fresh water case and validating the usability of the LoRa technology in contexts of underwater to above water transmissions. Although the packet loss is minimal and the available link margin is sufficient in laboratory test conditions, the reliability of the transmission degrades in the case of field measurements where noise and loss factors increase. However, the results remain satisfactory as they demonstrate the usability of this technology in a set of applications based on transmitter nodes close to the water surface (e.g., systems for the monitoring of water sport athletes mounted in the swim cap, monitoring of bridges and harbor structures, water parameter acquisition) and where the reliability requirement is not mandatory (e.g., high-water or fill-level alarm devices). More generally, these outcomes open the way for salt water monitoring applications relying on low-cost and low-power commercial devices, in contrast with other underwater communication systems that are usually not suitable for the development of pervasive monitoring systems because more complex and expensive infrastructures are required.  
A great accordance is proved between experimental and predicted data, derived according to the proposed link budget model based on the ITU documentation for the estimation of the salt water electric permittivity. Hence, this model can be effectively used as a valid tool to investigate the preliminary feasibility of the LoRa radio channel in different operational conditions, thus allowing to tune the radio settings on the basis of the obtained results and of the project constraints.  
Conceptualization, A.P. and I.C.; methodology, A.P.; software, I.C.; validation, F.C. and R.F.; formal analysis, I.C.; investigation, A.P., F.C. and R.F.; resources, A.P., F.C., R.F. and M.Z.; data curation, I.C.; writing--original draft preparation, A.P. and I.C.; writing--review and editing, A.P., I.C., F.C., R.F. and M.Z.; visualization, I.C.; supervision, A.P. and M.Z.; project administration, A.P. and M.Z.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on \"Telecommunications of the Future\" (PE0000001--program \"RESTART\") and the FSE REACT EU, PON Research and Innovation 2014-2020 (DM 1062/2021).  
Not applicable.  
The authors declare no conflict of interest.
# Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks
## 2 Related Works
Experiments ascertained that Spark is a better option for analyzing Big Data using a multilayer perceptron (MLP). DL4j (Deeplearning4j) applies deep neural networks on distributed Spark servers integrated with GPUs. [PERSON] [34], the founder of DL4j, includes deep learning libraries with Spark and deploys the deep learning networks by parallelizing the dataset. Using distributed Spark, DL4j uses data parallelism and converges across the clusters of the machine. In experiments, distributing data across the clusters using the Resilient Distributed Data (RDD) property of Spark reduces converging time without compromising accuracy percentage. In Reference [35], [PERSON] et al. introduced heterospark, which is Spark, along with GPU accelerated to integrate the computation capability of both. Comparing with GPU, Spark shows promising results due to the increased number of cores in a distributed setup. Spark has a significant benefit in terms of reducing the computation complexity of the neural networks model. [PERSON] et al., in Reference [36], exclusively developed a framework named SparkNet for deep models training in Apache Spark. Caffe library is used for deep models, and Spark RDDs are used for reading the dataset through a customized interface. They benchmarked the performance using the Imagenet dataset and altering the number of workers in the Spark server. The number of standard iterations and clusters is stated in the research paper to omit the communication overhead. From the preceding research papers, it is evident that deep learning accuracy entirely depends on the datasets' size. There is a better chance for a decent accuracy percentage in a deep model as we expand the dataset's size for training. Today, Big Data is flooding in zeta-bytes of data per minute from all over the world, such as social media, sensors, organizations, search queries, and the like. Massive databases are available to save zeta bytes of data, where machine learning and deep learning algorithms help to process the extensive amount of data to get valuable insights as the dataset gets larger, the training time for profound neural network advances in terms of weeks and even months. By distributing the data or model across the number of clusters reduces the computational cost and complexity. In the following section, we will discuss how we implemented our proposed model in Apache Spark, a vibrant ecosystem of deploying deep neural models.
# Multiplexing Perfect Optical Vortex for Holographic Data Storage
###### Abstract  
Holographic data storage (HDS) has emerged as a promising technology for high-capacity data storage. In this study, we propose a novel approach to enhance the storage density in HDS through a multiplexing perfect optical vortex (POV) hologram. By utilizing the orthogonality property of POV, different POV-recording holograms can be multiplexed to store multiple data pages within the single hologram. Compared with the conventional optical vortex, the better storage density of POV through proof-of-principle experiments is demonstrated. For the POV-multiplexing hologram of six data pages, each one can be reconstructed successfully. In addition, we investigate the impact of axicon periods and multiplexing numbers on the storage performance. Our results reveal that an appropriate selection of axicon periods and multiplexing numbers is crucial to balance storage density and bit error rate (BER). The proposed multiplexing approach offers a valuable solution for achieving high-density and secure holographic data storage systems.  
perfect optical vortex; holographic data storage; multiplexing holography Article
## 3 Materials and Methods
### Mixing Parameters  
It has been shown that when the EP content reaches 35 wt%, the EP phase in the EA binder undergoes a phase transition with the asphalt phase, and the binder changes from a thermoplastic to a thermosetting material [25]. Additionally, considering that the EP content of the EA binder commonly used in current engineering was 50 wt% [26; 27], the EP content in the ESA binder was determined to be 35, 40, 45, and 50 wt% and designated as ESA35, ESA40, ESA45, and ESA50. ESACs prepared with the above ESA binders were labeled ESAC35, ESAC40, ESAC45, and ESAC50, respectively. TPS is mainly composed of thermoplastic rubber, which can absorb the lighter components of asphalt when it swells at high temperatures, increasing the viscosity of the binder and improving its comprehensive properties [28]. Currently, the additive amount of TPS in SBS-modified asphalt binder is generally 8-12 wt% [29; 30]. Hence, the TPS content in the HSA binder was set to 7, 9, 11, and 13 wt%, and the HSACs prepared were labeled as HSAC7, HSAC9, HSAC11, and HSAC13, respectively. Additionally, the amount of lignin fiber added to the concrete was 0.3 wt% of the aggregates.
# Exploring the Feasibility of Autonomous Lighting Systems for Pedestrian Crossings in Off-Grid Areas
## 4 Discussion
If the lighting system fails, there is a risk of serious accidents. Therefore, the reliability of the system is crucial to ensure lighting continuity. * **Maintenance and costs** of the lighting system, such as repairs, battery replacement, cleaning of photovoltaic panels, etc. * **Alternative power sources**. In some cases, it may also be necessary to consider alternative power sources, such petrol engine generators. * **Control technologies** can influence and extend operating time. * **Monitoring technologies**. The monitoring and diagnostic systems allow us to improve the performance and condition of the system.
## 4 Results and Discussion
### Calculation of the Free Surface Profiles in Linear Arrangement  
Equation (2) allowed simulation of free surface profiles, once again by the direct step method and adopting the \(C_{D-UC}\) values. The new profiles fit very well the experimental \((x,\ z_{0}+h)\) points. As an example, Figure 10 shows the results for tests T1, T5, T8, T9, T18 and T23. Note that profiles T5, T8, T9 and T23 were not used in the determination of Equation (28), because they were used for validating it.  
Equation (28) was also used to simulate four of the profiles observed by [PERSON] et al. [1], i.e., the ones with comparable densities (\(\phi_{vcg}\) = 1.0%, 1.8%, 4.1% and 7.3%) to those for which the above equation was derived (\(\phi_{vcg}\) = from 0.97% to 4.36%). The comparisons between the experimental and simulated profiles for two densities are shown in Figure 11. Both profiles are well simulated, whereas the water depth related to densities \(\phi_{vcg}\) = 1.8% and 4.1% is slightly underestimated. It should be pointed out that Equation (1) was derived from the results of experimental tests relating to slopes 0.48% \(<i<\) 2.2%. Thus, we can conclude that the method here proposed can give good results.  
Figure 9: Comparison of the computed \(C_{D-UC}\) values using Equation (28) vs. the experimental ones. The dashed lines indicate an error equal to \(\pm\)10%; the dotted lines indicate an error equal to \(\pm\)20%.  
Figure 10: Comparison between the measured and the calculated free surface profiles obtained using \(C_{D-UC}\): (**a**) T1; (**b**) T5; (**c**) T8; (**d**) T9; (**e**) T18; (**f**) T23. The black bold line indicates the flume bottom; the black line is the calculated free surface; the red symbols indicate the measured free surface.
# A Lightweight TA-YOLOv8 Method for the Spot Weld Surface Anomaly Detection of Body in White
## 4 Experiment
### Experimental Pipeline and Detection Systems  
Based on the methods and related technologies discussed in previous sections, we finally developed a Body-in-White welding spot quality detection system, integrating sensing technology, deep learning detection techniques, and communication technology. The system's architecture, shown in Figure 12, includes multiple modules: a data acquisition module, a vision inspection module, and a data management module. The system has been deployed on an actual automotive body welding production line, verifying the practicality and effectiveness of each module.  
Figure 11: Validation sample results for model generalization ability.  
Figure 10: Visualization and validation sample results for model testing. The model effectively captures the location of welding defects through highlighted (green) regions.  
Data Acquisition Module: This system was validated on a production line in the welding workshop of an automotive company. The line includes four welding stations, each equipped with two FANUC robots, with welding guns powered by servo motors. Equipment on the automated line is integrated into the field PLC, ensuring high-reliability welding control.  
Vision Inspection Module: This module utilizes pretrained model weights to evaluate welding spot quality. By implementing the vision inspection algorithms proposed in this study and integrating real-time welding parameter monitoring, this setup ensures adaptability under data constraints, leveraging deep learning visual models to enhance the accuracy and efficiency of weld quality inspection.  
Data Management Module: Comprising a data visualization module, storage and analysis module, and alert module, the data management component serves as the user interface for interacting with the system. It allows users to query relevant information, including the dynamic visualization of data processing, the real-time display of weld spot data, historical data queries, and quality alert notifications. The system provides feedback to the user on the quality of each weld spot on the body, facilitating the further analysis and optimization of the welding process. Additionally, users can review weld quality based on alerts and optimize model learning through continuous quality feedback.
# [PERSON] Glacier Status Analysis Based on Historical Data and Moving Detection Using RPAS
## 6 Data Processing
### Processing of Historical Image Data
#### 6.2.2 Archive Landsat Satellite Data  
Google Earth provides previews of Landsat satellite data from 1984 to 2016 (Figure 20) for the Karale and Knud Rasmussen Glaciers. The data were always taken on the same day (directly on the last day of the year, 31 December), which is an advantage for comparison. In ArcGIS, all of the images were compared (Figure 20). It can be seen from the series of images that major changes to the front of the Knud Rasmussen Glacier did not occur until after 2011. The Karale Glacier began to recede as early as 1990.
# Identification of Rotundone as an Important Contributor to the Flavor of Oak-Aged Spirits
## Materials and Methods
### Quantitative Analysis of Selected Compounds by Stable Isotope Dilution Analysis (SIDA)
#### 3.5.2 Direct Solvent Extraction (DSE)-GC-MS Method  
In a 60 mL conical test tube, whiskkey sample (10 mL) was spiked with various levels of labeled internal standards described in Table 6 and then diluted to 10% ABV with deodorized-distilled water. Dichloromethane (CH\({}_{2}\)Cl\({}_{2}\), 2.25 mL or 3.0 g) was added and the tube sealed using PTFE-lined cap. The tube was shaken vigorously for 5 min and then centrifuged at 1500\(\times\)\(g\). The solvent (bottom CH\({}_{2}\)Cl\({}_{2}\) layer) was dried over 0.5 g of anhydrous sodium sulfate and transferred to a 2 mL sample vial equipped with a PTFE-lined cap. Extracts were stored at \(-\)20 \({}^{\circ}\)C until analysis.  
The GC-MS system used for analysis consisted of a 7890A GC (Agilent Technologies, Inc.)/Pegasus IV time-of-flight (TOF) MS (LECO Corporation). Extracts (2 \(\upmu\)L) were injected in the cold splitless mode (\(-\)50 \({}^{\circ}\)C initial temperature, held 0.1 min, then ramped at 8 \({}^{\circ}\)C/sec to 200 \({}^{\circ}\)C, held for 5 min, then ramped to 250 \({}^{\circ}\)C and held there for remainder of run; 1.10 min splitless valve delay time). Separations were done on a Stabiliwas capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m df; Restek) using helium as the carrier gas (1 mL/min). The initial oven temperature was 40 \({}^{\circ}\)C. After 5 min, the oven temperature was increased at 4 \({}^{\circ}\)C/min to the final temperature of 225 \({}^{\circ}\)C and held for 30 min. The flow rate of helium carrier gas was 1 mL/min. For GC-(TOF) the mass spectra (from 35 to 300 a.m.u.) acquisition rate was 50 spectra/sec. GC-MS interface temperature was 230 \({}^{\circ}\)C, source temperature was 200 \({}^{\circ}\)C, and ionization energy \(-\)70 eV.  
Peak areas for selected (quantitation) ions of labeled IS and target analytes were determined using Leco Chroma TOF software (version 3.34). Mass ion area ratios and R\({}_{t}\) values were used to calculate concentrations of target analytes as described in Section 3.5.1.
# Investigation on Numerical Simulation of VIV of Deep-Sea Flexible Risers
## 4 Results and Discussion
### Characteristics of VIV Track of Riser  
Figure 14 illustrates the mass center trajectories at 17 different slices along the riser axis.  
By comparing Figure 14a-c, it can be observed that the IL and CF displacements of all slices in the riser axis increased with an increase in the slenderness ratio. As shown in Figure 14a, when L/D = 400, a clear \"8\"-shaped vibration trace was visible at each slice. With an increase in slenderness ratio, the \"8\" vibration trace of the riser became increasingly scattered from the middle position of the riser. Figure 14c demonstrates that when L/D = 700, only a faint \"8\"-shaped motion trace was visible at both ends of the riser.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Diameter (D) & 0.04 & m \\ Modulus of elasticity (E) & \(2.74\times 1010\) & N/m\({}^{2}\) \\ Top tension (T) & 1500 & N \\ Mass ratio (m *) & 1.83 & \\ \hline \hline \end{tabular} \({}^{a}\): Dimensionless.  
\end{table}
Table 3: Riser model parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline L/D = 400 & \(f_{1}\)(Hz) & \(f_{2}\)(Hz) & \(f_{3}\)(Hz) & \(f_{4}\)(Hz) & \(f_{5}\)(Hz) & \(f_{6}\)(Hz) \\  & 0.7837 & 1.7136 & 2.6067 & 3.7466 & 5.0832 & 6.6353 \\ \hline L/D = 550 & \(f_{1}\)(Hz) & \(f_{2}\)(Hz) & \(f_{3}\)(Hz) & \(f_{4}\)(Hz) & \(f_{5}\)(Hz) & \(f_{6}\)(Hz) \\  & 0.6421 & 1.3103 & 2.0286 & 2.8192 & 3.6991 & 4.6824 \\ \hline L/D = 700 & \(f_{1}\)(Hz) & \(f_{2}\)(Hz) & \(f_{3}\)(Hz) & \(f_{4}\)(Hz) & \(f_{5}\)(Hz) & \(f_{6}\)(Hz) \\  & 0.4297 & 0.8695 & 1.3587 & 1.8564 & 2.3903 & 2.9675 \\ \hline \hline \end{tabular}
\end{table}
Table 5: First six natural frequencies of riser model in still water.  
Figure 13: _Cont._  
By comparing Figure 14c-f, it can be observed that the IL and CF displacements of all slices in the riser axis increased with an increase in the flow velocity. Figure 14d reveals that a relatively \"8\"-shaped motion trace of the mass center could be seen for all nodes except for partial nodes in the middle section of the riser. As the flow velocity increased, the trajectory of the \"8\"-shaped mass center gradually became more blurred, but it remained faintly visible. When U = 0.25 m/s, as shown in Figure 14f, only a \"e\"k\" \"8\"-shaped motion trace was visible in the region of both ends of the riser. Furthermore, it can be observed that with an increase in flow velocity, the IL and CF displacements continuously increased at the same slenderness ratio. This is because as the flow rate increases, the fluid forces acting on the riser increase, thereby causing the equilibrium positions of the standpipe in the IL direction to increase.  
The \"8\"-shaped vibration trace reflects the occurrence of dual resonance where the IL and CF vibration frequencies have a ratio of two [36]. Due to the influence of different slenderness ratios and flow rates, the IL and CF vibrations at different axial positions of the riser had different phase differences, leading to different figure-eight shapes on the 17 slices along the riser axis.
# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 3 Results
### Dataset  
We validated the proposed COMMA using five datasets. Kvasir-SEG [38] is the largest dataset, containing 1000 challenging images for polyp segmentation. We divided Kvasir into 80%, 10%, and 10% for the training, validation, and testing images, respectively, following the experimental settings of the existing study [27]. CVC-ClinicDB (CVC-612) [39] contains 612 images in which the boundaries are low in contrast to their surroundings. In the same manner, we used 550 images (90%) for training and validation and 62 images (10%) for testing. To enable a fair comparison, all test sets were equivalent to those in previous study [27]. CVC-ColonDB [40], ETIS [41], and EndoScene (CVC-T, CVC-300) [42] were used to evaluate the generalization performance, because these small datasets contain 380, 196, and 60 images, respectively.
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
## 2 Fault Feature Extraction
### Hilbert-Full-Vector Spectrum  
The traditional Hilbert-envelope spectrum is based on the Fourier transform of the envelope signal of a single channel, and the obtained device spectrum information is not comprehensive. The Hilbert-full-vector spectrum is based on the principle of homologous information fusion. First, the Hilbert-envelope signals of the horizontal and vertical channels at the same section of the device are fused, and then FFT operation is performed to obtain the spectrum information that can fully reflect the actual state of the device. The steps of the Hilbert-full-vector spectrum are as follows:  
(1) Two identical sensors are used to simultaneously collect signals, \(H(t)\), \(V(t)\), from the mutual perpendicular direction of the same section of the rotating device.  
(2) According to Equations (1) and (2), the Hilbert transform is applied to signals \(H(t)\) and \(V(t)\) to obtain \(H_{h}(t)\) and \(V_{h}(t)\), respectively, thus constituting analytic signals \(H_{a}(t)\) and \(V_{a}(t)\) with the original signals \(H(t)\) and \(V(t)\):  
\[\begin{array}{ll}H_{a}(t)&=H(t)+jH_{h}(t)\\ V_{a}(t)&=V(t)+jV_{h}(t)\end{array} \tag{7}\]  
Then the envelopes of \(H(t)\) and \(V(t)\) are obtained according to Equation (3) as follows:  
\[\begin{array}{ll}A_{H}(t)&=\sqrt{H^{2}(t)+H_{h}^{2}(t)}\\ A_{V}(t)&=\sqrt{V^{2}(t)+V_{h}^{2}(t)}\end{array} \tag{8}\]  
(3) Based on the full vector spectrum technology, the dual-channel envelope signals \(A_{H}(t)\) and \(A_{V}(t)\) are fused with the same source information, and the principal vector is obtained by Equations (4)-(6), and then the FFT operation is performed on it. Finally, the spectrum information that can reflect the actual operational state of the device is obtained.
_Applied_  
_sciences_  
Force-Based Seismic Evaluation of Retrofitting Interventions of Historic Masonry Castles by 3D Rigid Block Limit Analysis  
[PERSON] 1, [PERSON] 2,*, [PERSON] 1\({}^{\text{(c)}}\), [PERSON] 2,  
**[PERSON] 2 and [PERSON] 2**  
1 Department of Structures for Engineering and Architecture, University of Naples \"Federico II\", Naples 80134, Italy; [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (F.P.A.P)  
2 Department of Engineering and Geology, University \"G d'Annunzio\" of Chieti-Pescara, Pescara 65127, Italy; [EMAIL_ADDRESS] (M.R.F.); [EMAIL_ADDRESS] (C.M.); [EMAIL_ADDRESS] (M.V.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +39-085-453-7071 check for updates  
Received: 15 June 2020; Accepted: 16 July 2020; Published: 22 July 2020  
**Featured Application: The methodology proposed in the paper is useful for the pre-selection of the most suitable retrofitting intervention for masonry constructions, with specific regard to Italian castles. In particular, it enhances the understanding of the effect of local structural interventions on the global behavior of masonry construction.**  
**Abstract:** This paper deals with the force-based assessment of collapse mechanisms and strengthening interventions of the historic masonry castle \"Bussi sul Tirino\" (Abruzzi, Italy) using rigid block limit analysis (RBLA). The structure, which is a fortified palace dating back to the 11 th century, has experienced severe earthquakes over the centuries and was hit once again in 2009 by the L'Aquila earthquake. Based on the historical analysis and the results of in situ investigations, a spatial rigid block model of an entire structural unit was generated using the in-house software LiABlock_3D. The software is a MATLAB(r) based tool for three-dimensional RBLA, which provides as outputs collapse failure modes and collapse load multipliers. In addition, a specific routine was developed for the purpose of the study to compute the participating mass ratio and the spectral acceleration that activated the failure mechanisms. The results of the numerical analysis were used to address three different retrofitting interventions, based on the use of connection elements and ties that, according to the minimum intervention principle, progressively enhanced the seismic capacity. Comparisons in terms of seismic safety indices are finally provided in order to give a quantitative measure of the effectiveness of the adopted retrofitting strategies.  
**Keywords:** masonry structures; seismic vulnerability; fortified palace; castles; discrete model; rigid blocks; limit equilibrium analysis; LiABlock_3D; cultural heritage