esa-sceva/satcom-chunk-collection huggingface dataset:
# Transactive Energy to Thwart Load Altering Attacks on Power Distribution Systems
## 3 Automatic Generation Control  
The automatic generation control mechanism is based on this key feature of the alternative current (AC) power grids--that the voltages of all buses follow sinusoidal waveforms. Namely, a power imbalance in AC power grids impacts the frequency of sinusoidal voltages throughout the power grid. For instance, an imbalance between power generation and power consumption in a power distribution system impacts the frequency of sinusoidal voltages not only in the power distribution system, but also in all other parts of the power grid. The generators sense and measure the changes in the frequency of sinusoidal voltages and react to them. In a case where the power distribution system encounters an over-supply of energy, the frequency of the sinusoidal voltages leaps from the rated frequency of 60 Hz. The generators observe the surge in the frequency of sinusoidal voltages and react to it by reducing their power injection into the power grid. Similarly, in a case where the power distribution system encounters an under-supply of energy, the frequency of the sinusoidal voltagesdrops below the rated frequency of 60 Hz. The generators observe the frequency drop and react to it by increasing their power injection to the power grid.
# Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
## 2 Material and Methods  
### Traditional Balancing Methods  
The purpose of every testing, adjusting, and balancing (TAB) procedure is to reach the only single configuration of the balancing devices and setting of the pump at which two main conditions are fulfilled: (i) flow rates are measured at the design value (within the tolerances), (ii) consumption is the minimum achievable. The latter condition can be referred to as the \"minimum-energy or minimum-pressure balancing requirement\" and is satisfied when at least one path (namely the most disadvantaged--MD) sees all valves completely open and the pressure provided by the pump is that required for the flow at the MD terminal to reach its nominal value. All balancing methods, to be satisfactory, must be able to identify this unique configuration.  
In current practice, the most applied procedures are the ones proposed by ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) [28] and/or NEBB (U.S. National Environmental Balancing Bureau) [29], both basically refer to the same methods: the ratio method (RM), also referred to as the proportional method, and the stepwise method (SM), both iterative in nature. The same technical arrangement is required for the application of both methods in terms of balancing devices (typically calibrated balancing valves), requested accessibility, measurement techniques of terminal/branch flow rates, as well as total flow rate. Whatever the procedure followed, flow meters' accuracy must fit the required tolerance.  
Considering the NEBB reference, in A are summarized the operational steps for the TAB activity of a constant water volume (CWV) system according to the two methods mentioned.
## 4 Results
### Data Collection
#### 4.1.2 Crowdsourced 3D Digitization  
As another option for gathering 3D content, we implemented a low-end 3D digitization pipeline to document heritage with images taken with a smartphone (Figure 2). The goal was to document cultural heritage using images and 3D models from user-generated photos and to integrate the results in the DFG 3D-Viewer repository [81]. The web frontend and processing pipeline were in a beta state at the end of 2023, and had already contributed 3D sculptures to our 4D applications.  
3 DHeritage consists of a webpage frontend in multiple languages (currently English, German, Ukrainian, Russian, and Arabic), providing a guided workflow to take images and upload them to the portal servers with metadata. Metadata can be freely added or retrieved from Wikipedia for object descriptions, from ORCID [283] for user information, and from Geonames for location information [284]. After uploading, a server-side process is initialized, which uses a scripted Meshlab pipeline to create 3D models from these images. We used this tool with an unsupervised pipeline; we are currently automatically uploading the models to Sketchfab and then retrieving them in the DFG 3D-Viewer to reduce error sources and data conversion.  
The workflow has been operational in public beta since September 2022 and used in various settings with ~400 images of 21 objects processed so far; of those objects, 19 could be transferred to a 3D model. As a general finding, all 3D meshes produced in the automatically processed pipeline are of low quality with non-watertight and gapped meshes. Despite these limitations, the 3D objects give at least a visual impression of the shape and texture and are of sufficient quality for viewers.  
Figure 1: (**Left**): Screenshot of the website for the contest entitled “Jena at its most beautiful ever” ([[https://das-schoenste-jena.de/](https://das-schoenste-jena.de/)]([https://das-schoenste-jena.de/](https://das-schoenste-jena.de/)) (accessed on 17 February 2024)); (**Right**): Screenshot of the re-photo application ([https://4 dcity.org](https://4 dcity.org) (accessed on 17 February 2024)).  
Figure 2: 3 DHeritage Workflow Scheme.
# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
## 4 Model Introduction and Application
### Multi-Scale Convolutional Neural Network  
CNN is a classic deep-learning algorithm. However, in this study, the input covers 25 parameters that are correlated with the output. MCNN focuses on the extraction of multi-scale features, and its structure usually contains multiple parallel convolution and pooling layers. Each layer focuses on feature extraction at different scales, which helps capture feature information of different targets. The overall structure of MCNN is shown in Figure 11. The multi-scale convolution module in Figure 11 uses three convolution layers with different convolution kernel sizes to extract multi-scale features of the input data. The outputs of these feature extraction layers are further fed into the fourth fusion convolutional layer for feature fusion operations. Finally, the features processed by a deep convolutional model are transferred into the fully connected layer to produce the final output result.  
In the convolutional layer, the mathematical form of the convolution operation can be expressed as Equation (4):  
\[X_{O}=\sum(X_{I}\times W_{I})+b_{I} \tag{4}\]  
where \(X_{O}\) is the convolution calculation result, \(X_{I}\) is the input of the previous layer, and \(W_{I}\) and \(b_{I}\) are the weight and bias terms of the \(l_{th}\) convolutional layer respectively.  
Figure 10: Distribution of related sensors.  
The kernel_size, padding and stride of each Conv_block are illustrated Figure 11. For example, in Conv_block1, the kernel_size is 2, the padding is 3, and the stride is 2. This model uses BatchNorm2d for normalization, rectified linear unit (ReLU) as activation function in the convolution block and fully connected layer, and SoftMax activation function in the output layer.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 4 Knowledge Gaps and Challenges
### Indoor 3D Reconstruction  
Continuous monitoring of the construction process is necessary, both indoors and outdoors. Compared with outdoor, indoor construction monitoring contains more contents. First, many elements need to be arranged (e.g., pipeline and cable installation, surface decoration, and fire protection) which makes detailed progress monitoring challenging [67]. Second, many construction activities occur indoors. All these cover a significant portion of the whole project and the delays associated with them can result in costly consequences and re-scheduling of the project [68]. Third, when the work moves indoors, the need for situation awareness and monitoring increases because of many trades involved including  
Figure 11: Effect of shadow on a single working day: (**a**) 3:00 PM; (**b**) 4:00 PM; (**c**) 6:00 PM [33].  
Figure 10: Different weather conditions during a construction project: (**a**) fog; (**b**) rainy; and (**c**) snow [33].  
site managers, framers, insulation installers, electricians, drywall installers, plasterers, painters, and laborers [69].  
However, the interior construction sites are complicated, congested, and frequently changing. Especially in civil buildings, the available operating space is extremely limited. Therefore, some 3D reconstruction techniques are neither directly applicable indoors nor validated for interior sites [4], although most of them are applied on outdoor construction sites. Of course, many scholars have also explored the construction progress monitoring under the indoor scene, providing interesting directions, such as the results of [PERSON] et al. [56], [PERSON] et al. [45] and [PERSON] et al. [29].
# Dual-Gratings Imaging Spectrometer
## 2 Dual-Gratings Imaging Spectrometer  
### Traditional Imaging Spectrometer  
In general, a spectrometer is composed of a slit, collimating mirror, dispersing component, focusing mirror, and detector [16]. There are several classical imaging spectrometers, e.g., E-F structure, C-T structure, and Littrow structure. Here, we choose to use one of the simplest E-F structures as the basic structure for description, and other structures can also be selected. The traditional E-F structured spectrometers only use one mirror that serves both as collimator and imaging lens. A slit is also placed on the object plane. The light passing through the slit is then converted into collimated light by the mirror and exits at different dispersion angles after passing through the grating. It is finally imaged on the image plane by the same mirror as shown in Figure 2.  
The main advantages of the E-F structure imaging spectrometer are its simple assembly and high-quality images.
## 3 Results
### Analysis of Verification Results of Different Data
#### 3.5.2 Continuous Occlusion Results with the ORL Database
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Occlusion Rate and Method**} & \multirow{2}{*}{**Image Size**} & \multicolumn{6}{c}{**Method**} \\ \cline{3-8}  & & **LRC** & **SRC** & **CRC** & **ESRC** & **Module LRC** & **BPLRC** \\ \hline \multirow{3}{*}{10\% (Vertical Occlusion)} & \(15\times 10\) & 89.38 & 36.88 & 53.13 & 58.75 & 85.63 & **91.25** \\  & \(20\times 15\) & 85.63 & 17.50 & 60.00 & 24.38 & 91.25 & **95.00** \\  & \(25\times 20\) & 89.38 & 47.50 & 63.13 & 55.00 & 90.63 & **93.13** \\ \hline \multirow{3}{*}{20\% (Diagonal Occlusion)} & \(15\times 10\) & 23.75 & 13.75 & 30.63 & 31.25 & 53.13 & **76.25** \\  & \(20\times 15\) & 28.13 & 5.63 & 36.88 & 3.75 & 85.63 & **85.63** \\ \cline{1-1}  & \(25\times 20\) & 27.50 & 17.50 & 26.88 & 12.50 & 85.63 & **85.63** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy (%) of different methods when identifying 10% vertically and 20% diagonally occluded face images from the ORL database. It can be seen from Table 8 that, compared to face images randomly obscured by 10% and 20%, the recognition accuracy of the BPLRC method has decreased. However, the BPLRC method also obtains the best results when compared to other related methods.
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 3 Supercritical Water Gasification (SCWG)
### SCWG of Plastic  
As said previously, in supercritical conditions, water acts as a non-polar solvent which can solve the problems of poor heat transfer and high viscosity of plastic material by dissolving the plastic fragments ([PERSON] et al., 2019). Moreover, water also acts as a hydrogen donor which helps plastic cracking and gasification. For these reasons, this technology can be used for various types of plastic material, for example cross-linked polyethylene, plastic with flame retardants or contaminants. Figure 4 shows a graphic scheme of the SCWG of waste plastics.  
An interesting application is that related to the removal of microplastics from the marine environment. Other types of processes such as pyrolysis and gasification are not a suitable choice because, the high water content of plastics recovered from the ocean would require a drying step, which would significantly increase the cost of recycling [103]. At present, there are few studies on the characteristics of plastic supercritical water gasification and how plastics react with supercritical water. A very active research group on this technology is that of [PERSON], at Xi'an Jiaotong University (China). This group between 2018 and 2020 had published five articles on this topic. These and other articles will be described in Section 6.
# Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography +
## 6 Discussion
Future work should consider the limitations of the proposed approach for cavity detection. To inspect a culvert in a real setup using the proposed approach, the IR camera and halogen lamps should be placed inside the pipe. For large-span pipes (large than 2 m), this can be done in different ways, but for smaller pipes, we recommend using a small robot to navigate through the pipe while the camera and lamps are installed on it. Considering the size of the camera and lamps, and the required distance between the camera and the interior surface of the camera, the current version of the proposed approach is not suitable for pipes with a diameter smaller than 1.2 m. However, by deploying modern cameras (e.g., FLIR Boson) which are available in very small sizes, inspecting smaller pipes is practical; the size of a FLIR Boson without a lens is only 21 \(\times\) 21 \(\times\) 11 mm\({}^{3}\). Metallic materials generally have very low emissivity, thereby reflecting thermal waves strongly. Generally speaking, IRT is not appropriate for inspection of low emissivity materials including metals, unless surface coating is allowed [12; 15]. In our laboratory tests, we had to paint the specimen to deal with a very shiny surface of the specimen as it was a cut from a brand-new pipe. However, this problem will be of less importance as inspection is often carried out for old pipes whose surfaces become unpolished due to erosion, accumulation of dirt, and other factors. In the worst-case scenario, the low emissivity can be addressed by painting. In most experiments, the cavities were directly in contact with the specimen. In experiment 3, one of cavities was placed at a distance of 1 cm from the specimen and the gap was filled with the soil. Detecting this cavity was noticeably more difficult than detecting the other cavities, and we expressed our concern about the reliability of this approach for detecting deep cavities. This is consistent with the fact that the main limitation of IRT is the penetration depth. Therefore, we suggest future research to measure the maximum depth in which the detection is possible. Moreover, infrared waves cannot penetrate through water and therefore the surface areas of the culvert that are under water cannot be investigated.
## 4 The Compressive Acquisition Model for Gas Data Flow  
Compressive-sensing-based signal compression, acquisition and recovery has the following characteristics.  
1. Encoding end is simple: only a measurement matrix that follows random distribution and satisfies incoherence is required and codes are non-adaptive.
2. Encoding and decoding are unequal to each other: at the encoding end, the computation is simple, indicating less energy consumption and uniqueness; at the decoding end, the computation is quite complex, and many reconstruction algorithms can be selected.
3. Compressed sensing is characterized by robustness and noise immunity. Since the measurement matrix has allotted high-dimensional signal information to each measured value, each measurement coefficient indicates the same importance or unimportance and the errors caused by the loss of several measurement coefficients are the same.  
Therefore, compressed sensing is particularly applicable to signal processing systems that require low-energy-consumption sensor encoding but have high computing capability at the decoding end. The gas data detected by a mobile gas detection instrument were transmitted to wireless access points via a wireless network; when multiple mobile gas detection instruments moved underground, the mobile gas detection instruments near a wireless access point constituted a wireless sensing network. The mobile gas detection instruments were poor in data processing, but the principal computer exhibited extremely strong signal processing capability; therefore, the mobile gas detection instruments constituted a system that is exactly suitable for the compressed sensing theory.
# Assessing the Role of Program Suspend Operation in 3D NAND Flash Based Solid State Drives
## 5 SSD Simulations to Understand the Role of Program Suspend  
We performed a set of SSD simulations using the SSDExplorer platform to understand the impact at the system level of the program suspend operation in 3D NAND Flash memories by back annotating all the electrical characterization metrics presented so far into the simulation environment. As we cannot disclose the full details of the operation timings featured by the tested 3D NAND Flash product, we assume the generic values for the program, read, and erase operations as defined in Table 2, which were extracted from literature referring to a similar TLC 3D NAND Flash technology [9].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(t_{PGM}\) & 2 ms \\ \(t_{READ}\) & 80 μs \\ \(t_{ERS}\) & 5 ms \\ \(VDD_{core}\) & [2.7–3.6] V \\ \(VDD_{Q}\) & [1.2–1.8] V \\ \hline \hline \end{tabular}
\end{table}
Table 2: The 3D NAND Flash operation timings and voltage intervals considered in the simulations.  
Figure 9: (**a**) The RBER measured in all the wordlines of a block where the program suspension took place. (**b**) The same measurements performed in a block where no program operation is issued.  
The specific values of \(t_{EPS}\) and \(t_{PGM,s}\) shown during the characterization results are taken into account since they are expressed as in terms of relative deviation with respect to \(t_{PGM}\), therefore, being applicable in this study case. The workload used as the input stimulus for the SSD to extract all the drive's performance metrics is either a 75% write \(-25\%\) read or a 25% write \(-75\%\) read according to the evaluation guidelines for suspension operations provided in [9].  
In all the simulations performed, we compared three different use cases for the SSD: (i) the case in which the 3D NAND Flash memories integrated do not implement the program suspend functionality; (ii) the case where the program suspend is enabled and allows for a maximum of five suspensions to preempt an incoming read operation; and (iii) the same as in the previous case, but allowing up to 10 suspensions.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
### Related Work
[12] employed a deep learning approach with a deep convolutional encoder-decoder model based on SegNet (PPNet) to predict the path loss (PL) in wireless communication. The model was trained on outdoor propagation data to generate a path loss heatmap, which could predict the distribution of the path loss. Furthermore, the model effectively generalized to previously unseen outdoor environments. [PERSON] et al. [13] introduced a 5G maritime UAV (Unmanned Aerial Vehicle) air-to-ground link channel model based on LSTM-DCGAN, using millimeter-wave (mmWave) technology to obtain the channel state information. This model leveraged LSTM and a Distributed Conditional Generative Adversarial Network (DCGAN) to enhance the channel estimation and training. Additionally, ref. [14] proposed a machine-learning-based interference suppression relay selection technique (ITRS), which combined DF coding and network coding to optimize the relay selection, reduce interference, and improve the reliability. In [15], UAV relay strategies were investigated by employing two adversarial neural network structures, further enhancing the communication performance. [PERSON] et al. [16] proposed a deep learning solution that leveraged channel information and cruise data to predict the link availability, highlighting the advantages of deep learning for link prediction.
## 2 Results and Discussion
### The Residue CH\({}_{2}\)Cl\({}_{2}\) Solution Chemical Composition  
Results of GC and GC-MS analyses of the residue CH\({}_{2}\)Cl\({}_{2}\) solution reveals the high-molecular weight diterpenes methyl dehydrodietate (33.6%) and retene (24.1%) as the major constituents, as seen in other ancient organic residues [2,3]. A representative chromatogram is presented in Figure 3.  
Other abundant compounds were 1,4-dimethoxyphenanthrene (6.8%) as well as naphthalene and phenanthrene derivatives [7-(1-methylethyl)-1,4a-dimethyl-1,2,3,4,4a,9,10,10a-octa-hydronaphthalene (2.8%), 7-(1-methylethyl)-1,4a-dimethyl-2,3,4,4a,9,10-hexahdophenanthrene (1.9%), 7-(1-methylethyl)-1,4a-dimethyl-1,2,3,4,4a,9,10,10a-octahydrophenanthrene (3.6%), 3,6-dimethylphenanthrene (2.9%) and 2,3,5-trimethylphenanthrene (3.2%)].  
Abietic acid is the main component in resins of _Pinaceae_ origin [5]. During the heating process abiotic acid (AA) can be converted into dehydrodietic acid (DHA) through dehydrogenation [20]. A series of other transformations can produce many other intermediate organic compounds when the  
Figure 3: Total ion chromatogram of the organic residue solution in CH\({}_{2}\)Cl\({}_{2}\) on HP-5 MS column. Numbers refer to Table 1.  
thermal treatment is maintained and is more intense (Scheme 1). Although the main reaction is dehydrogenation of AA to DHA, decarboxylation of DHA produces dehydrodietin. Increasing aromatization of dehydrodietin as well as decarboxylation of DHA generates norabietamene. Norabietamene is further dehydrogenated to tetrahydrororetene and retene (formed predominantly when the process is carried out at high temperatures [20]). Further retene dealkylation produces phenathrene.  
**Scheme 1.** Transformation reactions of abietic acid.  
Methyl DHA is formed when the resin is heated in the presence of wood because CH\({}_{3}\)OH released when wood is heated to high temperatures reacts easily with DHA, which is absent when the sealing material is produced by pyrolysis of the resin alone [2,20]. The simultaneous presence of retene and methyl dehydrodietate highlights that the resin was heated in the presence of wood obtained from plants of the _Pinaceae_ family [2,20].  
In general, oxygenated products (such as 7-oxo-DHA or 7-oxo-15-hydroxy-DHA) are the consequence of an aging process due to contact with the atmosphere. These compounds were abundant in other ancient organic residues that were GC analyzed after derivatization [3,21]. Direct GC analysis of the sample is limiting for determination of such high-molecular weight oxygenated products. However, the organic residue was preserved under the sea for a long period and therefore oxygenation reactions were probably not promoted.