esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Austenitizing on the Microstructure and Mechanical Properties of Gray Cast Iron
## 2 Experimental  
In this experiment, gray cast iron was used as the raw material, with the main chemical compositions (wt, %) as follows: 3.0 C, 2.2 Si, 1.0 Mn, P \(\leq\) 0.1, and S \(\leq\) 0.1. The specimens were sectioned into 10 \(\times\) 10 \(\times\) 10 mm metallographic samples, wear test specimens, and tensile test pieces using wire electrical discharge machining; the tensile specimens and the specimens for friction and wear tests are shown in Figure 1. This was followed by the heat treatment of the gray iron samples. The critical temperatures for heat treatment were determined according to empirical formulas [27; 28]. The specimens were austenitized at 810 \({}^{\circ}\)C, 850 \({}^{\circ}\)C, and 900 \({}^{\circ}\)C for 1 h in a vacuum tube furnace. After heating, the gray iron parts were subjected to austempering in a molten salt solution composed of 50% KNO\({}_{3}\) and 50% NaNO\({}_{2}\) at 320 \({}^{\circ}\)C (this nitrate salt bath has good fluidity at 320 \({}^{\circ}\)C, which can uniformly envelop the gray iron parts; it has a low vapor pressure and weak volatility, ensuring stable maintenance of its composition; it has a high specific heat capacity, enabling efficient heat transfer and storage to guarantee a stable quenching temperature; moreover, it has a certain degree of oxidizing ability, which may cause a thin oxide film to form on the surface of the gray iron parts), before being kept at a constant temperature for 1 h. The process flow chart is shown in Figure 1. The as-cast specimen and the specimens with different austenitizing temperatures were named Ht zt, At 810, At 850, and At 900, respectively. The obtained metallographic specimens were ground, polished, and etched. The etching solution was a nitric acid-alcohol solution with a volume fraction of 4%. The content of the microstructure was measured using Image-Pro Plus software (NIH, Bethesda, MD, USA). The microstructure was observed and analyzed using a GeminiSEM300 field emission scanning electron microscope (ZEISS, Oberkochen, Germany). Tensile tests were carried out using an E45--305 microcomputer-controlled electronic universal testing machine (MTS, Norwood, MA, USA) at room temperature with a crosshead speed of 1 mm/min. Five replicate experiments were conducted, and the average values of tensile strength, yield strength, and elongation were processed. Hardness tests were performed using a UH250 fully automatic universal hardness tester (Wilson, NC, USA) with a load of 3000 g, an indentation spacing of 2 mm, and 5 indentations. The friction performance of the AGI test pieces was tested using an MZF--1 rotary reciprocating friction and wear tester (Baohang, China). A WC ball with a hardness value of 1400 HV was selected for the friction experiment. The friction diameter was 10 mm, the reciprocating frequency was 5 Hz, the test force was 20 N, and the test time was 60 min. Each experiment was repeated five times.
# Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals
## 2 Materials and Methods  
### Experimental Data  
Sleep-EDF Dataset  
The dataset is open-source, and many previous researchers have utilized this dataset in sleep scoring research [17; 18; 19; 20; 21; 22]. Among three available versions of the dataset, we used an expanded version containing 61 recordings from 42 Caucasian male and female subjects. The subjects' ages ranged from 18 to 79 years. This dataset was organized into two sub-sets. The first subset with 39 recordings  
Figure 2: The flowchart of the proposed research.  
from 20 subjects was EEG data recorded in a study from 1987 to 1991. These subjects were healthy and in ambulatory condition. The second subset with 22 recordings from 22 subjects was EEG data recorded in a study in 1994 and the subjects reported slight difficulty in falling asleep but were otherwise healthy. The EEG data had been collected over 24 h of the daily lives of the subjects. A miniature telemetry system recorded nocturnal EEG data from four subjects in a hospital [19]. The data was collected from just two channels: Fpz-Cz and Pz-Oz, at a sampling frequency of 100 Hz. The previous researchers had established that on single-channel analysis the Pz-Oz channel demonstrated improved performance over the Fpz-Cz channel. Using R & K criteria, EEG recordings of both of the subsets had been annotated by an experienced sleep technician on a 30 s basis. Therefore, the duration of each epoch is established as 30 s and yielded 3000 samples. The epochs had been annotated by sleep technicians as: AWA, REM, S1, S2, S3, S4, \"Movement Time\" or \"Unsocred.\" On the other hand, the annotations using AASM criteria consisted of the designations AWA, REM, N1, N2, N3, and \"Unknown sleep stage.\" The number of samples according to R & K criteria are shown in Table 2. After removing \"Movement Time\" and \"Unsocred,\" total number of the samples is 127,663. The epoch duration was 30 s.  
As far as features analyzed, we prepared 6000 features extracted from two channels (Pz_Oz and Fpz_Cz). For each channel, 1000 features were extracted at a sampling frequency of 100 Hz and epoch lasted 30 s. The features were used in the experiments separately or in combination.
## 3 Proposed Methods
### Protocol Description
When authentication is not passed, then it withdraws. When the ID of the earlier cluster head integrated by REPR is not the present cluster head's ID, then it withdraws. Otherwise, the routing table is updated. At that point, when the REPR is initially from the receiver node, the cluster head transmits the REPR as  
B \(\rightarrow\) A: IDB1! IDA! IDB! IDB2! IDR! ID receiver! IDREQR! Seq! N! MAC(KAB! IDR! ID receiver! IDREQR! Seq! Ng! Ng! B2! ID! IDB! IDA)! MAC(KS, IDR! ID receiver! IDREQR! Seq! Ng)  
where B1 ID and B2 ID denote the ID of the previous node B1 and next node B2, the adjacent group leaders are indicated as A and B. If REPR originates from the intermediate group leader C, then REPR is sent as follows:  
B \(\rightarrow\) A: B1 ID! IDA! IDB! B2 ID! IDC! IDS! IDSink! IDREQR! Seq! N! MAC(KAB,IDC! IDS! IDSink! IDREQR! Seq! N! B2 ID! IDB! IDA)  
where B1 ID and B2 ID denote the ID of the previous node B1 and next node B2, the adjacent group leaders are indicated as A and B and IDC denotes the ID of the node that generates REPR. Acknowledgment of Data: The authentication from the receiver node is transmitted as follows: Receiver \(\rightarrow\) S: IDP! IDR! ID receiver! NR! NRceiver! MAC(KS, IDR! ID receiver! NR! NRceiver)  
The ID of the preceding hop is given by IDP in the route. When the authentication is accepted by the transmitter node, and validation is passed by the MAC, then that route is safer from a black-hole attack. Simply, the receiver node generates the appropriate MAC through the secret key KS. When the validation fails, the transmitter node would be suspicious, regardless of whether the route is secured against a black-hole attack or not. For further confirmation, the transmitter node transmits additional control messages often at the rate of the message as RC' (Rc! Rc'! Rc'! Rd). When the appropriate authentication is not received within the threshold time, the transmission node confirms that the route is not safe. The cluster head that creates REPR must then be added to the black-hole list. The route is protected from black-hole attacks when data authentication is accepted within the threshold time. The SOPR algorithm is described in Algorithm 1, and the routing process is described in Figure 6.  
Figure 6: Flowchart for the routing protocol. ```
\(N\gets Number\:SensorNodes\); \(N\gets n_{1},n_{2},n_{N}\); \(N\in\) IdleMode; whileDiscovery\(\
eq\) 0 do \(ID\leftarrow\) assignedtoPackets; ifBroadCastID\(found\)then
```  
**Incoming Packet Rejected**; elseif\(BroadCastIDnotfound\)then \(N\gets Mmodes\); \(M\in RRange\); \(Selectm_{s}nodes\in MinR\); (\(n_{K}>AverageEnergy\)) Data Successfully propagated; (\(n_{K}<AverageEnergy\)) Repeat with M modes;  end if ifthen\(DataIDfound\) Successful Transmission; else\(DataIDnotfound\) Repeat Discovery; ```  
**Algorithm 1** Algorithm for SOPR.
## 3 TVWS-DTV Coexistence: Field Measurements
### Field Tests Considering Short Distances  
Short-distance tests were conducted within the Inatel campus, implementing links with distances of 15 and 50 m between the interfering signal generation station and the reception setup. It is important to note that, in these field tests, only the 5G-RANGE transceiver employing the GFDM technique was used as the interfering signal.  
The chosen distances between the interfering signal generation station and the mobile receiving unit allowed for achieving PRs representing the operational limits of DTV STBs. This allowed validation of the limit conditions found in laboratory tests. The tests provided  
\begin{table}
\begin{tabular}{c c c} \hline
**Interference on Channels \(n+1\) and \(n-1\)** & **PR (dB) System: 4G** & **PR (dB) System: 5G-RANGE** \\ \hline Non-simultaneous & \(-29.1\) & \(-33.1\) \\ \hline Simultaneous 4G (\(n-1\)) and 5G-RANGE (\(n+1\)) & \(-26.0\) & \(-30.2\) \\ \hline \end{tabular}
\end{table}
Table 3: Tests with simultaneous interferences on channels \(n-1\) and \(n+1\).  
a PR of around \(-30\) dB for interference on channels \(n-1\) and \(n+1\) and a PR of around \(-50\) dB for interference on channels \(n-2\) and \(n+2\).  
Next, more details will be presented regarding the characterization of the test environment for short distances, the interfering signal, and the results obtained.
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Issues on Image-Based SHM
#### 4.4.5 Other Issues  
Poor image quality is prominent in image-based SHM. Capturing pictures of weak quality results in the inaccurate detection of the number of days and total loads that can be applied before delamination [84]. To solve this, image enhancement [84; 85], the process of modifying digital pictures to make them more acceptable for display or further image analysis [86; 87], must be explored to carry out accurate structural behavior analysis [88], cable force monitoring [89], building inspection and monthly check-up [90], and bridge capacity monitoring [91], which are all areas where image-based SHM and other forms of SHM are useful.
## 2 Understanding WebAssembly
### Portability  
Wasm's binary format is designed for efficient execution across various operating systems and instruction set architectures, both within and beyond the web environment. Efficient execution in different environments may require certain characteristics. Even if an execution environment lacks these characteristics, it may be able to execute Wasm modules by emulating the unsupported behaviors, although this could result in suboptimal performance. As the standardization of Wasm progresses, these requirements and adaptations for new platforms will be formalized. The portability of Wasm relies on execution environments that provide the following features:  
* Eight-bit byte representation.
* Byte-level memory addressing.
* Support for unaligned memory accesses or reliable trapping mechanisms to emulate them.
* Support for 32-bit two-complement signed integers, with the option for 64-bit support.
* Compliance with the IEEE 754-2008 standard for 32-bit and 64-bit floating-point numbers, with a few exceptions.  
* Little-endian byte ordering.
* Efficient addressing of memory regions using 32-bit pointers or indices. The wasm64 extension allows for linear memory larger than 4 GiB with 64-bit pointers or indices.
* Secure isolation between Wasm modules and other modules or processes running on the same machine.
* Guarantee of forward progress for all execution threads, even in non-parallel execution scenarios.
* Availability of lock-free atomic memory operators for 8-, 16-, and 32-bit accesses, including an atomic compare-and-exchange operator. The wasm64 extension requires lock-free atomic memory operators for 64-bit accesses.
* Wasm does not define specific APIs or system calls. Instead, it relies on an import mechanism in which the available imports are determined by the host environment. In web environments, functionality is accessed through the Web APIs provided by the web platform. Non-web environments have the flexibility to implement standard Web APIs, standard non-web APIs (such as POSIX), or develop their own custom APIs.
## 4 In-Network Computing via ICN
### Reactive Executor Selection Strategies
#### 4.2.1 Distributed Approaches  
In [65], the authors extend NFN with three resolution strategies for mobile and constrained scenarios, namely EdgeFox, Find-and-Execute (FaX) and Find-or-Pull-and-Execute (FoP)aX. EdgeFox is designed for stationary IoT networks, where devices are typically resource-constrained and data are generated at the network edge. Instead of transferring data to the network core, the target is to look for cached results or deploy computations on the edge node as close as possible to the IoT sources. If no edge node is available, EdgeFox asks executing the computation at the data source, if possible. The FaX strategy is tailored for highly mobile IoT networks: it immediately starts the requested computation in the node closest to the data sources, instead of searching for cached results. If a cached result is found during execution, it is returned to the requester, and the computation is stopped. While this reduces delays, it can lead to inefficiencies, such as redundant computations. Therefore, FaX is suitable for scenarios where timeliness is more critical than computational efficiency. Finally, the (FoP)aX strategy extends FaX by integrating the request-to-computation mechanism to fetch intermediate results from neighboring nodes. The goal is to reduce overall computation time by leveraging partial results already calculated elsewhere in the network. (FoP)aX is particularly useful for long-running computations in mobile scenarios, where nodes may frequently disconnect or switch networks. By fetching intermediate results, it avoids redundant calculations and improves the likelihood of timely result delivery. In [60], the authors propose a distributed strategy to identify the in-network service executor as the available node closest to the data source(s). The consumer initiates the process by transmitting an Interest packet that includes, in its name field, both the name of the data to be processed and the name of the required computing function. Additionally, a new boolean field, called _ExecAvailability_, is added to the PIT entry by intermediate nodes when forwarding the packet. This field is set to 1 if the node has the capability to execute the processing function. The Interest packet is forwarded toward the data source(s) until it reaches the _branching node_, i.e., the last NDN router before the data source(s), which is responsible for data collection. If the branching node possesses the required computing function, it executes the service locally. Otherwise, it sends a NACK back through the network. In this case, the first node with a matching PIT entry and the _ExecAvailability_ field set to 1 takes over the service execution. While this approach can reduce data retrieval latency by selecting a node close to the data source(s), a significant drawback is that the
## 3 The Proposed FMEA Model
### Failure Modes and Effects Analysis  
Failure modes and effects analysis is a systematic method for identifying potential failures in a system, product, or process. It assesses the risks based on three key factors: severity, occurrence, and detection, each of which is rated on a scale of 1 to 10:  
* Severity (S): measures the impact of the failure, with 10 being the highest level.
* Occurrence (O): Indicates how likely it is that the failure will occur, with 10 being very likely.
* Detection (D): Indicates how easily the failure can be detected before it occurs, where 10 means it is least likely to be detected.  
These values are multiplied to calculate the risk priority number (RPN), where a higher RPN means a higher risk:  
\[RPN=S\cdot O\cdot D \tag{1}\]  
In this proposal, the RPN is replaced by a hybrid combination of the Rank Order Centroid (ROC) method and the Combined Compromise Solution (CoCoSo) method, which aim to improve risk assessment by removing the limitations of the RPN, such as the equal weighting of all factors.
# SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition
## 3 Stacking of Distilled Vision Transformers (SDViT)
### Vision Transformer  
The vision transformer (ViT) is a modified version of the original transformer architecture, initially designed for natural language processing (NLP) tasks, adapted to solve vision-based tasks. It operates by dividing input images into patches, which are then linearly embedded and passed through the transformer. This treatment of image patches as tokens resembles the processing of text sequences in NLP applications, where tokens are fed into the transformer to predict class labels for images. The modifications to the original transformer architecture to create ViT were introduced by the Google Research Brain team [37]. The architecture of the ViT model in the proposed SDViT, as depicted in Figure 2, comprises several key components: the linear projection, transformer encoder, multi-head self attention (MSA) layer, and the multi-layer perceptron (MLP) layer. According to the researchers in [37], ViT offers significant improvements in both computational efficiency and accuracy compared with state-of-the-art (SOTA) CNN models. Nonetheless, ViT demonstrates a lesser inclination for translation equivariance and locality in its inductive bias compared with CNN models. This implies that ViT may not generalize well when trained on small datasets, and the authors recommend training ViT on at least 14 million images to mitigate this weakness. To address this limitation, the ViT model implemented in this paper utilizes the ViT model pretrained on the ImageNet21k + ImageNet2012 dataset. The transfer learning of the pretrained ViT model helps to mitigate the weak inductive bias issue associated with the ViT model.  
Figure 1: Network architecture of the proposed SDViT approach.  
Given the hand gesture image, the initial step involves resizing it to a dimension of \(256\times 256\) to ensure that it can be subdivided into patches measuring \(32\times 32\). The resized image is subsequently divided into patches of consistent size, resulting in a series of flattened 2D patches denoted as \(\mathbf{x}_{P}\in\mathbb{R}^{N\times(P^{2}\cdot C)}\). The number of patches \(N\) is determined by the image dimensions and patch size, with \(N=\frac{HW}{P^{2}}\). Here, \(H\) and \(W\) represent the height and width of the image, while \(C\) signifies the channel count and \(P\) denotes the patch size. \(N\) also serves as the effective sequence length applicable to the transformer.
## 3 SNR Evaluation Based on Proposed TP-CNN  
In the section, the proposed SNR evaluation method based on DL is discussed. Firstly, a novel UAV remote control signal dataset is generated. Based on the constructed dataset, a two-path CNN model is proposed to train the dataset next.
## 2 Materials and Methods
### CNCs Characterization
#### 2.3.3 Thermogravimetric and Derivative Thermogravimetry (TGA/DTG) Analysis  
A TGA/DTG (Shimadzu Instruments 50, Caluire-et-Cuire, France) was utilized to investigate the MCC and CNCs samples' thermal stability, weight loss, and decomposition temperature. An amount of 5-7 mg of each sample was put in an alumina cup and annealed at a heating rate of 10 \({}^{\circ}\)C/min between 25 and 600 \({}^{\circ}\)C under a nitrogen atmosphere.
## 3 Results
### Investigation of Fresh Tongue  
The three saildrone vehicle observations were very close to each other while within the fresh tongue (Figure 4). Hence, one of the vehicles (SD1026) was chosen for an analysis of the temperature and ocean conditions at and over the tongue. According to [PERSON] et al. (2016), this fresh tongue originated from a freshwater plume off the coast of South America. They also noted that the fresh tongue was transported to this area by mesoscale eddies as well as Ekman Transport. The saildrone salinity values recorded in this feature, observed in Figure 4, are relatively similar to the SMAP estimates and less saline than HYCOM estimates. These data provide an opportunity to better understand this feature and improve both the satellite and modeled products. Figure 7 compares the time series of the saildrone, SMAP JPL and RSS salinity data as the saildrone crossed the fresh tongue. Figure 7a puts the SSS into focus by comparing the salinity data observed by the saildrone with salinity data from RSS40 and RSS70, JPL and HYCOM. The figure shows that the satellite products were able to resolve the fresh tongue SSS, albeit not with as much detail as the saildrone data. Despite resolving small scale variability, the RSS40 was the only satellite product to capture the observed small increase in salinity at approximately midday on 18 February 2020. As previously stated, HYCOM does not simulate the fresh tongue event.  
Figure 6: (a) Map of RSS70, (b) RSS40, (c) JPL and (d) HYCOM salinity for 8-day average centered on 17 February 2020 shown behind the 1026 saildrone vehicle’s SBE37 salinity data from the 16 February to the 20 February 2020. On 18 February 2020, the saildrone crossed the fresh tongue, the vehicle position indicated by white dot.  
Upon entering the fresh tongue, the SST fell by approximately 0.3 \({}^{\circ}\)C and the SSS also experienced a reduction of approximately 2 psu (Figure 7a,b). This decline in SST and SSS reveals that the saildrone crossed a cold core eddy.