esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Subcooled Flow Boiling Heat Transfer with High Porosity Sintered Fiber Metal
## 2 Experimental Apparatus and Procedure
### Test Section and Measurement of Thermal Physical Properties  
A uniform temperature field was created for the working fluid when it passed through the aluminum rectifier installed in the inlet part of the test section and then it flowed into the heating section as shown in Figure 2a. The pressure and temperature were measured in the inlet and outlet parts of the heating section. The physical properties of the working fluid were calculated using the software Refprop ver. 9.1 [28]. The channel was a square of 10 mm \(\times\) 10 mm and the length of the heating section was 100 mm. As shown in Figure 2b, the heating surface was located on the lower part of the flow channel and covered by the polycarbonate to avoid heat loss.  
Figure 3a shows a top view of copper block A, which had a heat transfer surface of 10 mm \(\times\) 100 mm. Ten thermocouples were divided into five groups of two thermocouples, and each group was inserted into copper block A. The five groups were located at (10, 30, 50, 70, and 90 mm) in the flow direction from the inlet part of the heating surface, respectively. Figure 3b shows the distribution of each pair of thermocouples; the distance between them was 9 mm, and the distances to the heating surface were 1 and 10 mm, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Mass Flux & \(G\) [kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)] & 150, 300, 600 \\ \hline Inlet subcooled temperature & \(\Delta T_{\text{sub}}\) [K] & 30, 50, 70 \\ \hline Porous thickness & \(H_{p}\) [mm] & 0, 0.5, 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental condition.  
Figure 1: Schematic of the experimental apparatus. (**a**) Schematic of experimental apparatus; (**b**) assembly of heating section.  
The heat flux at each location was calculated using [PERSON]'s law Equation (1). The average of each measurement point was taken as the total amount of heat flux.  
\[q=-\lambda_{\text{c}}\frac{dT}{dx} \tag{1}\]
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 2 Background
### Defence of Identity Privacy
#### 2.4.4 Ephemeral NIDs for ILNP  
Under ILNP, NIDs take the place of IPv6 IIDs, and, by default, are reused in different flows as in SLAAC. However, ILNP's addressing architecture make it possible to use _ephemeral_ NIDs (eNIDs) for a single transport layer flow, after which an eNID value can be discarded. Like temporary addresses in TA-SLAAC, this prevents an attacker correlating different communication flows based on a common address value. However, in TA-SLAAC, the timeframe of validity of the IID value is an administrative control that is _independent_ of the timeframe of a flow duration, and so IID values are reused across multiple flows. In contrast, ILNP eNIDs are part of the local transport layer operation, so:  
* they provide stronger support for both spatial flow privacy and scope of trust, as eNID values are generated _locally_, at the source node, as pseudo-random values;
* they provide stronger support for temporal flow privacy because eNID values are _never_ reused, even by simultaneous flows;
* their use confines the scope of trust, as the transport protocol state information is maintained at the source of the flow, so the communication does not rely on additional entities, e.g., as in CGNAT, which might be subject to address reconnaissance attacks [23].  
Previous work on _emulated_ networks has shown that eNIDs should provide better protection against correlation attacks than TA-SLAAC [24]. eNIDs have since been implemented as part of the ILNP support in FreeBSD14 [17], and this paper shows their behaviour and evaluation in two real network scenarios: a local IPv6 testbed; and the global IPv6 Internet.
## 4 Delay and Energy Consumption Evaluation  
In this section, we verify the models presented in Section 3 using computer-based simulations. In particular, we evaluate how the delay and the energy consumption are influenced by the number \(n\) of end-devices in the network. For FSA-ACK and FSA-FBP we take into account the number \(m\) of slots per frame, whereas for DFSA we consider the value of \(\rho\). Based on the results, we discuss how to minimize the delay and energy consumption depending on the protocol variant.
## 2 Experimental Apparatus and Parameters  
In order to carry out the vibration and diagnosis analysis of the rotor system, a dual-rotor test rig with sliding bearings is built (as shown in Figure 1) to investigate the dynamic response. The rotor test bench was built by us after several strength check experiments and critical speed calculations, using materials that are more widely used in industry. The basic parameters of the test bench and the stability of the operation in the experiment have been gradually improved through adjustments. The test rig includes a pedestal, two bearing seats, a motor connected with a shaft, two disks, and an inflow and outflow pipe filled with oil. The length and diameter of the shaft in the rotor system are 0.7 m and 0.02 m. The diameters of the disks are both 0.14 m with a thickness of 0.025 m, and a single disk weighs 3 kg.  
The rotating speed of the rotor is adjusted through a control cabinet shown in Figure 2, and the connection between the output shaft of the motor and the rotating shaft is implemented by a diaphragm coupler that also provides power to the experimental rotor system. A set of four WT0150 series eddy current sensors is used to capture and feedback the vibration signal from the disk and the shaft in the vicinity of the fault bearing. The sensors spaced by 90\({}^{\circ}\) are placed along the X and Y directions, respectively, for the disk and shaft, see Figure 1. In addition, an acceleration sensor installed at the sliding bearing is used to record the rotating speeds of the experimental system, and a photoelectric sensor with a horizontal arrangement is used to record the signals of rotating speed and its phase at the coupler. During the experimental campaign, the variation ranges of the motor rotating speed are from 300 rpm to 6500 rpm, taking into account the experimental design and system stability. The displacement signal caused by system vibration is logged by the HD2000 data acquisition system, sampled at 1024 Hz. At each rotational speed, the sampling duration of the experimental data under investigation is 30 s. The data acquisition system and the sampling interface are shown in Figure 3.  
Figure 1: The dual-rotor sliding bearing system test rig.  
The oil supply system is shown in Figures 4 and 5 shows the physical diagram of the oil tank and oil pressure gauge. (1) 0.18-0.55 K\(\Omega\), rated speed 157 rad/s; (2) oil pressure max. 60 bar (6 MPa); (3) oil pipe size inner diameter 6 mm, material stainless steel; (4) oil tank volume is 20 L (with air outlet).  
Figure 4: Oil supply device.  
Figure 3: Data acquisition system.  
Figure 2: Control cabinet.
# Improving Transaction Speed and Scalability of Blockchain Systems via Parallel Proof of Work +
## 1 Introduction
### Proof of Work  
The algorithm that is used to confirm the transaction and add new blocks to the chain is called Proof of Work [7]. With Proof of Work, miners go up against each other to finish exchanges on the system and be compensated. A decentralized ledger accumulates every one of the exchanges into blocks. A block is added to the blockchain when any miner solves the hash for that block. The goal of Proof of Work is to find a possible solution for a complicated mathematical puzzle. The puzzle consists of many elements such as puzzle protocol and hash function. The complexity of the puzzle increases with the growth of the network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Cryptocurrency**} & \multirow{2}{*}{**Transactions per Second**} & \multicolumn{2}{c}{**Average Transaction**} \\  & & **Confirmation Time** \\ \hline Bitcoin & 3–7 & 60 min \\ \hline Ethereum & 15-25 & 6 min \\ \hline Ripple & 1500 & 4 s \\ \hline Bitcoin Cash & 61 & 60 min \\ \hline Stellar & 1000 & 2–5 s \\ \hline Litecoin & 56 & 30 min \\ \hline Monero & 4 & 30 min \\ \hline IOTA & 1500 & 2 min \\ \hline Dash & 10-28 & 15 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transaction speed of various cryptocurrencies.  
For different types of cryptocurrency, different types of techniques are used as proof of work. For example, Bitcoin uses the SHA-256 cryptography technique [8]. Litecoin also follows a similar type of system, known as scrypt [8] while Ethereum uses the Ethash algorithm [8]. Elements such as transaction time, complexity, and hash power differ in different cryptocurrencies due to dissimilar algorithms.
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
###### Abstract  
Rotor fault diagnosis has attracted much attention due to its difficulties such as non-stationarity of fault signals, difficulty in fault feature extraction and low diagnostic accuracy of small samples. In order to extract fault feature information of rotors more effectively and to improve fault diagnosis precision, this paper proposed a fault diagnosis method based on variational mode decomposition (VMD) symmetrical polar image and fuzzy neural network. Firstly, the original rotor vibration signal is decomposed by using the VMD method and the relevant parameter selection algorithm of the VMD method is also proposed. Secondly, the intrinsic mode functions (IMF), which are sensitive to the signal characteristics, are selected for signal reconstruction based on a comprehensive evaluation factor method. As well, the reconstructed signal is transformed into a two-dimensional snowflake image through using the symmetrical polar coordinate method. Finally, the image features are extracted by the gray level co-occurrence matrix to form the state feature vector, which is input into the fuzzy neural network to realize the rotor fault diagnosis. Through the analysis of measured signals, the experimental results show that the proposed method can reach a higher recognition rate of 98% and the k-cross-validation experiment is used to demonstrate the robustness of the fuzzy neural network, and the average recognition accuracy of this experiment is 99.2%. Compared with some similar methods, the proposed method still has the highest fault recognition precision 98.4%, and the smallest standard deviation 0.5477.  
v 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
Mechanical Engineering College, Beihua University, Jilin 132021, China  
*Correspondence: [EMAIL_ADDRESS] (L.C.); yzl19910/11@126.com (Z.Y.); Tel.: +86-137-5084-6426 (L.C.); +86-176-0074-0711 (Z.Y)  
v 2023  
variational modal decomposition; symmetrical polar image; fuzzy neural network; rotor; fault diagnosis
# Robust Optimization of Transport Organization for China Railway Express
## 3 Optimizing China-Europe Freight Train Logistics Amidst Uncertain Transportation Demand
### Determine the Transportation Organization Optimization Model in the Environment  
The objective function, as depicted in Formula (1), aims to minimize both the train operating cost and container operating cost within the China-Europe freight train transportation organization.  
\[\text{min}F=\sum_{(i,j)\in A}\sum_{v\in V}\left[f_{ij}^{v}\cdot\left(C_{v}+C_{ ijv}\cdot d_{ij}\right)\right]+\sum_{i\in N_{3}}\left[z_{i}\cdot Q_{i}\cdot C_{i}^{3}\right] \tag{1}\]  
The schema constraints are as follows:  
s.t.  
\[x_{ij}^{v}\leq Q_{v}\cdot f_{ij}^{v}\cdot y_{ij}\quad\forall(i,j)\in A,v\in V \tag{2}\]  
\[\sum_{j\in N_{2}\cup N_{3}}\sum_{v\in V}f_{ij}^{v}\geq 1\quad\forall i\in N_{1} \tag{3}\]\[f^{p}_{ij}=0\quad\forall i\in N_{2},j\in N\;\text{or}\;\forall i\in N,j\in N_{2},v \in V_{1} \tag{4}\]  
\[f^{p}_{ij}=0\quad\forall i\in N_{1}\cup N_{3}\;\text{and}\;\forall j\in N_{1} \cup N_{3},v\in V_{2} \tag{5}\]  
\[f^{p}_{ij}\in Z^{+}\cup\{0\}\quad\forall(i,j)\in A,v\in V \tag{6}\]  
\[x^{p}_{ij}\geq 0\quad\forall(i,j)\in A,v\in V \tag{7}\]  
\[y_{ij}\in\{0,1\}\quad\forall(i,j)\in A \tag{8}\]  
\[z_{i}\in\{0,1\}\quad\forall i\in N_{3} \tag{9}\]  
Formula (2) represents the constraint related to train transportation capacity;  
Formula (3) pertains to network site constraints;  
Formulas (4) and (5) serve as constraints for the train's operational route;  
Formulas (6) to (9) establish constraints for the decision variables.
# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks
## 4 Methodology
### Phase 1: Collection of Parallel Texts
#### 4.1.3 Digitization and Manual Correction  
Digitization was performed only for the physical books. We scanned each page of the books and saved them in document image format. Then we use the Abbyy FineReader software ([[https://finnereader.en.softonic.com/](https://finnereader.en.softonic.com/)]([https://finnereader.en.softonic.com/](https://finnereader.en.softonic.com/))) to convert the image documents into plain text. The document image reading in Abbyy FineReader was performed in Spanish, as the software does not include Mixtec in its language catalog. During the document reading, two main issues were identified: first, unreadable words in the Mixtec language, and second, confusion in selecting texts adjacent to images. The strategies implemented in text correction are detailed below.  
* Identify and Modify Unreadable Words in Mixtec: During the document image reading, we encountered Mixtec words that were unreadable. In these cases, we manually modified the text to the correct word in Mixtec. Additionally, we added the characters identified as causing difficulties in reading the document image to Abbyy FineReader's alphabet. Then, we re-read the document with the added characters, allowing us to correct these issues. Below is the list of characters added to the Abbyy FineReader dictionary: \(\underline{A},\underline{a},\underline{E},\underline{e},\underline{L}, \underline{L},\underline{O},\underline{o},\underline{U},\underline{u}, \underline{A},\underline{E},\underline{e},\underline{I},\underline{i}, \underline{O},\underline{o},\underline{U},\underline{a}\). Table 5 gives the words that Abbyy FineReader could not recognize and their corrected forms.
* Confusion in Selecting Texts Adjacent to Images: Sometimes, during the digitization of documents, the software selected both the image and the text, making it difficult to separate the text from the image. In these cases, the solution was to move the selection area over the text and then remove the image from the text.  
Figure 4: Synthetic data generation architecture. Input is marked in green, the translation phases with GPT-3.5 are in blue, and the final output is indicated in gold.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Words** & **Corrected Words** \\ \hline vaxi & vaxi \\ kpo & kpo \\ hava & tava \\ kue’e & kue’e \\ Ihjin & tasin \\ \({}^{y7}\) yó & ta’ya yó \\ \hline \hline \end{tabular}
\end{table}
Table 5: Words not recognized by Abbyy FineReader in Mixtec language.
## 3 A Service-Less Multicast Architecture for Distance Learning
### VNF Multicast Architecture  
The eMBMS architecture for multicast in 5G is service oriented, resulting in unicast transmission of live video streams. Virtualization in the cellular networks brings forward an opportunity for application of multiple management functions. A VNF can perform functions such as firewalls and IPS. A VNF overlay module at 5G core is proposed to enable on-the-fly service-less multicast initiation [26]. A VNF-Application Server (VNF-AS) works as a proxy entity for both content provider and user device while residing at the edge of the network. The VNF-AS takes as input service ID, multicast IP address, and session time among other parameters provided by the UE and the multicast service provider from within an edge network. The VNF-AS monitors requests for live videos from the perspective of an edge network. If multiple requests are generated from the same cell within the edge  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref.** & **Objective** &
\begin{tabular}{l} **5G Architecture** \\ **Changes** \\ \end{tabular} & **ML** \\ \hline
[21] &
\begin{tabular}{l} Two layer Non-Orthogonal Multiplexing \\ (NOM) to deliver multicast \\ \end{tabular} & Yes & No \\ \hline
[22] &
\begin{tabular}{l} Service-based method for \\ broadcast/multicast \\ \end{tabular} & Yes & No \\ \hline
[23] &
\begin{tabular}{l} offloads video content from cellular network \\ to dense D2D 5G networks considering the \\ physical and social attributes \\ \end{tabular} & No & No \\ \hline
[4] & VNF-based scheme to enable multicast & No & No \\ \hline
[15] &
\begin{tabular}{l} CNN for popularity prediction and \\ edge caching \\ \end{tabular} & NA & Yes \\ \hline
[25] & \
\begin{tabular}{l} SVM for popularity prediction and \\ edge caching \\ \end{tabular} & NA & Yes \\ \hline This article &
\begin{tabular}{l} three-dimensional solution for EI enabled \\ multicast and caching \\ \end{tabular} & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing works.  
network, VNF-AS enables multicast on-the-fly. The VNF-AS forwards these parameters to the eMBMS architecture to initiate multicast service. The UE and service provider are not inherently multicast and the VNF-AS acts as a proxy to enable multicast in 5G eMBMS architecture. Request monitoring and multicast session initiation, content provider proxy, and user equipment proxy constitute the basic modules of the VNF-AS. The VNF module sends less content requests due to its multicast nature while lowering the traffic burden from the network.
## 2 Materials and Methods
### In Vitro Biofilm Investigations  
An investigation was performed in nutritionally rich Tryptone soya broth (Oxoid, CM0129B, Basingstoke, United Kingdom) and in nutritionally poor M9 and ABTG minimal salts. A minimal M9 medium was made of a commercially available product (M9 minimal salts 5\(\times\), Difco, Becton Dickinson and Company, Le Pont de Claix, France, consisted of Na\({}_{2}\)HPO\({}_{4}\) 3.39%, KH\({}_{2}\)PO\({}_{4}\) 1.5%, NaCl 0.25%, NH\({}_{4}\)Cl 0.5%), with the subsequent addition of (in final concentrations) 0.4% glucose, 0.004 M MgSO\({}_{4}\), and 0.00001 M CaCl\({}_{2}\) (all provided by Supelco, Merck KGaA, Darmstadt, Germany). A minimal ABTG medium was made as previously described [32], and it consisted of 1 mM MgCl\({}_{2}\), 0.1 mM CaCl\({}_{2}\), 15.1 mM (NH\({}_{4}\))SO\({}_{4}\), 22 mM KH\({}_{2}\)PO\({}_{4}\), 0.5% glucose, (all provided by Supelco, Merck KGaA, Darmstadt, Germany), 33.7 mM Na\({}_{2}\)HPO\({}_{4}\) \(\times\) 2H\({}_{2}\)O, 51 mM NaCl, 0.01 mM FeCl\({}_{3}\), (all provided by Chempur, Piekary Slaskie, Poland), and 2.5 \(\upmu\)g/mL Thymain-Hydrochlorid (purity >98.5% [PERSON], Karlsruhe, Germany).  
The strains were grown on the Columbia 5% sheep blood agar (Thermo Fisher Scientific, Waltham, MA, USA) for 24 h at 25 \({}^{\circ}\)C. Bacterial inoculums were prepared by suspending each strain in the appropriate medium to an optical density (OD) of OD\({}_{600}\) 0.08-0.1 (approx. 1 \(\times\) 10\({}^{8}\) CFU/mL), after which it was diluted 1:10, and 100 \(\upmu\)L of the prepared dilutions (1 \(\times\) 10\({}^{7}\) CFU/mL) were inoculated into microtiter plates with 96 flat-bottom wells (neoCulture Cell and Tissue Culture Plates W96, neoLab Migge GmbH, Heidelberg, Germany). Each isolate was inoculated in twelve replicates. The incubation lasted 7 days in Binder climatic chambers (KBF-LQC 240, Binder, Tuttlingen, Germany) under controlled atmospheric conditions at 8 \({}^{\circ}\)C, 16 \({}^{\circ}\)C, and 25 \({}^{\circ}\)C and at 80% humidity, and it was performed in two independent experiments.  
_Aeromonas salmonicida_ ATCC 7965 (Microbiologics, St. Cloud, MN, USA) was used as a comparative control in both applied methods.
## 3 Feature Selection and Methodology
### Performance Metrics  
The performance metrics are common to use in model evaluation to evaluate the performance of each prediction model, and it is calculated by confusion matrix value: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). TP and TN are the cases where the actual value is correctly predicted. FP and FN are observed when the case predicted the actual value was incorrect. The equations for the metrics of accuracy, kappa, sensitivity, specificity, and FI score are defined in Equations (3)-(7), respectively:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}*100 \tag{3}\]  
\[\text{y}=\frac{\left[\left(\text{TP}+\text{FP}\right)\times\left(\frac{\text {TP}}{\text{Accuracy}}\right)\right]+\left[\left(\text{FN}+\text{TN}\right) \times\left(\text{Accuracy}\right)\right]}{\text{Accuracy}} \tag{4}\]  
\[\text{Kappa}=\frac{\left(\text{y}-\text{Accuracy}\right)}{\left(100\%- \text{y}\right)}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
\[\text{Specificity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
\[\text{F1}-\text{score}=2\times\frac{\text{Sensitivity}\,\times\,\text{Specificity }}{\text{Sensitivity}+\,\,\text{Specificity}} \tag{7}\]  
[PERSON] et al. categorized AUC values as follows: AUC \(\geq\) 0.9, outstanding discrimination; 0.8 \(\leq\) AUC < 0.9, good discrimination; 0.7 \(\leq\) AUC < 0.8, acceptable/fair discrimination; 0.6 \(\leq\) AUC < 0.7, poor discrimination; and AUC < 0.6, no discrimination [46]. Higher accuracy, sensitivity, specificity, and kappa are better.
## 3 Physics Layer  
To facilitate on-board application, careful consideration needs to be taken regarding reliability and low power consumption. In our design, each node consists of one control processor and two RF circuits (RFICs). As a mature commercial circuit, an 8051 series microprocessor from Cygan (Austin, TX, USA) is adopted as the control processor. Another chip from Texas Instruments (TI) (Dallas, TX, USA)  
Figure 1: The structure of the ISWN.  
Figure 2: Model of the protocol.  
is chosen as the data transceiver, which supports three types of modulation, FSK, MSK and GFSK. The chip supports configurable Clear Channel Assessment-Energy Detect (CCA-ED). This provides the possibility of using contention-based MAC protocol design. In addition, the microprocessor has been verified on-orbit by the Chinese satellite Shiyan-3 and functioned well [35]. The structure of the wireless node is illustrated in Figure 3 below.  
In Figure 3, each node consists of one control processer and two RF circuits. A physical address (hardware address) is used as the identification marker of each RF component. By setting the physical address, each node supports full-duplex communication, in which one RFIC is used as transmitter while the other as a receiver. In the case of function failure of any RFIC, the surviving RF chip can work alternatively of transmitter and receiver. Working in the 2.4-2.5 GHz ISM band, the chip's data rate is 500 Kbps. The main parameters of the wireless node are given in Table 1.