esa-sceva/satcom-chunk-collection huggingface dataset:
# Study the Effect of Operating Temperature on Performance in Dry Scroll Vacuum Pump
## 1 Introduction
However, the detailed flow field and the impact of the structural movement on unsteady flow cannot be accurately predicted by the dimensionless mathematical models. Therefore, further analyzing the thermodynamic characteristics of the dry scroll vacuum pump by CFD method and studying the impact of operating temperature on performance are very necessary for the design and application of the cooling system of the dry scroll vacuum pump. In recent years, the world's energy supply has become increasingly tight, and the energy crisis continues to deepen. Under the influence of extreme weather and other factors, the supply and demand of electricity have become tenser. In order to respond to global climate change, industrial equipment should pay more attention to improving energy efficiency. There is no acknowledged method for assessing the energy efficiency of the dry scroll vacuum pump. Considering the development of energy saving, the energy efficiency assessment system of the scroll pump needs to be defined. This study proposes a way to evaluate the energy efficiency of the dry scroll vacuum pump and introduces the energy efficiency ratio (EER) into the assessment system of scroll vacuum pump performance. As further in-depth research of our previous work, based on the spring smoothing method, the numerical simulation model of the multi-stage dry scroll vacuum pump is established. Under the suction pressure of 1000 Pa, the simulation calculation is carried out at different operating temperatures of fixed scrolls (25 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, and 65 \({}^{\circ}\)C). Moreover, the pumping speed and gas compression power are analyzed. The energy efficiency ratio of the dry scroll vacuum pump is evaluated. This work provides a valuable reference for the design and optimization of the dry scroll vacuum pump.
## 2 Materials and Methods
### Vibration Test Scheme
#### 2.3.2 Test Conditions and Data Acquisition Procedure  
The tests were conducted with the combine harvester stationary and at idle speed to eliminate interference from external dynamic excitations, such as uneven ground and crop load. To accurately analyze the excitation contribution of each working component, the experiment employed a procedure of sequentially activating and deactivating the components. The data acquisition sampling frequency was set to 2 kHz. The specific test procedure was as follows:  
1. Engine-Only Operation Phase: The engine was started and allowed to run stably for 10 s. During this phase, the engine was the primary source of vibration excitation, which was used to capture its fundamental excitation characteristics.
2. Threshing and Cleaning System Operation Phase: After the engine stabilized, the threshing cylinder and vibrating screen were engaged and allowed to run stably  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Component** & **Excitation Frequency (Hz)** \\ \hline
1 & Engine & 29.3 \\
2 & Threshing cylinder & 58 \\
3 & Vibrating screen & 16 \\
4 & Cutting header & 1.27–2.55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Excitation frequencies of combine harvester sources under idle conditions.  
for 10 s. In this phase, the engine, threshing cylinder, and vibrating screen acted as combined excitation sources.
3. Full-Function Operation Phase: Subsequently, the cutting header was activated, causing components such as the cutter, auger, reel, and conveyor trough to begin operation. The system was then allowed to run stably for 10 s. At this point, all core working components (engine, cutting header, threshing cylinder, and vibrating screen) were in an active state of excitation.
4. Sequential Shutdown Phase: After 10 s of stable full-function operation, the cutting header was first disengaged. Following this, the threshing cylinder and vibrating screen were shut down. Finally, the engine was turned off. After each shutdown action, a 10-s waiting period was observed to ensure system stability. By following this procedure, the vibration responses of the chassis frame under various combinations of excitation sources could be obtained, providing rich input data for the OTPA.
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 5 Experimental Results
### Results of Deleting False Negative Samples  
Our experimental results are shown in Table 2. We found that deleting a small number of false negative samples had a positive effect on the model's optimization and improved the accuracy of the model in low-resource speech recognition. However, deleting too many false negative samples had a negative effect on the model's optimization. In the English dataset, deleting two false negative samples resulted in a relative improvement of 2.69% compared to the baseline model. In Mandarin, deleting four false negative samples resulted in a 3.94% improvement relative to the original model. However, if too many false negative samples are deleted, it can lead to the deletion of difficult negative samples, which can negatively affect the model's ability to learn speech representation by reducing its distance from the embedding space for negative samples. We believe that this occurs because negative samples with similar but slightly different pronunciations from \(c_{i}\) are removed, reducing the quality of the negative sample set and affecting the model's learning of difficult negative samples. This, in turn, leads to a slight reduction in the model's ability to learn speech representations. If the speech data contain more but shorter segments, then the number of false negative samples that need to be deleted should be increased accordingly.
## 2 Methods
### Attention Mechanism
#### 2.4.3 Multi-Head Attention Mechanism  
Attention mechanism divides features into multiple heads to form multiple subspaces, allowing the model to pay attention to different aspects of feature information. We call this practice the multi-head attention mechanism. The core idea of attention mechanism is the scale dot-product attention mechanism. \(Q\), \(K\), \(V\) represent query, key and value, respectively. In multi-head attention mechanism, they are all established by sending the input to full-connect layer. \(Q\) and \(K\) have the same dimension \(d_{k}\); thus, the output of scaled dot-product Attention can be written as  
\[Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{15}\]  
In this equation, \(K^{T}\) means the transpose of \(K\), the output vector is the reweighted value \(V\), the weight assigned to each value is calculated by \(K\) and \(Q\). the dimension of \(Q\), \(K\) and \(V\) can be uniformly written as \(C\times E\), where \(C\) is the channel dimension and \(E\) denotes embedding dimension. \(Q\) and \(K\) must have the same embedding dimension while the embedding dimension of \(V\) can be different. The multi-head mechanism divides \(Q\), \(K\) and \(V\) into \(h\) parts, undertakes scaled dot-product attention \(h\) times and finally concatenates all \(h\) outputs, which can be denoted by  
\[\begin{split}& MultiHead(Q,K,V)=Concat(head_{1},head_{2},\cdots, head_{h})W^{o},\\ & wherehead_{i}=Attention(QW^{Q}_{i},KW^{K}_{i},VW^{V}_{i}). \end{split} \tag{16}\]  
where \(W\) means the parameter matrix. \(W^{Q}_{i}\in\mathbb{R}^{d_{model}\times d_{k}}\), \(W^{K}_{i}\in\mathbb{R}^{d_{model}\times d_{k}}\), \(W^{V}_{i}\in\mathbb{R}^{d_{model}\times d_{o}}\) and \(W^{O}_{i}\in\mathbb{R}^{hd_{o}\times d_{model}}\)
## 5 Analytical Methods for Detection, Identification, and Quantification of C-CTXs
### Analysis of C-CTXs in Fish by HPLC-MS/MS (Low-Resolution)  
#### 5.2.1 First Quantification of C-CTX-1 in Caribbean Fish Using HPLC-MS/MS  
A significant advance in terms of ciguatoxin detection was achieved with the arrival of HPLC coupled with tandem mass spectrometry (HPLC-MS/MS). Using pure standards of P-CTX-1 and C-CTX-1 (and PbTx-2), HPLC-MS/MS signals for the detection of P-CTX-1 and C-CTX-1 were acquired using an ionspray-triple quadrupole (PE-Sciex API III) coupled to a HPLC eluted using a water/acetonitrile gradient modified with 0.05% TFA [200]. Fish samples from Saint Barthelemy (F.W.L) were also tested using MBA and analysed in the positive ion mode. The MS/MS signals were optimized for ions originating either from the loss of NH\({}_{3}\) or the additional losses of one, two, or three H\({}_{2}\)O molecules from the dominant [M+NH\({}_{4}\)]\({}^{+}\) ion [200]. Despite negative matrix effects of 37% for C-CTX-1 and 15% for P-CTX-1, this method allowed their detection at clinically relevant levels in fish flesh, with detection limits of 0.1 ppb and 0.04 ppb for C-CTX-1 and P-CTX-1, respectively, in 2.5 g of fish flesh equivalent injected in LC [200]. This first study on several Caribbean fish of variable toxicity (non-toxic, borderline, toxic by the MBA) individually extracted from 50 g of fish flesh showed that, considering a maximum safe level of C-CTX-1 set at >0.25 ppb, 5 of 12 borderline toxic fish and all toxic and inedible fish contained levels of C-CTX-1 that were likely to affect humans. Moreover, ~50% of the toxicity determined using the MBA was due to the presence of C-CTX-1 [200]. 2.2 Identification and Quantification of C-CTX-1 in Fish from Macaronesia Using HPLC-MS/MS with Water/Methanol LC Gradients and SRM on Precursor Ions [M+Na]\({}^{+}\)  
Using an LC mobile phase of methanol modified with ammonium formate and formic acid to produce high-intensity signals for stable [M+Na]\({}^{+}\) ions on ESI-triple quadrupole, [PERSON] et al. [30] monitored the sodium adducts of known Pacific CTXs as precursor ions in Q1 and as product ions in Q3. These LC conditions allowed for single reaction monitoring (SRM) of [M+Na]\({}^{+}\) > [M+Na]\({}^{+}\) transition for each CTX, and with high collision energy, it was possible to eliminate background noise and monitor the sodium product ions without any additional fragmentation [109]. This targeted MS analysis was applied to quantify C-CTX-1 in several fish species along the North-East Atlantic coasts as part of the European project \"EuroCigua: Risk Characterization of Ciguatera Fish Poisoning in Europe\" (Table 3) [109; 110; 112; 155; 175; 176]. Using this method, C-CTX-1 was detected using LC-MS/MS in eight out of eleven samples from the Selvagens Islands, but only three fish had C-CTX-1 levels above the LOQ: a 19-kg dusky grouper, _Epinephelus marginatus_ (0.05 ppb); a 1.6-kg Barred Hogfish, _Bodinus_
## 4 Vehicle Storage Analysis
### Experiment Setup and Temperature Control  
We conducted a series of experiments on QLC SSDs under varying temperature conditions to assess both performance and reliability. To simulate vehicular workloads, we used Flexible I/O Tester (FIO) [18] and HiBench [19] for generating I/O access patterns. The initial set of experiments utilized FIO to apply data storage and access loads on the SSDs, while the second set used HiBench to impose big data and machine learning workloads on the flash devices. These experiments were performed on HPE ProLiant servers with 128 GB of memory, running Ubuntu v22.04.  
To control and stabilize the temperature, each SSD was placed in a programmable thermal chamber connected via PCIe riser cables, ensuring the drives were at the specified test temperature (\(\pm\)1 \({}^{\circ}\)C) before each test began. Fans and server airflow were adjusted to minimize interference from external cooling. We kept all other factors (server load, OS settings, and so forth) consistent between runs.  
Temperature control played a crucial role in the experiments, with specific bounds established for consistency and reliability. Table 1 lists the recommended operating temperatures for different hardware devices, including SSDs. To avoid triggering thermal throttling in the SSDs, we set the upper temperature limit at 50 \({}^{\circ}\)C, as the default thermal throttling threshold for NVMe drivers is 66 \({}^{\circ}\)C. This limit helps prevent issues like thermal throttling or disconnection, which can terminate ongoing processes. For the lower temperature boundary, we adhered to guidelines from the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE), choosing 25 \({}^{\circ}\)C. These controlled temperature conditions allowed us to systematically observe and analyze the behavior of flash storage under different thermal stresses.  
Due to the confidentiality agreements with industrial collaborators, we cannot disclose exact model numbers or manufacturers of the SSDs under evaluation. However, we have characterized the tested SSDs in terms of technical specifications, including NAND type, architecture (3D QLC), firmware version, capacity, and intended usage class (consumer vs. industrial). These parameters are elaborated in Section 4 and help contextualize performance differences without disclosing proprietary identifiers.
## 3 Methods
### Improved RF Models  
Although the RF model has good performance and has been applied in many areas, there is still some room for improvement. For the random forest approach, in addition to sampling with the replacement for constructing training datasets, a random number of features are picked each time to lower the degree of correlation between individual produced decision trees. The end outcome is a simple average computed by each created decision tree of the forest [55]. Although the RF shows remarkable performance in some regression or classification tasks, it seems that some improvements for the combined way of the base learners (i.e., decision trees) can be made to achieve a better prediction of the RF. Decision trees exhibit different predictive capabilities because of the bootstrap replicates and random feature picking. However, in the regular RF, each decision tree is assigned with the same weights, which seems unreasonable and can be further improved. Therefore, some techniques such as weighting the decision trees based on their predictive capabilities are proposed to optimize the regular RF. The accuracy of decision trees is considered the indicator that can amend the weights of decision trees instead of using the same weight for each tree [59]. In this paper, two indicators, i.e., the coefficient of determination (R\({}^{2}\)) and the root mean squared error (RMSE) of decision trees, are used as benchmarks for improving the weights of decision trees. For example, the first category is that decision trees with higher R\({}^{2}\) will be assigned relatively higher weights, and vice versa. Another case is that decision trees with lower RMSE will be assigned relatively higher weights, and vice versa. Furthermore, we also put forward using three metaheuristic algorithms (i.e., GWO, WOA, and TSA) to search for the best weights of decision trees and then compare the effect of their improvements on the regular RF model.  
To sum up, there are a total of five methods that are leveraged to refine the weights of decision trees in a forest. Before applying these methods, the first task that needs to be carried out is the parameter tuning of the RF model, which is to establish the optimal RF model based on the training dataset. Subsequently, the task is to use the five categories mentioned above to amend the weights of decision trees. This part will be elaborated hereinafter. Moreover, the schematic diagram of the design of the RF and improved RF models is depicted in Figure 4.
# Hybrid Chirp Signal Design for Improved Long-Range (LoRa) Communications
###### Abstract  
Long-range (LoRa) communication has attracted much attention recently due to its utility for many Internet of Things applications. However, one of the key problems of LoRa technology is that it is vulnerable to noise/interference due to the use of only up-chirp signals during modulation. In this paper, to solve this problem, unlike the conventional LoRa modulation scheme, we propose a modulation scheme for LoRa communication based on joint up- and down-chirps. A fast Fourier transform (FFT)-based demodulation scheme is devised to detect modulated symbols. To further improve the demodulation performance, a hybrid demodulation scheme, comprised of FFT- and correlation-based demodulation, is also proposed. The performance of the proposed scheme is evaluated through extensive simulation results. Compared to the conventional LoRa modulation scheme, we show that the proposed scheme exhibits over 3 dB performance gain at a bit error rate of \(10^{-4}\).  
LoRa; LoRaWAN; IoT; LPWAN; chirp modulation 2022
# Potential Role of Plant Extracts and Phytochemicals Against Foodborne Pathogens
## 3 Plant Extracts and Phytochemicals against Bacteria Causing Foodborne Diseases
### Shigella  
_Shigella_ is a genus of Gram-negative bacteria. It has four species, _S. flexneri_, _S. sonnei_, _S. dysenteriae_ and _S. boydii_. These are rod-shaped, non-motile, non-spore forming and facultative anaerobic bacteria [144]. They cause diseases commonly known as shigellosis, characterized by profuse watery diarrhea, fever, abdominal cramps and also bloody dysentery. Ingestion of as low as 100 numbers of these bacteria can cause foodborne disease. These bacteria attack the epithelial cells of the colon of primates only. The resultant inflammation causes high intestinal motility and diarrhea which even leads to dysentery [145]. The virulence of Shiga toxin is like that of verotoxin of _E. coli_, which halts protein synthesis in the host cells [146; 104]. In Iran, pomegranate (_Punica granatum_) is commonly used to treat diarrhea. [PERSON] et al. reported that the the extracts of pomegranate were active against _Shigella_[147]. Another study has reported the anti-_Shigella_ activity of five plants, e.g., _Thymus vulgaris_ [PERSON], _Thymus carmanicus_ [PERSON], _Zataria multiiflora_ [PERSON], _Ziziphora clinopodioides_ [PERSON] and _Ziziphora tenuior_ L. Among these _Thymus carmanicus_ and _Zataria multiiflora_ were found comparatively more active having MIC values of 0.78 and 1.56 mg/mL, respectively [148]. In a study by [PERSON] et al. 23 traditionally used plants for various ailments including diarrhea, were subjected to antibacterial activity against _Shigella_ bacteria. Although, all the plants had antibacterial property, _Acacia burkei_ [PERSON]. (MIC 0.25 mg/mL), _Acanthosperm glabatum_ (DC.) [PERSON] (0.44 mg/mL), _Brachylaena transvaalensis_ [PERSON]. ex E.Phillips & Schweick. (0.5 mg/mL), _Catharanthus roseus_ (L.) [PERSON] (0.41 mg/mL), _Chenopodium ambrosioides_ L. (0.5 mg/mL), _Cissampelos hirta_ [PERSON] (0.38 mg/mL), _Gymnosporia senegalensis_ (Lam.) [PERSON]. (0.63 mg/mL), _Lippiia jacunica_ (0.5 mg/mL), _Mangifera indica_ L. (0.25 mg/mL), _Melia aczdarach_ L. (0.57 mg/mL), _Psidium guajara_ L. (0.33 mg/mL), _Sarcostemma viiminale_ ([PERSON]) [PERSON]. (0.5 mg/mL), _Schotia brachypedala_ [PERSON]. (0.58 mg/mL), _Sclerocarpa birrea_ ([PERSON].) [PERSON]. (0.34 mg/mL), _Syzggium cordatum_ [PERSON]. ex [PERSON] (0.43 mg/mL) and _Terminalia sericea_ [PERSON]. ex [PERSON]. (0.04 mg/mL) were particularly potent [149]. Essential oils are also utilized for foodborne infections caused by _Shigella_[150; 151].
# Split-And-Delay Unit for FEL Interferometry in the XUV Spectral Range
## 4 Results and Discussion
### WLI Single-Shot Precision Determination
Consequently, the contribution is the smallest for data points located on crests of the sinusoid and the largest for points of the highest gradient. Therefore, the SD plotted as a function of \(s\) oscillates at twice the frequency of the interferogram and is shifted by \(\pi/2\). To compare the contribution of the WLI error with the camera noise we bin the interferogram data in OPD bins of different size (see Figure 7). The figure clearly demonstrates that the ratio of the oscillation amplitude to the noise level decreases as the bin size gets smaller. At the bin size \(\delta s=1\) nm, the oscillation amplitude becomes comparable to the noise level and we consider this value to be the effective precision of our WLI system. In our experimental geometry, \(s=1\) nm corresponds to the SDU displacement \(\Delta z=0.75\) nm and translates into 3 attoseconds of time delay. This value is sufficient to perform interferometric experiments with laser pulses in the XUV wavelength range as was already demonstrated [22].
## 4 Experiments
### Implementation Methods  
We implement a target model, which is to be explained, and the interpretation methods in the following list.  
* **Target model:** Since our main goal is to interpret a deep learning classifier, we train a deep learning model, which is to be explained, by the proposed method. The target model is proposed for automatic sleep stage classification [39]. We train the model using the SleepEDF dataset, which is described in Section 3.3. The model consists of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). CNNs and LSTMs in the model are designed to capture features from a given epoch of an electroencephalography (EEG) signal and from sequential epochs of EEG signals, respectively. Because only CNN layers, which are called FeatureNet, achieve comparative performance with the full model in [39], our target model is FeatureNet.
* **LIMESegment [25]:** We compare our method with LIMESegment. They also exploit LIME to interpret a classifier for time series. They focus on detecting the change point given the time series data, which are considered the boundary of segments. Once the change points of the input signal are determined comparing the similarity of window slides, it generates perturbed samples. To create the perturbed samples, the intuition is that the segment in the input signal is filtered based on the threshold frequency. The threshold is determined by the highest frequency value over time with minimal variance. After that, anything lower than the threshold is filtered out. We exploit the implementation given by the authors from their GitHub repository.
* **Ours:** We also implement our interpretation method, which is described in Section 3.
# Estimation of the Noise Source Level of a Commercial Ship Using On-Board Pressure Sensors
## 3 Source-Level Estimation at Full Scale
### Hull Pressure Measurement  
For the hull pressure measurement, several holes were made at the hull surface above the propeller [14; 32]. Figure 18 shows a 3D model of the holes in the hull. It can be seen that the sensor holes were not exactly on the line of propeller plane or on the centerline. This was due to the presence of beam structures, which are seen in Figure 19 for the sensors at C0, S1, and S2. The coordinates of the sensors are listed in Table 3. Note that the origin (0,0) is the point where the centerline and the propeller plane cross.  
Figure 19: Pressure sensors at C0, S1, and S2 installed on the line of the propeller plane.  
Figure 18: Positions of the pressure sensors (bottom view): (**a**) overall and (**b**) relative to the propeller.  
For the measurement, Kulite XTM 190 pressure transducers with an HBM amplifier and a B&K data acquisition system were used. No corrections were made for these signals, as the measured signal was more than 10 dB greater than the background noise at all frequencies.  
Differently from the URN measurement, the hull pressure measurement does not need an exact data window (between \"St.4\" and \"St.5\" in Figure 16b) unless the pressures are to be synchronized with the URN. Therefore, if the operating condition does not change, the result is expected to be consistent. In the measurement, the time window of data recording was set to be one minute, during which the operating condition remained unchanged. Although this was within the data acquisition region, the hull pressures were not exactly synchronized with the URN data.