esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Metal-matrix-impregnated diamond composites are seeing wide use in the fabrication of diamond tools for cutting, grinding, polishing, and drilling hard and brittle materials such as ceramic, rock, and concrete [1; 2; 3; 4; 5; 6; 7]. The metal-matrix-impregnated diamond composites prepared by powder metallurgy consist of two parts: diamond grits and metal matrix. The diamond grits embedded in the metal matrix are the working element, and the metal matrix has a decisive effect on whether or not the diamond grits can function fully and effectively [2; 8].  
At present, pre-alloyed powders have successfully realized industrial application in diamond tools, including diamond cutting segments and diamond drill bits [9; 10; 11; 12]. The component uniformity is one of the advantages of pre-alloyed powders, because each alloying particle includes the entire composition, which means the segregation can be avoided [13]. In addition, the pre-alloying of powders can decrease the activation energy, which is conductive to reducing the sintering temperature, which in turn can avoid diamond graphitization [14]. The Fe-Co-Cu matrix, sintered from pre-alloyed Fe-Co-Cu powders, is usually used as the matrix in diamond tool manufacturing [4; 10]. It has high strength and adjustable properties suitable for different kinds of hard materials, ensuring that the diamond grits contact the hard materials effectively, maintaining an abrasive cutting surface. Nevertheless, the hard and complex service conditions such as elevated temperature, high impact stresses, and hydro-abrasion demand that the mechanical properties and wear resistance be further improved [7; 15].  
Deep cryogenic treatment (DCT) is a supplement of conventional heat treatment used to modify materials properties in order to enhance their performance [16; 17]. Recently, some researchers found that deep cryogenic treatment can improve the hardness, compression strength, fatigue resistance, and wear resistance of tool steels [18; 19; 20; 21; 22; 23]. The transformation of retained austenite to martensite, the release of residual stress, and the precipitation of ultra-fine carbide particles are the main mechanisms of cryogenic treatment in tool steels [24]. Moreover, some studies have indicated that deep cryogenic treatment can increase the wear resistance and service life of polycrystalline diamond compact (PDC), which is ascribed to the change of stress state [25]. It can be predicted that deep cryogenic treatment is a promising way to improve the properties of metal-matrix-impregnated diamond composites. However, the effect of deep cryogenic treatment on the microstructure, mechanical properties, and wear resistance of metal-matrix-impregnated diamond composites has been rarely studied.  
In this study, deep cryogenic treatment was applied to Fe-Co-Cu-based diamond composites. The effects of deep cryogenic treatment on the microstructure, mechanical properties, and wear resistance of Fe-Co-Cu-based diamond composites were investigated and discussed. This work is aimed at seeking the optimal treatment time to meet the requirements of mechanical properties and wear resistance.
## 4 Summary and Outlook  
Nanosecond optical pulses at two alternating wavelengths between 964 nm and 968 nm were generated, using a MOPA system consisting of a Y-branch DFB laser and a power amplifier with an integrated optical gate and tapered section. The Y-branch DFB laser was operated in CW mode, while the optical gate and the tapered section were operated in pulsed mode. The spectral properties of the MOPA are governed by the MO and the Fourier transform limit of the generated pulses. The measured spectral widths below 10 pm, limited by the resolution of the OSA, remained constant at all output power levels of the MOPA system. A true spectral line width of the order of 100 MHz (0.3 ps) for 8 ns long pulses, given by the Fourier transform limit, can be expected. A maximum optical peak power of 16.3 W and a SMSR of 37 dB were obtained for a pulse width of 8 ns. These parameters show the potential suitability of the light source concept for the measurement of water vapor, using the DIAL technique. Further studies will include measurements of the spatial beam properties and the spectral linewidth of the MOPA system. The attachment of a fast driver circuit would enable a rapid switching between the wavelengths of the two DFB branches in our monolithic MO. A field test of differential absorption LIDAR measurement will be performed in future to confirm the advantage of the system in water vapor detection.  
Conceptualization, B.S., A.K., T.Q.T. and G.T.; methodology, A.K. and T.N.V.; Investigation, T.N.V. and J.F.; supervision, writing--original draft, writing--review and editing, T.N.V., H.W., B.S., J.F. and G.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Institute of Materials Science, Vietnam Academy of Science & Technology under Grant Number CS.16/20-21.  
Not applicable.  
The data can be received from the authors on request.  
T.N.V.; A.K. and B.S., acknowledge the technical support by [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 5 Case Study: Panel Test and E-Nose for the Determination of VOOs' Shelf Life
### EVOO Quality and Main Stability Issues
Since VOOs produced in one crop season are usually consumed before the next crop season [115, 116], it is necessary to minimize oil deterioration during the storage period. The assurance of the stability of EVOOs is a matter of great concern for the olive industry [115]. In this context, it is mandatory for the olive oil industry to monitor oil quality throughout the production line and to be able to provide realistic information about the stability and the SL. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Free Acidity** & **Peroxide Index** & \multirow{2}{*}{\(\mathbf{K_{232}}\)} & \multirow{2}{*}{\(\mathbf{K_{270}}\)} & \multirow{2}{*}{\(\mathbf{\Delta K}\)} & **Fruity** & **Defects’** \\  & **\% Oleic Acid** & & & & & **Median** & **Median** \\ \hline EVOO & \(\leq\)0.8 & \(\leq\)20 & \(\leq\)2.50 & \(\leq\)0.22 & \(\leq\)0.01 & \(>\)0 & \(=\)0 \\ VOO & \(\leq\)2.0 & \(\leq\)20 & \(\leq\)2.60 & \(\leq\)0.25 & \(\leq\)0.01 & \(>\)0 & \(<\)3.5 \\ LOO & \(>\)2.0 & - & - & - & - & - & \(>\)3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quality standards of virgin olive oil as requested by current legislation (EU Reg. (EEC), 2568/91 as amended, that also establish the official methods for their determination [7]).
## 3 Samples and Experimental Methods  
### Sample and Data  
The geophysical logging curves of 43 exploratory wells in the study area were collected, and a total of 6 sand groups, including source rocks (shale/mudstone) and tight sandstone reservoirs, were interpreted. To complete the corresponding supporting experiments, a total of 62 tight sandstone samples were collected from the cores of 17 key exploration wells. There were 24 samples from turbidite reservoirs and 38 samples from delta front reservoirs, all of which were conventional core plunger samples with a length of 6 cm and a diameter of 2.5 cm cut parallel to the bedding plane. To ensure the scientific and rigorous selection of samples, all the key well cores related to this study were carefully observed and described. Therefore, the samples were selected according to their color, structure, granularity, and oil-bearing characteristics. Moreover, all the samples were tested and analyzed with fresh cut surfaces to improve the accuracy of the results without the influence of weathering. The 6 cm long core plunger sample was cut into 3 sections and the 4 cm core in the middle section was used for physical property determination, HPMI, and NMR analysis. The remaining 2 cm at the upper and lower ends were subsequently used for X-ray diffraction (XRD), casting thin sections, cathode luminescence experiments, and SEM analysis. Before the experimental measurements, rock samples were washed with a dichloromethane-methanol (93:7, \(v/v\)) solvent at 90 \({}^{\circ}\)C and 0.5 MPa for seven days to remove any residual oil [44]. The cleaned samples were then dried in a constant-temperature chamber (60 \({}^{\circ}\)C/48 h) before proceeding with the experiments. XRD, porosity, permeability, and SEM were performed for all the samples and 13 representative key samples were selected for cathodoluminescence (CL), HPMI, and NMR analyses according to the experimental analysis results. In addition, more than 150 lithologic composition, XRD, porosity, and permeability data points were collected from the Sinopec Shengli Oilfield Branch.
## 1 Background  
The Nd:YAG Laser has become indispensable in lung surgery procedures [1]. It is used mainly in the context of open surgical procedures for the resection of lung metastases. After opening the thorax, the entire non-ventilated lung is examined by palpation. The aim is to perform a non-anatomic resection of all palpable lung metastases in order to preserve the largest possible amount of lung parenchyma [2]. Anatomical lung resections are only carried out when the metastases are located more centrally or have reached a certain size [1]. If the branch of the lobar bronchus exhibits infiltration it even becomes necessary to perform sleeve lobectomy. The laser method is an excellent method for removing lung metastases with a safety distance of 5 mm [3]. The lung metastasis is felt by palpation, after which the lung parenchyma surrounding the lesion is separated. An advantage of this method is that the resection surface is coagulated at the same time. It is thus possible for the surgeon to work without any blood loss. In addition, the lung can be inspected for further lesions [4]. In our clinic we use the diode-pumped Nd:YAG Laser LIMAX(r) 120 (wavelength: 1318 nm) (Gebruder Martin GmbH & Co KG, Tuttlingen, Germany). The advantage of a diode-pumped Nd:YAG laser in comparison to a Nd:YAG laser is, that a higher rate of electric energy will be transformed into laser light. So the diode-pumped Nd:YAG laser is more efficient. As a rule, we work with a focusing handpiece without contact to the tissue and a laser power of 80 to 100 W. The maximum focus of the laser beam is achieved at a distance of 30 mm to the lung surface, where it exerts the maximum effect. Lung parenchyma is continuously cut and simultaneously coagulated by moving the handpiece. Smoke evacuation is required due to the development of smoke during the procedure.  
As an alternative to the focusing handpiece, a laser fiber of a so-called bare fiber can also be used. A 600 mm bare fiber is currently available for applications involving the lung parenchyma. The separation of the lung parenchyma is performed through direct contact with the laser fiber, which is moved forward.  
Similar to a scalpel, the surgeon is able to precisely cut through the tissue with the laser. The laser fiber is held by hand with a laser fiber holder. The laser fiber is guided along the intended resection line with permanent contact to the lung parenchyma. If tissue particles adhere to the laser fiber, there is always the risk of spontaneous ignition and burning of the laser fiber, which must then be prepared again for further use. Unfortunately, there are no data available on the extent of the local effect of this bare fiber on the lung parenchyma depending on the operating speed and laser power. We would like to study this question using a specially developed experimental design.
# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
###### Abstract  
Image-based Arabian camel breed classification is an important task for various practical applications, such as breeding management, genetic improvement, conservation, and traceability. However, it is a challenging task due to the lack of standardized criteria and methods, the high similarity among breeds, and the limited availability of data and resources. In this paper, we propose an approach to tackle this challenge by using convolutional neural networks (CNNs) and transfer learning to classify images of six different Arabian camel breeds: Wadden, Majaheem, Homor, Sofor, Shaele, and Shageh. To achieve this, we created, preprocessed, and annotated a novel dataset of 1073 camel images. We then pre-trained CNNs as feature extractors and fine-tuned them on our new dataset. We evaluated several popular CNN architectures with diverse characteristics such as InceptionV3, NASNetLarge, PNASNet-5-Large, MobileNetV3-Large, and EfficientNetV2 (small, medium, and large variants), and we found that NASNetLarge achieves the best test accuracy of 85.80% on our proposed dataset. Finally, we integrated the best-performing CNN architecture, NASNetLarge, into a mobile application for further validation and actual use in a real-world scenarios.  
transfer learning; convolutional neural networks; image classification; camel; InceptionV3; NASNetLarge; PNASNet; MobileNetV3; EfficientNetV2 +
Footnote †: journal: Applied sciences
## 5 Barriers
### IoT Implementation Barriers
#### 5.1.1 Physical Layer Security Threats  
The sensing layer manages IoT sensors and actuators, also known as the physical layer. Sensors keep track of a wide range of physical processes in their immediate surroundings. Actuators use sensory data to accomplish a specified task in a physical situation. At the physical layer, the following critical security threats can be encountered: (1) Denial of Service (DoS): A well-known type of security attack can keep IoT devices busy receiving and processing erroneous signals, preventing them from converting to an energy-saving sleep mode and quickly draining their battery capacity. Denial-of-sleep is another name for this form of DoS attack, and it is seen as a particularly critical security issue for IoT  
Figure 10: IoT implementation barriers and threats.  
devices with little battery capacity. (2) Selective Jamming Attack: Jamming attacks are among the most severe and well-known DoS attacks against wireless networks. Selective jamming is more power-efficient to run and substantially more challenging to check and recognize than other jamming attacks due to the opponent's lesser exposure. (3) Insecure Initialization: Initializing and configuring IoT at the physical layer is a precautionary measure that ensures appropriate use of the entire framework while ignoring security and network service interruption. The attacker injects malicious code into the memory of the IoT devices as part of the attack. (4) Insecure Physical Interface: A few physical components work together to create real threats to IoT device functionality. Physical security flaws, programming access through physical interfaces, and testing/debugging devices have all been used to compromise network nodes. (5) Spoofing: These attacks target the weaknesses in IoT device identity validation methods. Rich countermeasures are challenging to build due to resource restrictions and the enormous number of IoT devices. The Sybil assault is a paradigm in which adversaries attempt to duplicate or counterfeit real people's identities to commit malicious behaviors and degrade IoT performance [126].
# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 4 Results
### Experiment Setup  
The code for the proposed model is implemented using the Pytorch 2.3.1 framework. The Chinese pre-trained model is based on the bert-base-Chinese model released by Google, while the English pre-trained model utilizes the bert-base-cased model. To prevent overfitting, a dropout rate of 0.3 is applied. The initial learning rate is set to 2 \(\times\) 10\({}^{-5}\), and the batch size is set to 4. The proposed model, along with several baseline models, is trained and tested using an NVIDIA A40 GPU.  
Three commonly used evaluation metrics are employed to evaluate the model's performance: precision, recall, and F1 score. Precision represents the proportion of true entities among all predicted entities, recall represents the proportion of true entities correctly predicted by the model out of all true entities, and the F1 score is the harmonic mean of precision and recall. The formulas for these metrics are provided below:  
\[Precision=\frac{\sum_{i=1}^{c}TP_{i}}{\sum_{i=1}^{c}TP_{i}+\sum_{i=1}^{c}FP_{i}}\]  
\[Recall=\frac{\sum_{i=1}^{c}TP_{i}}{\sum_{i=1}^{c}TP_{i}+\sum_{i=1}^{c}FN_{i}}\]  
\[F1=2*\frac{Precision*Recall}{Precision+Recall}\]  
In addition, to evaluate the model's reliability, we use bootstrapping to compute the confidence intervals for accuracy, recall, and F1 score, with a 95% confidence level. We also employ the paired t-test to assess whether the performance difference between the proposed model and the baseline model is statistically significant, with the significance level set at \(p<0.05\).
## 3 Materials and Methods
### Loss Function  
We employed two different loss functions, classification and hybrid loss functions, to investigate whether performance would be improved by learning ordinal relationships among the age classes. The main purpose of the loss function is to optimize the model by minimizing classification loss. To improve the performance in ordinal classification, we added a regression loss term to the loss function, which provided ordinal information for optimization.  
To implement the classification loss function, we combined cross-entropy and cosine losses. Cross-entropy is a widely used method for optimizing various types of models for classification problems. However, a previous study suggested that cross-entropy might not be an optimal choice for training models with small datasets [57]. To address this issue, [PERSON] et al. proposed a cosine loss as a loss function, which maximized the cosine similarity between the true class and the model's output, and demonstrated that the cosine loss outperformed cross-entropy by a considerable margin when a model was trained on small datasets [57]. Furthermore, for small datasets, combining cosine loss and cross-entropy outperforms employing either cosine loss or cross-entropy alone [57]. Therefore, the classification loss function used in this study can be defined as follows:  
\[L_{class}=CrossEntropy(y_{class},\hat{y}_{class})+CosineLoss(y_{class},\hat{y}_{ class}),\]  
where \(CosineLoss=1-\) (cosine similarity between \(y_{class}\) and \(\hat{y}_{class}\)). Additional details on the implementation of cosine loss can be found in [57].  
To implement the hybrid loss function, we added the MSE loss term to the loss function, which considers the ordinal relationship among age classes. Models were constrained to learn the median age of each age group before being recategorized. The MSE loss penalized a failed prediction that was farther from its corresponding true class, whereas all misclassifications were penalized equally without MSE loss. The hybrid loss function can be defined as follows:  
\[L_{hybrid}=L_{class}+MSE(y_{rgs},\hat{y}_{rgs}).\]
# Experimental Investigation into the Influence of Plasma Technology on Seed Surface Wettability
## 5 Conclusions  
The novelty of this study is based on the comparison of the effect of two types of plasma discharges on seeds and demonstrates their necessity to optimize the process parameters for each type of seed separately.  
Plasma treatment can affect the wettability of the surface of various seeds, but it is strongly dependent on the type of plasma discharge and the selected process parameters. LPMD has a significantly more positive effect on reducing the value of the apparent water contact angle on the seed surface. Generally, higher microwave power together with prolonged duration has a more significant effect. However, it is necessary to set up the process parameters in such a manner as to avoid thermal seed damage. APD reduces the water contact angle value only minimally, and instead, there is a negative effect on the wettability.  
Conceptualization, P.K. and P.B.; methodology, P.K. and P.S.; validation, P.K., P.B. and A.B.; formal analysis, P.K.; investigation, P.O., Z.H. and A.B.; resources, P.B. and P.S.; writing--original draft preparation, P.K., P.O., Z.H., S.K. ([PERSON]), P.C., M.F. and S.K. ([PERSON]); writing--review and editing, P.K., P.O., S.K. ([PERSON]), P.C. and M.F.; supervision, P.B., S.K. ([PERSON]) and P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
This work is based on data obtained within the project TA04021252 \"Development of equipment for physical treatment of seeds and malt using low-temperature plasma\" (Technology Agency of the Czech Republic).
## 2 Data and Methodologies
### CEVSA2 Model Simulations  
Firstly, the 30-year averaged climatic data (1971-2000) and a fixed CO\({}_{2}\) concentration level of the 30-year mean value were used to drive the CEVSA2 model until the model reached equilibrium status, so that the initial state parameters were obtained. Then, the simulation was conducted using transient climate and atmospheric CO\({}_{2}\) data for the period 1961-2099 in China at a time step of 10-days and a spatial resolution of 0.1 degrees. To eliminate the impact of assumed initial state, the model was run repeatedly with the same transient datasets for dynamic simulation.
## 4 Experimental Stations
### Experimental Station Bernina
#### 4.2.12 ESB-MX Station  
Until Experimental Station Cristallina (ESC) becomes operational, fixed target protein crystallography [181; 182; 183; 184; 185; 140] will be implemented at Bernina [186]. A dedicated station (ESB-MX) is currently under construction. It will allow for serial femtosecond crystallography experiments at up to 100 Hz from 3D micro-crystals of size below 1 \(\upmu\)m, as well as synchrotron-like data collection schemes from large crystals [187]. The crystals will be placed on a solid support, for example a chip with a silicon nitride window. The measurements can be performed in air or in helium atmosphere for photon energies from 5-12.4 keV. The samples will be either at room temperature or maintained under cryogenic conditions by a cold nitrogen or helium gas jet. The movable ESB-MX station can be installed and aligned within one day in the Bernina hutch.  
The installation requires the GPS and XRD stations to be moved to the side along the rails. The main element of the station will be a sample diffractometer suitable for various data collection schemes which require a precise and fast tracking of the crystal positions. The station also includes an experimental chamber for the diffractometer and the cryo-jet, the room temperature and liquid nitrogen sample storage, as well as a robot for automated sample exchange. The diffraction images from the protein crystals will be collected with the 16 M pixel array JUNGFRAU detector positioned immediately downstream of the chamber with the large robot arm fixed to the ceiling. In this configuration, a resolution of better than 1 A and 2.5 A can be achieved with 12 keV and 5 keV radiation, respectively.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 5 Smart Grid Attacks
### Non-Cryptography Solutions  
Cryptography plays a crucial role in securing smart grids, particularly in terms of data security. However, security solutions can be based on a non-cryptographic approach to detect and prevent intruders in addition to correcting the damage at a lower latency time and reinforcing the security level with an acceptable trade-off. This also requires providing additional layers of security while allowing them to be linked with cryptographic techniques to provide comprehensive security for smart grids. The main non-cryptography solutions presented below include anomaly/cyberattack detection and honeypot solutions.