esa-sceva/satcom-chunk-collection huggingface dataset:
# MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification
###### Abstract  
The term \"proteome\" refers to the total of all proteins expressed in an organism. The term \"proteomics\" refers to the field of research that includes not only information on the expression levels of individual proteins, but also their higher-order structures, intermolecular interactions, and post-translational modifications. The core technology, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), is available for protein analysis thanks to the work of [PERSON] and [PERSON], who were awarded the Nobel Prize in Chemistry in 2002. The most successful proteome analysis in clinical practice is rapid microbial identification. This method determines the bacterial species by comparing the proteome profile of the bacteria obtained by matrix-assisted laser desorption ionization-time of flight MS (MALDI-TOF MS) with a database. MS is superior in simplicity, speed, and accuracy to classic speciation by staining and phenotyping. In clinical microbiology, MS has had a large impact on the diagnosis and treatment of infectious disease. Early diagnosis and treatment of infectious disease are important, and rapid identification by MALDI-TOF MS has made a major contribution to this field.  
mass spectrometry; matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS); blood culture; acid-fast bacteria; identification +
Footnote †: journal: Applied  
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Footnote 1: email: {T.N.  
specimens (blood, urine, feces, sputum, etc.) [11; 12; 13]. Moreover, direct identification from positive blood cultures is being investigated as an alternative to cultured colonies, to detect and identify organisms that cause bacteremia and sepsis [14; 15; 16]. For the successful MALDI-TOF MS-based bacterial identification from blood culture bottles, separation of microorganisms from host cells is a critical step, and a number of laboratory-developed and commercially available protocols for this purpose have been reported, as reviewed elsewhere [17]. Currently, to our knowledge, three commercial protocols (including versions for anaerbes and facultative anaerbes) are available: the Sepsityper(r) kit (Bruker Daltonics) [18], the Vitek MS blood culture kit (bioMerieux, Inc.) [19], and the rapid BACpro(r) II kit (Nittobo Medical Co., Tokyo, Japan) [20]. The basic principles of the Sepsityper(r) kit and the rapid BACpro(r) II kit consist of low-speed centrifugation to remove blood cells, followed by an additional lysis procedure [21].  
The clinical applications of MS are diverse, but we will focus on clinical microbiology, which is currently the most widely used application, and outline the rapid identification of microorganisms with this method.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 4 Experimental Setup  
### Experimental Design  
To elicit different levels of pilot workload among participants, this study designed three categories of simulated flight tasks, labeled A, B, and C, with increasing levels of difficulty. Task A is to pilot a single-engine light aircraft to take off and ascend to an altitude of 1500 m under the guidance of the tutorial. Task B is to pilot a Boeing 737 aircraft to approach from 3 nautical miles away and 3 km in the air under favorable weather conditions and land on a designated runway. Task C is similar to Task B but is conducted under adverse weather conditions. Weather conditions not only affect visual circumstances but also affect the piloting through the settings of dynamic parameters, thereby significantly increasing the pilot's workload. The specific details are shown in Table 1.
## 2 Materials and Methods
### Model Performances and Assessment of Variable Importance  
To compare the performances of the MLR, SVM and RF models, the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Pearson's correlation coefficient (\(r\)) and coefficient of determination (R\({}^{2}\)) were calculated as follows:  
\[\text{RMSE}\ =\sqrt{\sum_{i\ =1}^{n}\frac{\left(\text{P}_{i}-\text{A}_{i} \right)^{2}}{n}} \tag{6}\]  
\[\text{MSE}\ =\sum_{i\ =1}^{n}\frac{\left(\text{P}_{i}-\text{A}_{i}\right)^{2}}{n} \tag{7}\]  
\[\text{R}^{2}=1-\frac{\sum_{i\ =1}^{n}(\text{A}_{i}-\text{P}_{i})^{2}}{\sum_{i\ =1}^{n}(\text{A}_{i}-\overline{\text{A}}_{i})^{2}} \tag{8}\]  
\[\text{r}\ =\frac{\sum_{i\ =1}^{n}(\text{P}_{i}-\overline{\text{P}}_{i})\left( \text{A}_{i}-\overline{\text{A}}_{i}\right)}{\sqrt{\sum_{i\ =1}^{n}\left(\text{P}_{i}-\overline{\text{P}}_{i}\right)^{2}\times\sum_{i\ =1}^{n}\left(\text{A}_{i}-\overline{\text{A}}_{i}\right)^{2}}} \tag{9}\]  
where P\({}_{i}\) is the predicted value, A\({}_{i}\) is the observed value, P\({}_{i}\) is the mean of predicted values, and A\({}_{i}\) is the mean of observed values. To estimate and map the fine dead fuel load, the model with the smallest RMSE and highest r square was chosen as the most fitting. To assess the stability of the best model, 10-fold cross-validation was performed. This procedure consists of running the model 10 times on random subsamples of training and test sets and calculating the metrics of each subsample. Lastly, variable importance was calculated according to the best model, which was applied to a pilot area of the Apulia region to produce a predicted 1-h fuel load map.
## 4 Eigenvalue Fusion and other Fusion Methods for Detection OFDMA Signals
### Sample Fusion for OFDMA Signals  
Here we describe how the sample fusion strategy can be adapted to detect the occupancy of an OFDMA subchannel. Several sample combining rules can be implemented in this case; below we describe two alternatives that produce a sample covariance matrix at the FC with the same order as in the case of eigenvalue combining. These alternatives are designated as concatenation and maximum-ratio-combining.  
A number of \(K^{\prime}N\) samples are collected by each CR for each of the \(P\) subchannels. These samples are then sent to the FC, where they are processed as follows: First, a number of \(mP\) sample matrices of order \(K^{\prime}\times N\), as in Equation (22), are formed. In one situation, the matrices corresponding to the subchannel \(s\) from all CRs are concatenated, leading to \(P\) sample matrices of order \(K^{\prime}\times mN\) given by  
\[\mathbf{A}_{s}=\left[\mathbf{A}_{s}^{(1)}\quad\mathbf{A}_{s}^{(2)}\quad \cdots\quad\mathbf{A}_{s}^{(m)}\right] \tag{28}\]  
In another situation, the rows corresponding to all CRs are maximum-ratio-combined, resulting in \(P\) sample matrices of order \(K^{\prime}\times N\) given by  
\[\mathbf{A}_{s}=\sum\limits_{i=1}^{m}\mathbf{H}_{s}^{(i)}\mathbf{A}_{s}^{(i)} \tag{29}\]  
where \(\mathbf{H}_{s}^{(i)}=\text{diag}\left[H_{s,1}^{(i)},H_{s,2}^{(i)},\ldots,H_{s,K^{\prime}}^{(i)}\right]\) is the diagonal gain matrix whose diagonal entries \(\{H_{s,k^{\prime}}^{(i)}\}\) are the channel gains in the frequency domain between the primary transmitter and \(i\)-th CR in the \(k^{\prime}\)-th subcarrier of the \(s\)-th OFDMA subchannel, \(i=1,2,\ldots m\); \(s=1,2,\ldots P\); \(k^{\prime}=1,2,\ldots K^{\prime}\). From the matrices in Equation (28) or Equation (29), the corresponding sample covariance matrices  
\[\mathbf{R}_{s}=\frac{1}{N}\mathbf{A}_{s}\mathbf{A}_{s}^{\dagger} \tag{30}\]  
of order \(K^{\prime}\times K^{\prime}\) are computed and their eigenvalues \(\{\lambda_{1,s}\geq\lambda_{2,s}\geq\cdots\lambda_{K^{\prime},s}\}\) are estimated. The test statistics Equations (3)-(6) are now modified to detect the \(s\)-th OFDMA subchannel, leading to:  
\[T_{\text{GLRT},s}=\frac{\lambda_{\,1,s}}{\frac{1}{PK^{\prime}}\sum\limits_{j=1 }^{K^{\prime}}\sum\limits_{z=1}^{P}\lambda_{j,z}} \tag{31}\]\[T_{\text{MMED},s}=\frac{\lambda_{1,\,s}}{\frac{1}{P}\sum\limits_{z=1}^{P}\lambda_{K ^{\prime},z}} \tag{32}\]  
\[T_{\text{MED},\,s}=\frac{\lambda_{1,s}}{\sigma^{2}} \tag{33}\]  
\[T_{\text{ED},\,s}=\frac{1}{K^{\prime}\sigma^{2}}\sum\limits_{j=1}^{K^{\prime}} \lambda_{j,s} \tag{34}\]  
The decision upon the occupancy of the \(s\)-th OFDMA subchannel is then reached at the FC after comparing the adopted test statistic with the decision threshold.
## 4 Performance Evaluation and Analysis
### Evaluation Metrics  
In this study, accuracy, precision, recall, Negative Predictive Value (NPV), and specificity were utilized as evaluation metrics to quantitatively assess the defect detection performance of the deep learning models. Accuracy serves as a fundamental measure for evaluating the overall classification performance of the models. Precision represents the proportion of predictions identified as defective that are indeed defective, assessing the reliability of the model's defect predictions. NPV indicates the proportion of predictions classified as non-defective that are truly non-defective, measuring the reliability of the model's non-defect predictions. Recall represents the proportion of actual defective thermographic images correctly identified as defective by the model, evaluating how well the model detects defective images without missing any. Specificity represents the proportion of actual non-defective thermographic images correctly classified as non-defective, assessing how well the model detects non-defective data without overlooking any.  
In this study, these evaluation metrics were comprehensively applied to compare and analyze the defect detection performance of ResNet-50, DenseNet-121, and ViT models trained on synthetic thermographic datasets. Pre-ResNet-50, Pre-DenseNet-121, and Pre-ViT refer to models trained on unprocessed synthetic thermographic datasets, while Post-ResNet-50, Post-DenseNet-121, and Post-ViT refer to models trained on preprocessed synthetic thermographic datasets. Each model was evaluated based on accuracy, precision, recall, NPV, and specificity to identify the most suitable deep learning architecture for defect detection. Additionally, the impact of preprocessing on defect detection performance was analyzed. This approach validated the feasibility and effectiveness of the proposed synthetic data-based methodology.
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## System Model  
### System Setup  
Consider a multi-satellite system consisting of \(J\) LEO satellites, each serving \(M\) cells on the earth. Each satellite is equipped with multi-beam antennas to send service beams to its served cells. Each satellite uses \(N\) subcarriers of bandwidth \(B_{sc}\), so the total available system bandwidth is \(B_{tot}=N\cdot B_{sc}\). In the time dimension, we divide a period of time into multiple time-segments \(T_{seg}\), and then divide one time-segment into K time-slots with length \(T_{s}=T_{seg}/K\) for scheduling. During each time-slot, each satellite needs to select some cells covered by the beam according to the traffic demand of each cell. For the cell \(m\) served by the satellite \(j\), let \(\mathbf{A}_{m}^{j}\in\mathbb{R}^{N\times K}\) be the temporal and frequency resource-allocation matrix, and its element \(a_{m,nk}^{j}\) of the \(n\)th row and \(k\)th column is a binary assignment indicator, with \(a_{m,nk}^{j}=1\) indicating that in the \(k\)th time-slot, the beam using the \(n\)th subcarrier is allocated to this cell, and \(a_{m,nk}^{j}=0\) indicating it is not allocated. Meanwhile, let \(P_{max}\) denote the maximum transmission power of each satellite and \(\mathbf{P}_{m}^{j}\in\mathbb{R}^{N\times K}\) denote the power allocation matrix, with the element \(p_{m,nk}^{j}\in(0,P_{max}]\) if \(a_{m,nk}^{j}=1\).
## 4 Hybrid Fiber Composition
### Steel--Polymer Hybrid Fiber
#### 4.3.2 Mechanical Properties

Table 10: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \multicolumn{1}{c}{**Hybrid Fibers**} & & & & & & & & & & \\ \hline
**Part A** & & & & & & & & & & & \\ \hline
**Type** & \(\mathbf{l_{f}}\) **(mm)** & \(\mathbf{d_{f}}\) **(mm)** & \(\mathbf{V_{f}}\) **(\%)** & \multicolumn{2}{c}{**Part B**} & & & & & & & \\ \hline
**Type** & \(\mathbf{l_{f}}\) **(mm)** & \(\mathbf{d_{f}}\) **(mm)** & \(\mathbf{V_{f}}\) **(\%)** & \multicolumn{2}{c}{**Type**} & \(\mathbf{l_{f}}\) **(mm)** & \(\mathbf{d_{f}}\) **(mm)** & \(\mathbf{V_{f}}\) **(\%)** & & & \\ \hline straight & 14 & 0.22 & \( \begin{array}{c}0.5\\ 1.0\\ 1.5\end{array} \) & polyvinyl-alcohol & 12 & 0.026 & \( \begin{array}{c}1.5\\ 1.0\\ 0.5\end{array} \) & compressive strength = 95.8 MPa (+35.7\%) & & & & & \\ \hline  & & & & & & & & 1.5 & compressive strength = 98.4 MPa (+39.4\%) & [148] \\ \hline straight & 18 & 0.16 & 1.0 & polyethylene & 13 & 0.02 & 1.5 & compressive strength = 175.5 MPa (+24.4\%) \({}^{3}\) axial tensile strength = 11.7 MPa (+7.3\%) \({}^{3}\) & & & \\ \hline  & & &  
The UHPC mix with the addition of 1.5% straight steel fibers with a length/diameter ratio of 13/0.2 mm and 0.5% PVA fibers of 8/0.038 mm achieved a 14.3% higher compressive strength compared to the control sample and the mix containing 2% steel fibers [139]. The results indicate a positive hybridization effect without the formation of fiber agglomeration. Steel fibers were evenly distributed, allowing their properties to contribute to the increase in compressive strength. The compressive strength of the mixes with a total hybrid fiber content of 3% reached the highest value for the mixture containing 2.5% straight steel fibers with a length/diameter ratio of 13/0.2 mm and 0.5% polyethylene fibers of 12/0.024 mm [144]. Due to the higher modulus of elasticity and tensile strength of steel fibers compared to polypropylene fibers, the compressive strength of concrete improved with an increase in the content of steel fibers [189]. Increasing the proportion of polymer fibers led to an increase in the volume of pores within the matrix and disrupted its density, negatively affecting the strength parameters [190,191]. The compressive strength of the mixture with 1.5% steel fibers with a length/diameter ratio of 13/0.2 mm achieved values ranging from 142.5 to 146 MPa, depending on the content of polyoxymethylene (POM) fibers, which varied from 0.5% to 2% [145]. On the basis of the results, it can be concluded that the influence of polymer fibers on the compressive strength of concrete is not straightforward. The compressive strength depends primarily on the proportion of steel fibers in the mixture. The mixture containing only steel fibers achieved a compressive strength that exceeded 160 MPa, with a growing trend of up to 2.5% fiber content. This growing trend suggests that the steel fibers were evenly distributed, improving the bond strength between the concrete matrix and the fiber.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
###### Abstract  
In recent years, botnets have become one of the major threats to information security because they have been constantly evolving in both size and sophistication. A number of botnet detection measures, such as honeynet-based and Intrusion Detection System (IDS)-based, have been proposed. However, IDS-based solutions that use signatures seem to be ineffective because recent botnets are equipped with sophisticated code update and evasion techniques. A number of studies have shown that abnormal botnet detection methods are more effective than signature-based methods because anomaly-based botnet detection methods do not require pre-built botnet signatures and hence they have the capability to detect new or unknown botnets. In this direction, this paper proposes a botnet detection model based on machine learning using Domain Name Service query data and evaluates its effectiveness using popular machine learning techniques. Experimental results show that machine learning algorithms can be used effectively in botnet detection and the random forest algorithm produces the best overall detection accuracy of over 90%.  
b 26 April 201816 May 201818 May 201816 May 201816 May 201816 May 201816 May  
IP address, they access the C & C server to receive commands, as well as to download the updated bot code. To evade the scanning and detecting of C & C servers, the botmaster is constantly changing the names and IP addresses of C & C servers using predefined techniques, such as Domain generation algorithms (DGA), or Fast flux (FF) [3, 4]. The names and IP addresses of C & C servers are constantly being pushed to the DNS system. Bots are also equipped with the ability to automatically generate C & C server names in accordance with these techniques. As a result, bots can still find IP addresses of C & C servers by generating their hostnames automatically and using these hostnames to query the DNS service. Therefore, monitoring and analyzing DNS query data can reveal the existence of malicious activities in the monitored network, since some of the DNS query data may be generated by botnets. An in-depth analysis of suspicious DNS queries may reveal valuable information regarding the presence of C & C servers and botnets.  
This paper examines and evaluates the effectiveness of the bonnet detection method using DNS query data based on a number of commonly used machine learning techniques, including kNN, decision trees, random forest and Naive Bayes. Based on the evaluation results, we propose a bonnet detection model based on machine learning using DNS query data. The rest of the paper is structured as follows: Section 2 presents related works and Section 3 first introduces some common machine learning techniques and then describes the machine learning based botnet detection model. Section 4 describes experimental scenarios, results and the evaluation and Section 5 is our conclusion.
## 6 Discussion  
Data-sharing models typically involve three primary categories: B2B, B2C, and C2C. Organisations tend to align with one or more of these models based on their objectives and operational strategies. Consequently, the purpose behind collecting and sharing data varies among organisations and users. In each of these models, data exchange occurs between two organisations. Typically, one organisation serves as the data supplier, sharing data, while the other organisation acts as the data user, responsible for collecting and processing the shared data.  
In each of these models, the data-sharing methods and the types of data exchanged between the two entities are distinct. Moreover, there are inherent risks of privacy disclosures during the data-sharing processes. Given that the shared data in these models in the case study were often poorly managed, it is reasonable to anticipate various types of privacy risks, as each entity has unique requirements and intentions for using the data.
## 5 Numerical Results
### EH/EW of the Eye Diagrams  
Figure 11 shows the relative errors in eye height and width for 40 test channels with the PAM3 and PAM4 modulation methods. Channels No. 1 to No. 32 are stripline channels, while channels No. 33 to No. 56 are microstrip channels. Channels No. 1 to No. 10 exhibit more complex trace paths, resulting in larger errors in eye height and eye width. In contrast, channels No. 11 to No. 23, which have simpler routing with straighter or fewer bend paths, show better fitting precision. Most channels in the dataset utilize FR-4 as the dielectric layer, whereas channels No. 24 to No. 32 employ alternative materials, resulting in relatively larger errors. Compared to the stripline channels, channels No. 33 to No. 56, which utilize microstrip lines, show significantly higher relative errors, demonstrating that microstrip lines have weaker anti-interference capabilities than striplines. Table 4 shows the RMSE, MAE, MAPE, and MRE for the eye diagram prediction with the PAM3 and PAM4 modulation methods. It is evident that the prediction results exhibit good convergence and precision. Because the EH unit is chosen as mV, the RMSE and MAE metrics increase by an order of magnitude compared to EW. The results in this table, combined with those in Figure 11, demonstrate that the PAM3 and PAM4 models are effective and exhibit high accuracy.  
Figure 10: Comparison of predicted and actual values for (**a**) IL and (**b**) RL.
# Dynamic Energy Consumption Modeling for HVAC Systems in Electric Vehicles
## 3 Validation of the Model  
The model was validated through experimental measurements performed on a real air conditioning system built ad hoc for an internal combustion engine vehicle that was retrofitted to an electric vehicle. The HVAC system is composed of an aluminum evaporator with two rows of 38 parallel flat tubes with a length of 0.11 m and 72 fins each. The internal exchange area is 0.4682 m\({}^{2}\), while the external exchange area is 1.2 m\({}^{2}\). The condenser is a finned tube heat exchanger, with an internal exchange area of 0.7297 m\({}^{2}\) and an external exchange area of 1.8974 m\({}^{2}\). The compressor is a Benling scroll compressor DM18A8, with a discharge capacity equal to 18 cc/rev and a maximum power of 2 kW. The air temperatures and the refrigerant side temperatures were measured using 13 thermocouples as shown in Figure 3.  
All data were acquired by specific software developed on a Labview platform. The temperatures of the refrigerant fluid were acquired by placing the thermocouples on the metallic surface of the corresponding component.  
The pressure of the refrigerant fluid was continuously checked using two pressure sensors located before and after the compressor. With this configuration, it was possible to evaluate the minimum and maximum pressures of the refrigerant fluid within the system. The mass flow rate of the refrigerant fluid was measured using a Coriolis flow and density meter (Emerson Micro Motion R025) installed along the branch connecting the receiver dryer after the condenser outlet with the inlet of the expansion valve (MR in Figure 3).  
This position allows a correct reading of the refrigerant mass flow (the refrigerant fluid is in the liquid phase) and allows the measurement of the actual mass flow entering the expansion valve. The volumetric air flow rate was measured for different fan velocities using an integral anemometer (KIMO LV130) placed in correspondence to the air flow at the evaporator outlet (V in Figure 3). Table 1 summarizes the uncertainties of the measuring devices used for the experiments. The HVAC system, together with the related measurement instrumentation, were placed inside a remotely controlled climatic chamber. All thermal loads described in Section 2.2 were considered null to validate the HVAC system independently from the vehicle cabin model.