esa-sceva/satcom-chunk-collection huggingface dataset:
# MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification
## 10 Future Prospects  
In order to provide appropriate treatment for infectious diseases, quick identification of the causative microorganism is important, for which MALDI-TOF-MS plays an important role. In the future, MALDI-TOF-MS will become a key identification method, not only in clinical microbiology, but also in the broader field of microbiology, and will contribute greatly to the development of this field. However, some issues with the method remain. These include difficulty in differentiating related species and the inability to perform drug susceptibility tests, which may be solved with further improvements that are still expected to be a long way off. Therefore, it is essential to identify the organisms based on their basic characteristics, such as the initial appearance under Gram stain, and observation of colonies and biochemical characteristics, while considering the clinical symptoms, pathogenesis, and course of treatment.
## 1 Introduction
Moreover, the transceiver has a transmission range of \(8.2\) m and \(9.7\) m when the transmission chain margin exceeds \(20\) dB at \(1.4\) GHz and \(2.4\) GHz, respectively. To gain a more intuitive understanding of the characteristics of the antenna designed in this article, we list some main performance metrics for the antenna described in this article and compare them with those in the following references in Table 1. Firstly, the total size of the designed antenna in this article is the smallest. Secondly, only our work and the antenna in [16] have dual-polarization characteristics. Compared with [16], the peak gains, effective AR bandwidth, and SAR of our design are better, and only the impedance bandwidth in the ISM band is poor. Therefore, our design is a good candidate for use in biotelemetry wireless medical equipment applications, such as continuous wireless blood glucose monitoring and blood pressure monitoring. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref. ** & **Vol. (mm\({}^{3}\))** & **Freq. (GHz)** & **Bandwidth (MHz)** & **Gain (dBi)** & **SAR (1-g)** & **CP** \\ \hline \([6]\) & \(120.69\) & \(2.45\) & \(203\) & \(-22.7\) & \(733.5\) & Yes \\ \hline \([15]\) & \(102.87\) & \(104\) & \(150\) & \(-29.98\) & \(507\) & No \\  & \(2.45\) & \(158\)
## 3 Materials and Methods
### Encoder-Decoder Architectures
#### 3.3.2 Decoder Architectures  
The decoder reloads the spatial relationship of the features managed by the encoder and leads the upsampling processes to produce the prediction results with the same resolution as the input image. Considering the existence of both large and small samples of the same object class in the same image with different scales in underwater images during the decoding process. In this study, VGG19, MobileNet, and ResNet34 as backbones on the SUIM dataset were used. Encoder-decoder fusion architectures are given as VGG+FPN and Res34+Unet in Figure 3.  
**Unet as decoder.** Unet has a unique U-shaped architecture, comprising an encoder-decoder path. The encoder path contains four blocks within two convolutional layers following the ReLU activation function, and one max-pooling layer for downsampling. In the decoder section, upsampling is generated by concatenating the results [31].  
**Linknet as decoder.** Linknet [32] provides low computational cost with 11.5 M parameters contrary to UNet which is widely used in medical segmentation. During the feature-extraction process achieved by creating a unified mask in the UNet architecture, problems such as these masks overlapping each other or appearing too close to each other may occur.  
**FPN (Feature Pyramid Network) as decoder.** FPN [33] is a feature extractor that takes a single scale image of arbitrary size as input and extracts feature maps at multiple levels in a fully convolutional manner. It can fully connect the information of different scale feature maps, balancing special information and semantic information of each feature map.
## 2 Materials and Methods
### System Model
#### 2.1.3 Problem Formulation
\[f(t,(\Omega^{ul}(t),\Omega^{dl}(t))=(x,y))=E[\sum_{\Delta t=0}^{\infty}{( \gamma^{\Delta t\,mod\,N_{t}}\cdot r_{all}(t+\Delta t))}](\Omega^{ul}(t),\Omega ^{dl}(t))=(x,y)] \tag{11}\]  
where \(N_{t}\) is the slot number of a radio frame, and \(r_{all}(t)=\sum_{b\in B}((1-\tau_{b}(t))\sum_{u\in U}\eta_{b}(t)r^{u}_{ul}(t)\)\(+\)\(\tau_{b}(t)\sum_{u\in U}\eta_{b}(t)r^{u}_{dl}(t))\) represents the sum rate of all UE. Then, D-TDD configuration problem can be formulated as:  
\[ \begin{array}{ll}(\mathbf{P}1):\min_{\pi(a|(\Omega^{ul}(t),\Omega^{dl}(t))=( x,y))}&f(t,(\Omega^{ul}(t),\Omega^{dl}(t))=(x,y))\\ \mbox{s.t.}&\mbox{C1: }u\in U,b\in B\\ \mbox{C2: }\tau_{b}(t),\eta_{u,b},\kappa^{c}_{u,b}=0 or1\\ \mbox{C3: }\sum_{b\in B}\eta_{u,b}=1,\sum_{b\in B}\sum_{u\in U}\kappa^{c}_{u,b}=1 \end{array}  \tag{12}\]  
where C1, C2, and C3 are the constraints of indicators, which define that each UE only connects to one BS and sub-channel of each BS cannot reuse. Formulated problem P1 is a dynamic programming problem because of the existence of multiple discrete variables and the nonlinear function form. A dynamic programming problem is NP-hard problem because it combines the difficulty of optimizing over discrete variables with challenges of handling nonlinear functions. Therefore, we are motivated to design an efficient MARL solution.
## 5 Conclusions  
The integration of GeoEast and CGG dual processing systems optimized the processing workflow and facilitated the selection of the most suitable processing parameters, resulting in a high signal-to-noise ratio and the high-resolution processing of seismic data and obtaining a high-quality 3D seismic data volume. Addressing the challenge of uneven shot-receiver point distribution during data acquisition, we applied both 5D interpolation and OVT regularization processing technologies, effectively overcoming any adverse effects of irregular acquisition. This comprehensive approach provided highly consistent pre-stack migration gathers and stacked data volumes, strengthening the recognition characteristics of the goal boundary and enhancing the accuracy and resolution of our interpretations. By  
Figure 20: Diagram resulting from the comparison between the 45-Hz spectral decomposition slice goat boundary and tunnel projection for coal seam #3.  
combining data volumes of different types (i.e., conventional, conventional high-frequency, OVT processing, and OVT processing high-frequency), we conducted interactive interpretation and seismic attribute analysis, enabling fine identification of the goat through combined attribute analysis technology. Through comparisons of different sections and the application of 3D seismic data volume spectrum decomposition technology, we improved the position recognition accuracy of different layers in the goat, demonstrating high accuracy in determining the overall boundary position of the goat with a detection accuracy exceeding 75%.  
Conceptualization, G.Z. and T.D.; methodology, Y.W.; software, J.Z.; validation, J.Z., T.D. and Y.W.; formal analysis, Y.W.; investigation, G.Z. and Q.Z.; resources, Y.W. and T.D.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, J.Z., T.D. and Y.W.; visualization, Q.Z.; supervision, G.Z.; project administration, T.D. and Y.W.; funding acquisition, Z.N. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully thank the National Key Research and Development Program of China (2022 YFC2904005), the Hebei Science and Technology and Development Research Project (D2022402040) and the Innovation Fund Project of Hebei University of Engineering (SJ2401002064) for financial support.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.
## 2 Previous Space-Based Laser Communication Missions
The primary challenges encompassed: (i) ensuring quasi-error-free (bit error rate \(<\) 10\({}^{-9}\)) data transmission, especially given the fast signal fading caused by laser propagation in the atmosphere; (ii) technical demands of instruments with \(\upmu\)rad pointing requirements needed highly stable structures; (iii) optical communications introduce further complexities, particularly in optical design and detection; (iv) robust isolation between emission and reception, considering the significant power disparity of a factor of ~109 in laser power levels that range from 150 mW down to 20 to 200 pW; and (v) efficient stray light protection, especially during communication between satellites, with the Sun at angles nearing 5 degrees from the field of view, coupled with the need for exact alignment between emission and reception paths [20]. Furthermore, a coherent optical inter-satellite link in LEO was demonstrated in collaborative efforts between the German TerraSAR-X radar satellite and the U.S. Missile Defense Agency's NFIRE satellite [34]. The Laser-Communication-Terminal-on-TerraSAR-X (LCTSX) was established using a space-borne Nd:YAG laser with a wavelength of 1064 nm and two redundant 808 nm laser diode pump modules to attain higher reliability [35]. A mass of less than 30 kg and data rate of up to 5.6 Gbps was achieved over a distance of up to 80,000 km and a binary phase shift keying (BPSK) modulation scheme. The link provides sun exposure capability with a radiation tolerance greater than 80 Krad and a power consumption of less than 130 W [36]. In addition, China developed another LEO-GND communication terminal named the Haiyang-2 HY-2 satellite. The satellite was equipped with on-orbit micro-vibration measurement unit to assist in laser beam pointing prediction and correction. A data rate of 504 Mbps was achieved using a laser wavelength of 1.5 \(\upmu\)m and an Intensity Modulation/Direct Detection (IM/DD) scheme [37; 38]. In addition, the Onboard Terminal of a Laser Communication System (BTLS) on the Russian Segment demonstrated data rates of up to 1.8 Gbps between GEO-LEO and GEO-GND [39]. In addition, the USA continued its endeavours with LLCD (NASA GSFC) in 2013, which focused on Lunar-GND communication and demonstrated a 622 Mbps [40]. Table 2 summarizes the collective global effort to push the boundaries of space communication technology, each contributing unique capabilities and insights.
# Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification
## 2 Methods
### Data Normalization  
The data of the dataset were normalized to improve the results on activity recognition with machine learning methods. Four data normalization techniques were applied. Firstly, MaxAbsScaler scales and translates each feature individually by the maximum absolute value in the dataset [34]. Secondly, MinMaxScaler scales and translates each feature individually by the given range on the training set [35]. Thirdly, the RobustScaler removes the median and scales the data according to the quantile range [36]. The interquartile range (IQR) [53] is the range between the 1 st quartile (25 th quantile) and the 3 rd quartile (75 th quantile). Finally, Z-Score normalization is a normalizing strategy that avoids the outlier issue [33].
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 2 Methods
### Optimization of Weights and Biases  
Before using the networks, we optimized the values of weights and biases, using a back-propagation process. Back-propagation occurs during model training, where the data flow from the end of the network to the first layer for another iteration. We repeatedly cycled through a known dataset, calculating the error and optimizing the parameters by minimizing the loss function. To ensure a fair comparison, we adopted cross-entropy for all networks, which expresses the average discrepancy between the predicted class and the true class as follows:  
\[\text{E}\ =-\sum\ y^{\prime}_{k}\ \text{log}\ (y_{k}) \tag{4}\]  
where \(y\) is the outcome of _SoftMax_ for the k class, and \(y^{\prime}\)_k is 1 for a true prediction and 0 for a false one. We used the Adam optimizer [38] for the loss function with a learning rate of 0.001 as well as for monitoring the accuracy and mean square error.  
In this study, we used a work frame consisting of the TensorFlow 2.3.0 machine learning platform with graphics processing unit (GPU) support along with the ObsPy, NumPy, and scikit-learn libraries. We used a hardware platform containing dual GeForce RTX 2080 ti GPUs with 64 GB RAM to run all algorithms.
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 5 CoSiWiNeT Evaluation
### Predicted Clock Offset
#### 5.1.3 All Network Traces  
The NTP offset measurements were conducted simultaneously with raw offset measurements in the same WAN. To compare the outcomes of such related measurements based on the variability of predicted offsets, a statistical parameter called 'coefficient of variation (CV)' [33] was used. CV is a statistical measure of the relative dispersion of data points in a data series around the mean. It is calculated as the ratio of the standard deviation to the mean (average). As shown in Table 2, the CV is lowest for CoSiWiNeT in the case of trace 1 (41.86% and 39.13% improvement over NTP and KF, respectively) and trace 2 (35.39% and 40.33% improvement over NTP and KF, respectively). In the case of trace 3, the CV of CoSiNeT is lower than raw input and LS but comparable with KF and NTP. In trace 4, the CV of all the algorithms is almost comparable.  
Figure 10: NTP clock offset response during Trace 2 measurement.  
Figure 9: Trace 2—Statistical distribution of predicted clock offset.
## 3 Related Methods
### Dynamic Time Warping  
Dynamic time warping (DTW) is a widely used similarity measurement for time series. DTW enables to explore data points that have similar shapes at the different parts of the time series data [36].  
Suppose \(X=(x_{1},x_{2},\ldots,x_{N})\), \(Y=(y_{1},y_{2},\ldots,y_{M})\) denote two different time series where \(x_{i}\) is \(i\)th point in \(X\) and \(y_{j}\) is \(j\)th point in \(Y\), and \(N\) and \(M\) are size of the \(X\) and \  
where \(D\) is the accumulated distance matrix. The DTW distance is the minimum distance between time series of \(X\) and \(Y\). The best warping path in accumulated distance matrix D(M,N) can be calculated as follows:  
\[D(i,j)=d(i,j)+\min\begin{cases}D(i,j-1)\\ D(i-1,j-1)\\ D(i-1,j)\end{cases} \tag{5}\]  
Figure 2 presents the wind-speed time series clustering with dynamic time warping for the last 1000 h of one of our data sets. As in Section 3.1, in Figure 2, each line style represents a different type of cluster, and vertical lines form the clustering blocks. In dynamic time warping, the number of clusters is predetermined as five.
## 1 Introduction
The large mass (from 20 g onwards) of the heater block and the low speed of the thermistors and thermocouples used (response time of 100-140 ms, not ideal thermal contact with the heater block) do not allow heating or cooling of the nozzle to adjust the polymer temperature during the deposition process with a speed adequate to the deposition speed when filling the layer. There are a number of approaches for improving the consistency of quality of inter-layer fusion in the FDM 3d-printing, including the following ones: the use of ultrasonic treatment [19; 20; 21; 22; 23; 24] during and after deposition, temperature control of the deposited layers of the product during deposition [8; 25; 26; 27; 28; 29], annealing of polymer products, the use of mathematical modeling to adjust the printing parameters [30; 31; 32; 33; 34; 35], and the use of statistical methods to determine the optimal deposition mode [36; 37; 38; 39; 40; 41; 42; 43; 44]. To solve the described problems, the research team proposes an approach based on the use of an induction heating method of a thin-walled lightweight nozzle to achieve such dynamic characteristics of the heating and cooling processes of the nozzle, which will provide the possibility of adjusting the temperature of the extrudable polymer at a speed adequate to the deposition speed and a higher accuracy of temperature control due to the use of a modified eddy currents resonance method for measuring temperature. The known extruders [45; 46; 47] with induction heating have a large mass of the heated part (hot-end) therefore do not have any distinct advantages in comparison to conventional ones mainly due to the high delay of the temperature sensors used. Pyrometry is not used due to rapid contamination and changes in the properties of the controlled surface of the nozzle or heater during deposition [45; 48]. Known methods of temperature measurement based on the usage of the eddy currents resonance [49; 50] provide a measurement speed higher than that of the thermistors and thermocouples used, but do not allow to measure the temperature simultaneously with heating by high frequency (HF) currents, and their accuracy decreasesas the mass of the controlled product is getting lower. Excess temperature of the nozzle may lead to both its permanent deformation and burnout of the polymer material. On the other hand, a high cooling rate of the nozzle during material extrusion may lead to disruption or termination of the extrusion process.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & Fifth generation \\
6G & Sixth generation \\ AGM & Arithmetic to geometric mean \\ AUC & Area under the curve \\ AWGN & Additive white Gaussian noise \\ BPSK & Binary phase-shift keying \\ CC & Control channel \\ CFCPSC & Circular folding cooperative power spectral density split cancellation \\ CPSC & Cooperative power spectral density split cancellation \\ CR & Cognitive radio \\ CSS & Cooperative spectrum sensing \\ DF & Decision fusion \\ DFT & Discrete Fourier transform \\ DN & Dynamical noise \\ ED & Energy detection \\ EF & Eigenvalue fusion \\ FC & Fusion center \\ FS & Frequency-selective \\ GLRT & Generalized likelihood ratio test \\ HD & Hard decision \\ IoT & Internet of things \\ MMED & Maximum-minimum eigenvalue detection \\ nCSS & Non-cooperative spectrum sensing \\ NU & Noise uncertainty \\ PSD & Power spectral density \\ PU & Primary user \\ QoE & Quality of experience \\ QoS & Quality of service \\ ROC & Receiver operating characteristic \\ SCM & Sample covariance matrix \\ SF & Sample fusion \\ SNR & Signal-to-noise ratio \\ SS & Spectrum sensing \\ SSE & Signal-subspace eigenvalue \\ SU & Secondary user \\ WEF & Weighted eigenvalue fusion \\ WRAN & Wireless regional area network \\ WSF & Weighted sample fusion \\ \end{tabular}
## 5 Discussion  
### Comparison of Research Results  
This study systematically reviews the existing literature to reveal its academic value in the field of student academic performance prediction and identifies the unique contributions of this research from a methodological innovation perspective. In terms of technical approach, unlike the single deep neural network architecture used in [14], this study innovatively constructs a multi-model fusion framework, effectively integrating the strengths of heterogeneous models through ensemble learning, significantly enhancing the stability and accuracy of the prediction system. In contrast to [15], which solely relies on SMOTE oversampling or feature selection algorithms to address data imbalance, this method combines algorithm optimization with model integration, offering a multi-dimensional solution. Addressing the limitations of clustering analysis and deep learning in [16], this study creatively merges supervised and unsupervised learning methods, establishing a hybrid architecture where regression and classification models collaborate, expanding the prediction scope. Unlike [17], which relies on AutoML for automatic optimization, this study enhances both prediction performance and result interpretability through manually designed model selection and parameter tuning mechanisms. Although [18] constructs a prediction model based on online learning behavior data, this study builds a more robust prediction system by integrating multiple classification techniques. Notably, while [19] focuses on online education, the multi-source data fusion approach validated in this study within the context of traditional university education shares methodological similarities.
## 2 Islanding Detection Methods
### Passive Methods
#### 2.1.6 Total Harmonic Distortion Method (THD)  
The fundamental tenet of this approach is based on the PCC's measurement of total harmonic distortion (THD), which provides a clue to the existence of an islanding scenario. The harmonics produced by the inverter and transmitted to the grid do not create any distortion at PCC when the main grid is connected since the grid impedance is low. However, when islanding occurs, the inverter's current harmonics are passed to the load, causing harmonic distortions at the PCC, and if they surpass a threshold value, islanding will be recognized [14].  
This approach is simple to use and works even when the system has several inverters connected in parallel. However, setting the threshold value is not easy because any disturbance in the grid can cause error detection.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 7 Challenges for Future Work
### Evaluation and Performance Parameters Challenge  
The integration of these three different technologies, ML, SDN, and MSA, requires different sets of performance parameters to evaluate the final results. Performance parameters, such as latency and throughput, are considered under the utilization of SDN technology. Accuracy, precision, recall, and receiver-operating characteristic curve (ROC) are determined using ML technology. While evaluating the performance of microservices architecture in the proposed methodology, performance parameters, such as response time,CPU, and memory usage, etc., must be investigated. So, a plan of action to evaluate the proposed methodology should be set up before implementing it.  
Potential Course of Action  
A potential approach to overcome these challenges can be initiated by evaluating the entire IoMT network through packet delay and throughput parameters. Furthermore, due to the add-on features of real-time anomaly detection provided by the execution of the ML script on the software stack, these can be measured through the anomaly detection speed along with the parameters related to the accuracy. Finally, for evaluating the overall route optimization in this setup, the response time and new rule flow reconfiguration time can be considered as performance parameters.