esa-sceva/satcom-chunk-collection huggingface dataset:
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 3 Results
### Evaluation Metrics  
The model is evaluated and monitored using accuracy, precision, recall, and F1 score metrics. By observing changes in accuracy through these metrics, we can discern whether the model is overfitting. Precision allows us to determine how many of the samples predicted as positive by the model are actually positive. Recall shows us how many of the actual positive samples are correctly predicted by the model. The F1 score provides a comprehensive evaluation of the balance between the model's precision and recall. These metrics help us identify the strengths and shortcomings of the MMDL-Net model and guide us to make necessary adjustments and improvements, thereby enhancing the effectiveness and accuracy of the multi-label classification tasks.  
According to the concept of the confusion matrix, a \"matching matrix\" is often used. Each column in the matrix represents the predicted values, while each row represents the actual categories. It indicates whether there is confusion among multiple categories, that is, whether one class is predicted as another class. By understanding the parameters in Table 3, we can comprehend and calculate the evaluation metrics such as accuracy, precision, recall, and the F1 score.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{4}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{7}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration Item** & **Details** \\ \hline Deep Learning Library & TensorFlow \\ software & PyCharm PROFESSIONAL 2019.3 \\ Server & AMAX, Fremont, CA, USA \\ Graphics Card & NVIDIA GeForce 2080 Ti, Santa Clara, CA, USA \\ Optimizer & 0.001 \\ Training Cycles & 500 epochs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information on experimental configurations.
## 3 Results and Discussion
### Extraction and Clean-Up Optimisation
#### 3.2.1 Extraction
On the other hand, using EDTA may reduce the efficiency of the clean-up step [68]. In some cases, the presence of Na\({}_{2}\)EDTA adversely affected pharmaceutical recoveries. Ethyl acetate extraction in the presence of Na\({}_{2}\)EDTA (trial 9) yielded significantly lower recoveries of atenol, ciprofloxacin, doxycycline, duloxetine, fluoxetine, and indapamide compared to the procedure without the chelating agent (trial 7) (mean recoveries of 64 and 70%, respectively). In the case of acetonitrile extraction, an adverse effect of the presence of Na\({}_{2}\)EDTA (trial 5) was observed for only two of the three beta-blockers tested--atenol and metoprolol--and serntraline, an SSRI antidepressant. A possible rationale for this is that when Na\({}_{2}\)EDTA is in excess, it chelates metals and organic compounds [78]. Using the acetonitrile--citrate buffer--Na\({}_{2}\)EDTA combination (trial 6) resulted in the best recoveries, 70-99% (average of 85%). The recoveries of bisoprolol, candesartan, and venlafaxine N-oxide in this variant were satisfactory (above 70%). Nevertheless, this variant had relatively high RSD values, as high as 22% for levonorgestrel, 24% for duloxetine, and 29% for metformin (mean of 14%). The matrix effect, directly related to the use of the MS/MS technique, is a significant factor affecting the quality of the results. The presence of co-extracted substances in the sample can interfere with the ionization process at the spectrometer interface, leading to increased or decreased signal intensities of the analytes. Consequently, this can result in falsification of quantitative results [79]. The use of the described QuEChERS protocols was associated with the presence of substances in the extract, causing strong attenuation or amplification of the analyte signals of pharmaceuticals (ME between \(-89\) and 79%). ME values for trial 6 were the most promising (ME between \(-28\) and 37%); for 65% of the compounds, a satisfactory ME value (between \(-20\) and 20%) was achieved (Figure 2). An additional clean-up step based on using different sorbents was introduced to eliminate or reduce ME.
###### Abstract  
In this paper, a wireless mid-infrared spectroscopy sensor network was designed and implemented for carbon dioxide fertilization in a greenhouse environment. A mid-infrared carbon dioxide (CO\({}_{2}\)) sensor based on non-dispersive infrared (NDIR) with the functionalities of wireless communication and anti-condensation prevention was realized as the sensor node. Smart transmission power regulation was applied in the wireless sensor network, according to the Received Signal Strength Indication (RSSI), to realize high communication stability and low-power consumption deployment. Besides real-time monitoring, this system also provides a CO\({}_{2}\) control facility for manual and automatic control through a LabVIEW platform. According to simulations and field tests, the implemented sensor node has a satisfying anti-condensation ability and reliable measurement performance on CO\({}_{2}\) concentrations ranging from 30 ppm to 5000 ppm. As an application, based on the Fuzzy proportional, integral, and derivative (PID) algorithm realized on a LabVIEW platform, the CO\({}_{2}\) concentration was regulated to some desired concentrations, such as 800 ppm and 1200 ppm, in 30 min with a controlled fluctuation of \(<\)\(\pm\)35 ppm in an acre of greenhouse.  
Keywords:non-dispersive infrared; CO\({}_{2}\) sensor; wireless sensor networks (WSN); greenhouse +
Footnote †: journal: Sensors
# An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image
## 1 Introduction
Therefore, this paper studies the aircraft object detection algorithm with small samples. Figure 1: An image in the Pascal VOC dataset [10], in which a bus and a person are marked by yellow boxes. The circle frequency filter algorithm is small in calculation, good in saliency objects detection. However, the performance of circle frequency filter greatly reduces when detecting small objects and multiple objects. Therefore, this article proposed the fusion algorithm to improve the circle frequency filter detection accuracy with small samples. The experimental results show that the proposed method can correctly detect 12,215 aircraft on test set, accounting for 91.68% of the total number of aircraft in the test set. From the data analysis, it can be seen that in the case of small samples, the proposed method has better detection performance than Faster R-CNN, and the detection speed is faster.
# Profiling Croatian Whisky Using GC/MS-MS and UHPLC-QTOF
## 3 Results and Discussion  
This study's primary focus is on Croatian whisky from the growing interest in emerging whisky-producing regions and the need for scientific characterization of their products. Unlike traditional whisky-producing nations such as Scotland, Ireland, and the United States, Croatia's whisky industry is still in its developmental stages, with limited literature available on its chemical composition. Croatian whisky is influenced by locally sourced raw materials, distinctive climatic conditions, and modern distillation and maturation techniques, making it a valuable subject for analytical investigation.  
A comparative aspect with Scotch whisky is incorporated to contextualize the findings. Scotch whisky serves as a benchmark due to its well-documented chemical profile and global recognition. By analyzing six Scotch whisky samples using UHPLC-QTOF, this study assesses the extent to which Croatian whisky aligns with or diverges from established Scotch whisky profiles. This comparative analysis helps elucidate the impact of raw materials, fermentation conditions, distillation methods, and maturation processes on whisky chemistry.
## 8 Application for Optimum Inspection Planning  
Optimum inspection planning in this study indicates the process of determining the best inspection application times to minimize the probability of failure (_p/p_). In general, the optimization problem is formulated with design variables, objectives, constraints, and given conditions. Multiple objectives can be considered individually or simultaneously in an optimization problem for inspection planning [2]. In this application, a single-objective optimization was adopted and formulated as:  
Find the inspection application times \(\mathbf{t_{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text  
the dispersion of the optimum \(t_{insp,1}\) and \(p_{f}\). Each solution was represented by a polyline connecting \(t_{insp,1}\) and \(p_{f}\) on the vertical axes. As shown in Figure 11a, one of the solutions using MCS indicates the optimum inspection application time \(t_{insp,1}\) = 7.025 years and the associated \(p_{f}\) = 3.90 \(\times\) 10\({}^{-5}\). The optimum inspection application times \(t_{insp,1}\) were differed slightly across solutions, and the largest and smallest \(t_{insp,1}\) values in the 100 solutions were 7.036 years and 6.985 years, respectively. However, \(p_{f}\) remained near 3.90 \(\times\) 10\({}^{-5}\). When KDE was used to compute \(p_{f}\), the obtained optimum inspection application time was 7.013 years for all 100 computations (see Figure 11b). The \(t_{insp,1}\) value obtained with 100 computations using the combination of KDE and GPD was 7.157 years (see Figure 11c). As shown in Figure 11, (a) a more reliable optimum inspection plan can be obtained when KDE and the combination of KDE and GPD methods are used; (b) the objective values (i.e., \(p_{f}\)) of the optimization are almost non-dispersed when MCS, KDE, and the combination of KDE and GPD are used; and (c) the objective values based on MCS and KDE are almost the same (i.e., \(p_{f}\) = 3.90 \(\times\) 10\({}^{-5}\) for MCS and \(p_{f}\) = 3.97 \(\times\) 10\({}^{-5}\) for KDE). \(p_{f}\) obtained using MCS cannot be less than 0.1 \(\times\) 10\({}^{-5}\), as the sample size used for MCS was 10\({}^{6}\).  
Figure 11: \(Cont.\)
## 5 Results
### Few-Shot Scenario  
In this section, the results obtained for the few-shot approach scenario are presented. The values of the evaluation metrics are shown in Table 4. Three cases were considered. For each of the cases, the SIMARGL2021 dataset was enriched with additional \(N\) samples from the IoT-23 dataset. The number was gradually increased, starting from 100 traces, which  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Recall** & **Precision** & **F1-Score** \\ \hline Benign & 1.0000 & 0.9999 & 1.0000 \\ Anomaly & 0.9716 & 0.9884 & 0.9799 \\ \hline RUDY & 0.9941 & 0.9883 & 0.9912 \\ Slowloris & 0.9947 & 1.0000 & 0.9973 \\ FIN Scan & 0.9710 & 1.0000 & 0.9853 \\ NULL Scan & 0.9761 & 1.0000 & 0.9879 \\ UDP Scan & 0.9907 & 1.0000 & 0.9953 \\ XMAS Scan & 0.9552 & 1.0000 & 0.9771 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection effectiveness of the proposed approach (SIMARGL2021 dataset).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Class** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{2}{*}{Without Domain Adaption} & Benign & 0.69481 & 0.6295 & 0.66055 \\  & Anomaly & 0.66133 & 0.7235 & 0.69102 \\ \hline \multirow{2}{*}{With Domain Adaption} & Benign & 0.6823 & **0.8450** & **0.7550** \\  & Anomaly & **0.7965** & 0.6065 & 0.6886 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Zero-shot scenario. Model trained on SIMARGL dataset and tested on IoT-23, (with and without domain adaptation).  
represented a few minutes of recorded network flow samples. As shown in Table 4, as few as 100 additional samples produced a significant boost in terms of accuracy.
# Populating the Mix Space: Parametric Methods for Generating Multitrack Audio Mixtures
## 2 Theoretical Framework
### Generating Gain Vectors by Sampling the Mix-Space  
A set of mixes can be generated by choosing points in the mix-space. In selecting a suitable parametric distribution, it is important to note that linear distributions, such as the normal distribution, are not appropriate as the domain in question is not linear but a spherical surface. The statistics of such distributions are described by a number of equivalent terms in the literature, such as circular, spherical or directional statistics. In order to generate points close to a desired position on the  
Figure 4: A time-varying mix can be considered as a path in the mix-space. Here, a random time-varying mix is generated by means of a random walk. (**a**) Random walk in mix-space; Brownian motion, halted after 30 s; (**b**) Random walk from Figure 4a converted to gain-space; (**c**) Time series of gain values for each of the three tracks.  
\((n-1)\)-sphere, points are generated from a von-Mises-Fisher (vMF) distribution. The probability density function of the vMF distribution for a random \(n\)-dimensional unit vector \(\mathbf{x}\) is given by  
\[f_{n}(\mathbf{x};\mu,\kappa)=C_{n}(\kappa)e^{\kappa\eta^{T}\mathbf{x}}\]  
where \(\kappa\geq 0,||\mu||=1,n\geq 2\) and the normalisation constant \(C_{n}(\kappa)\) is given by  
\[C_{n}(\kappa)=\frac{\kappa^{n/2-1}}{(2\pi)^{n/2}I_{n/2-1}(\kappa)}.\]  
Here, \(I_{n}\) is the modified Bessel function of the first kind at order \(v\). The parameters \(\mu\) and \(\kappa\) are called the mean direction and concentration parameter, respectively. The greater the value of \(\kappa\), the higher the concentration of the distribution around the mean direction \(\mu\), resulting in lower variance. The distribution is unimodal for \(\kappa>0\) and is uniform on \(\mathbb{S}^{n-1}\) for \(\kappa=0\). Further details can be found in [12; 13]. The SphericalDistributionsRand ([[https://github.com/yuhuichen1015/SphericalDistributionsRand](https://github.com/yuhuichen1015/SphericalDistributionsRand)]([https://github.com/yuhuichen1015/SphericalDistributionsRand](https://github.com/yuhuichen1015/SphericalDistributionsRand))) code, based on the work of [14], was used to generate points according to a vMF distribution. In the context of audio mixes, \(\mu\) (where \(|\mu|=1\)) represents the mix about which others are distributed, akin to the mean in a normal distribution. The \(\kappa\) term represents the diversity of mixes generated, analogous (but inversely proportional) to variance. An example is shown in Figure 5, where three distributions are drawn from a 2-sphere.
# Towards a Fast, Open-Path Laser Hygrometer for Airborne Eddy Covariance Measurements
## 2 Materials and Methods
### Uncertainty Estimation  
The individual contributions to the laser absorption model provided in Equation (2) can be used to derive an uncertainty budget for this dTDLAS hygrometer. The most relevant uncertainty contribution is the gas temperature, which is measured by calibrated PT100 sensors. Because of possible temperature variations between the sensor and the optical plane of the multipass cell, the relative uncertainty of the gas temperature is estimated to be 0.33% (\(\approx\)1 K). The uncertainty of the pressure is estimated to be 0.2% (\(\approx\)2 hPa). The optical pathlength is determined by the CAD model and is estimated to have an uncertainty of 0.5% (\(\approx\)2.1 cm). The uncertainty of the line strength is provided by the HITRAN database with 3.5% [31]. Inhomogeneities in the H\({}_{2}\)O concentration along the optical path, and between the optical plane and the sample point of an extractive reference instrument used for validation of the dTDLAS are conservatively estimated to be 1.0%. The uncertainty of the line area (integrated term in Equation (2)) is influenced by the tuning of the laser (1%) and the quality of the fit (2%). The combined contribution of both of these parameters is 2.2%. This results in a total uncertainty of 4.5% (k = 1) for the H\({}_{2}\)O amount fraction (concentration), which is similar to comparable dTDLAS hygrometers [25]. The dominating contributors to the total instrument uncertainty are the line strength (65.7%), followed by the line area (fit and tuning) (26.8%) and spatial inhomogeneities (5.3%). The optical pathlength accounts for 1.3%, and the temperature and pressure for 0.6 and 0.2% of the total uncertainty.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 4 Over-the-Air Demonstrations
### Dynamic Spectrum Access  
In the previous section, we demonstrated that WISCANet and the ping-pong application enable rapid validation of dynamic PHY layer techniques. In this section, we further demonstrate that the system also supports validation of dynamic MAC layer techniques by implementing a simple dynamic spectrum access protocol. This implementation is an extension to the same results cited previously [27].  
We deployed several algorithms to automatically detect vacancies in time and frequency and find appropriate time-frequency slots for opportunistic spectral access. B5G and 6G systems demand better techniques for opportunistic time-frequency resource usage. 5G systems already do this by employing Bandwidth Parts and other co-operative access algorithms. The following results demonstrate the utility of WISCANet in quickly developing, implementing, and validating novel MAC layer techniques for B5G and 6G applications.  
In the first over-the-air demonstration [27], we configure 3 spectrum users to periodically transmit at different, fixed carrier frequencies. A fourth user listens to these transmissions for several frames and determines an appropriate center frequency and bandwidth that does not interfere with the other 3 transmitters. This user then executes the \"ping-pong\" communications system in the discovered open spectral vacancy.  
In the second over-the-air demonstration [27], we configure the same 3 spectrum users to periodically transmit at different, fixed carrier frequencies in adjacent frequency bins, such that these 3 users span the majority of the spectrum and do not leave sufficient spectral vacancy for frequency-division-only use. The fourth user listens to the environment for several cycles and selects an available time-frequency slot in which to execute the \"ping-pong\" communications system without interfering with the other users. The chosen window is at the same carrier frequency as one of the other users but does not overlap with the incumbent transmissions. The system architecture and over-the-air results are presented in Figure 14.  
Figure 13: Console output for the ping-pong communications system when noise is artificially injected into the system. Observe the degrading SNR, causing significant errors in the received signal. The PRE then automatically adjusts the communications protocol and leverages the Object Oriented API to swap out software-defined components and change parameters for the subsequent transmission, which is then decoded without error. This demonstration, like the others, is performed over-the-air in our indoor laboratory environment, so this demonstration includes a real channel and real noise sources.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Secure and Resilient Communication Channels
#### 4.1.1 Data Security and Vulnerabilities  
Data security is defined as the first line in the defence-in-depth model. It ensures the security of information exchanges between every actor in the smart grid. Information security in a smart grid is usually guaranteed by secured communication protocol and advanced encryption algorithms, thus avoiding data jamming and data tampering.  
The vulnerabilities and drawbacks of communication protocols used in smart grid are listed in [34]. The comparison between lightweight cryptography algorithms for IoT devices has been presented in [34]. They summarised that, compared to Advanced Encryption Standard (AES) [35], Blowfish, and Data Encryption Standard (DES) [36], the asymmetric algorithm Elliptic Curve Cryptography (ECC) is one of the most studied and secure algorithms because it requires fewer computing resources (capability, energy, and memory) than other algorithms when providing the equivalent security level.  
The Sandia National Lab introduced a network segmentation concept called enclaves to ensure information security [37]. Enclaves separate smart grid networks via system functions, share physical locations, and similar security requirements rather than traditional IT network segmentation. In [38], [PERSON] presented a Software-Defined Networking (SDN) communication architecture, which first separates the network control function from the forwarding data function in the network devices in the communication network layer and then gathers all the collected data in the SDN control layer; finally, it provides the SDN application in the application layer. They utilised SDN to verify there are no loops, black holes, consistent updates, and incremental consistent updates existing in the communication channel to ensure information security.
# Interactive Part Segmentation Using Edge Images
## 4 Experiments
### Comparison to the State-of-the Art Models  
We evaluated the proposed models in two ways to compare them with the existing state-of-the-art models. The first experiment compared the IOU accuracy of object and part segmentation between the proposed models and the existing interactive object segmentation model with the PASCAL VOC [7] dataset. We used the IOG [3] model as the interactive object segmentation model for comparison. During the experiment, the proposed models and the IOG model were evaluated using the same interaction information (two clicks for the bounding box and one click for the object). The code and parameters of IOG were from [[https://github.com/shiyinzhang/Inside-Outside-Guidance](https://github.com/shiyinzhang/Inside-Outside-Guidance)]([https://github.com/shiyinzhang/Inside-Outside-Guidance](https://github.com/shiyinzhang/Inside-Outside-Guidance)) (accessed on 21 August 2021). The second experiment was to compare the IOU accuracy of part segmentation with the model for semantic part segmentation trained with the PASCAL VOC Part [7] dataset and the proposed models. The interactive segmentation models use the input image and interaction information for each part or object, but the semantic part-segmentation model uses only the image as input without additional interaction information. Although the input information of the two types of models is different, a semantic part-segmentation model was added as one of the comparison models to compare the accuracy of part segmentation since, to date, there has been no study evaluating the accuracy of part segmentation with the interactive object segmentation model.
## 2 Field Monitoring
### Monitoring Methods
#### 2.2.2 Burial Chamber Environmental Data Monitoring  
As the female chamber of [PERSON]'s tomb has been preserved by the local museum, the monitoring process mainly focused on the male chamber, and the chamber plan is shown in Figure 2b. Since tourists visit the tomb regularly, the sensors were arranged in the tomb while fully ensuring their concealment. Therefore, the temperature and humidity recording devices and sensors were installed near the tomb and supported by a bracket to ensure the validity of the data recording process while considering aesthetics. Temperature and humidity data of the chamber were collected at 30-min intervals. Notably, points \"#1\" and \"#4\" were arranged at smaller depths, while points \"#2\" and \"#3\" were arranged at larger depths. Points \"#1\" and \"#2\" served as monitoring points at different depths on the same side, points \"#1\" and \"#4\" served as monitoring points at the same depth on different sides, and points \"#1\" and \"#3\" served as monitoring points at different depths on different sides.