esa-sceva/satcom-chunk-collection huggingface dataset:
# Convolutional Neural Network for Measurement of Suspended Solids and Turbidity
## 2 Materials and Methods
### Performance Metrics
#### 2.6.1 Performance Metrics for Classifier Evaluation  
A confusion matrix is used to analyze the performance of a classification tool [36, 37, 38]. Four important terms make up a confusion matrix, which describes the following cases: the cases that were predicted as elements that belong to a class and that actually belong to that class are known as true positives (_TP_). Similarly, the cases of elements that were predicted that do not belong to a class and do not belong to that class are known as true negatives (_TN_). On the other hand, the cases of elements that were predicted to belong to a class but which do not really belong to the class are named false positives (_FP_). Finally, the cases that were predicted not to belong to the class and actually belong to the class are known as false negatives (_FN_). The elements of the confusion matrix are used to calculate the following performance metrics for the evaluation of the classifier:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Algorithm optimizer & SGD \\ Learning rate & 0.0005 \\ Momentum & 0.9 \\ Batch size & 50 \\ Number of epochs & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters used in the training process.  
_Accuracy_ is the percentage of the total number of predictions that were classified correctly and is calculated as:  
\[Accuracy=(TP+FN)/N \tag{1}\]  
where \(N\) is the total number of elements to classify.  
_Precision_ is the ability of the classifier to predict a sample according to what it really is, and is defined as:  
\[Precision=TP/(TP+FP) \tag{2}\]  
_Recall_ is the ability of the classifier to find all the positive samples. In other words, how many examples of positive cases were correctly labeled, and can be written as:  
\[Recall=TP/(TP+FN) \tag{3}\]  
Similar to _Recall_, _Specificity_ is the ability of the classifier to find all the negative samples, and is defined as:  
\[Specificity=TN/(TN+FP) \tag{4}\]  
_F-Score_ is the harmonic mean of _Precision_ and _Recall_, and provides a notion of how precise the classifier is. A high _F-Score_ value indicates that the model performs better in positive cases. It is calculated as:  
\[F\text{-}Score=(2\times Precision\times Recall)/(Precision+Recall) \tag{5}\]  
Receiver operating characteristic (ROC) is a plot of the rate of true positives (_Recall_) versus the rate of false positives (_Specificity_). This graph characterizes the ability of a CNN to identify positive cases as positive, and negative cases as negative. Meanwhile, the area under the ROC curve (AUC) is the probability that a randomly chosen pair of positive and negative cases will be classified correctly.
## 6 Performance Analysis
### Security Performance
Since it is difficult for attackers to trace the source from random locations during a large scale area, we assume that each attacker starts from the sink and tries to trace the sources one after another. Specifically, once a packet forwarding process is monitored within the transmission range of the sink, an attacker starts his tracing. SLP and SLP-E try to extend the time that each attacker takes to locate the phantom locations, so each attacker takes a long time to arrive at different scattered phantom locations. Since the transmission range of each attacker is the same as the sensor, extra time is needed for them to move together for information sharing. In a real situation, once the source detects an event, it will send several messages to the sink over a period of time, so attackers have to trace the source during that period of time. It is difficult for several attackers to work together to locate the source in SLP and SLP-E. There are works focusing on dealing with a large number of collaborative attackers in the global traffic analysis attack. However, it is not reasonable to deploy lots of attackers to monitor all the packet transmissions over a large scale area in the real world. For example, as mentioned before, the Wolong Panda Reserve in China covers about two million square kilometers. For now, we have provided qualitative analysis results in this work, but leave more sophisticated analysis of the issue to our future work.
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 2 Mathematical Models
### ChPN with Orthogonal Chirps  
If the better cross-correlation performance is wanted, we can select the chirp waveforms based on the following equation [12]:  
\[p_{orth}(t)=\sqrt{2}\cos\left(\frac{\pi N}{T_{c}^{2}}\left(t-k\frac{T_{c}}{N} \right)^{2}-\frac{\pi}{4}\right)-\frac{T_{c}}{2}<t<\frac{T_{c}}{2} \tag{15}\]  
where \(N=B_{\text{s}}T_{c}\), and \(k=0,\ 1,\ \dots,\ N-1\). Replacing \(p(t)\) with \(p_{orth}(t)\) in Equation (1), then the ChPN with orthogonal chirps obtained, which is denoted as O-ChPN. Due to the constraint condition, the transmitter number is limited to \(N\), i.e., the maximum number of transmitters is 5 with a \(B_{\text{s}}\) of \(5f_{0}\), and a \(T_{c}\) of \(1/f_{0}\). Both the chirp waveforms and PN sequences are different among the transmitters. The further discussion will be present in Sections 4.5 and 5.
## 5 Performance Evaluation
### Implementation Technical Details
#### 5.2.2 Input Features  
According to our dataset information and the use case requirements, we define the following input features for our MLNN model:  
* [noitemsep,topsep=0 pt]
* segmentid: ID of the parking segment;
* observation time: including _hour_ and _minute_ features which keep the hour and minute for each observation;
* availability level: is calculated based on the number of free parking spots in each segment. In order to define our three availability levels, in this work, two T1 and T2 threshold values are considered equal to 3 and 10 respectively. We selected these values after doing several tests with different values. In this case:
* less than or equal to 3 free places is considered as level L1.
* between 3 and 10 free places is considered as level L2.
* equal or above 10 free places is considered as level L3.
* streetname: name of the related street in the segment;
* startx, starty, endx, endy: WGS84 coordinates of start and end point of parking segment.
## 3 Intrusion Detection Data Sets
### Cic-IDS2017 Data Set  
The CIC-IDS2017 data set [13] comprises unprocessed packet capture traces of network transactions encompassing both legitimate and malicious activities, which is well-suited for the assessment of network intrusion detection methodologies [32; 33]. The data set comprises PCAP and CSV formats, facilitating the analysis of network flows, notably featuring 80 attributes extracted through CICFlowmeter [34], a comprehensive toolchain for processing raw packet capture traces. By profiling 25 distinct categories of user behaviors generated in a virtual environment, it encompasses network traffic derived from HTTP, HTTPS, FTP, SSH, and email protocols, amassed over a span of five days. The data set contains eight distinct attack classifications, namely, Brute Force FTP, Brute Force SSH, Denial of Service (DoS), Heartbleed, Web Attack, Infiltration, Botnet, and Distributed Denial of Service (DDoS).
## 3 Extraction of Bistatic RCS from Passive Radar Measurements
### Target Detection and Tracking  
The extraction of the target's signal return levels was performed after a detection stage involving a two-dimensional cell-averaging (CA) CFAR detector with false alarm probability \(P_{fa}=10^{-5}\). This step was followed by a Kalman filter-based tracking stage [29] which processed plot information in the range and Doppler domains. To ensure that target returns were correctly associated to the aircraft, target tracking was applied simultaneously to the synchronized IMU ground truth data which was transformed into the corresponding measurement domain. The resulting track heads from the reference and measurements were compared in terms of their error ellipses derived from their track's respective covariance matrices. A preset gate determined whether the ground-truth could be associated to a given radar track. A side by side comparison of associated tracks in the bistatic range and the Doppler domain is shown in Figure 5.  
In terms of root-mean-square (RMS) error, the deviation in bistatic range \(\Delta r_{B,RMS}\) and bistatic Doppler \(\Delta f_{D,RMS}\) was 14.2 m and 3.9 Hz, respectively. As can be seen from Figure 5, the target track was lost at higher bistatic ranges due to greater path loss and during measurement times around 180 s when the aircraft was performing a turn with higher acceleration. This signal loss can be attributed to a reduction of coherent integration gain due to signal dispersal when the target migrated through multiple range and Doppler cells during a coherent processing interval. Together with signal leakage effects, this led to a spillover into multiple neighboring resolution cells. Figure 6 illustrates this effect on the RDM for each of the 11 receiving antennas of the LORA-11 system. The maps were centered around the cell associated with the target track head and cropped to highlight the energy dispersal effect.  
Figure 4: Cumulative RDM over 129 seconds. The radar track signature of the PC-12 is clearly visible in black. The vertical line at zero-Doppler corresponds to the stationary multipath. Map grayscale-scheme represents dB scale.  
Two comments are in order. Firstly, it is apparent that the spillover of received signal energy over multiple adjacent range/Doppler cells requires proper clustering to accumulate all the spread energy (see also Equation (2) below). Secondly, as briefly mentioned above, the measured signal power was different for each receiving antenna despite calibration. This variation can be reduced by averaging the different antennas' measurements.
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 5 Conclusions  
Planar circular photodiode arrays provide a cost-efficient method to measure bearings for indoor localization. In the measurements presented here, modulated infrared LED sources are used for the beacons, and the sensor is a set of photodiodes (channels), equally arranged on the surface of a cylinder. The measurement process provides amplitude estimates for each beacon at each channel, from which the bearing estimation process provides the azimuth estimates.  
A novel fast frequency domain bearing estimation method was proposed. The advantage of the proposed solution is that it has much lower computational complexity than the least squares method. The disadvantage of the fast solution is that the accuracy is somewhat decreased: according to the measurements, the fast method is more sensitive to reflections.  
The application of the proposed system requires that the transmitters and the PCPA be at the same plane. Tilting of the sensor, or uncontrolled elevation of the transmitters may cause additional error. According to measurements, the effect of this error source is negligible if the tilting of the sensor or, equivalently, the elevation of the beacon, remains below \(10^{\circ}\).  
The effect of reflections was also studied. The results show that the method is sensitive to reflections, reflective surfaces in the vicinity of 1 m can cause severe degradation of the estimates. Real measurement results indicate that in realistic circumstances the accuracy of the bearing estimation is approximately \(1^{\circ}\) for the least squares and \(2^{\circ}\) for the fast method.  
The applicability of the measurement method for indoor localization was also demonstrated. In a 5 m by 4 m office the mean localization error was 8 cm and 16 cm for the least squares and the fast methods, respectively, using four beacons.  
Conceptualization, G.Z. and G.S.; methodology, G.S., G.Z., and G.V.; software, G.Z. and G.V.; validation, G.Z., G.V. and G.S.; writing--original draft preparation, G.Z., G.V., G.S.; writing--review and editing, G.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 2 Materials and Methods
### Point Cloud Registration  
#### 2.3.1 Coordinate System Construction  
There are three lines of intersection for the three orthogonal facets of the scanned dice. These lines of intersection can be represented by vectors and thus used to construct the axes of a newly defined right-handed coordinate system, the die space. For example, the cross product of the normal vectors of the Pips 1 and 3 facets forms the X-axis (X\({}_{\text{d1}}\)) of the coordinate system for the dice scanned at Station 1, as illustrated in Figure 7a. The Y-axis (Y\({}_{\text{d1}}\)) and Z-axis (Z\({}_{\text{d1}}\)) are formed in a similar way. When the scanner is placed to scan the opposite facets of the die (Station 2), another coordinate system can be formed with the axes X\({}_{\text{d2}}\), Y\({}_{\text{d2}}\), and Z\({}_{\text{d2}}\), as shown in Figure 7b.  
However, since the die is not perfectly cubical, the three axes of the two constructed coordinate systems in the die space are not mutually orthogonal. The axis orthogonality can be corrected to form a new coordinate system (\(V_{x}\)-\(V_{y}\)-\(V_{z}\)) by first fixing the Z-axis. One of the axes should be first fixed. When the Z-axis is fixed, the orthogonality between the Z and X axes, and between the Z and Y axes, can be subsequently corrected. Denoting the axis in vector form, we have:  
\[\vvet{V_{z}}=\vvet{Z_{d_{1}}} \tag{1}\]  
and then estimating the vector for the new X-axis (\(V_{x}\)) by applying the cross-product [30],  
\[\vvet{V_{x}}=\vvet{Y_{d_{1}}}\times\vvet{V_{z}} \tag{2}\]  
so that \(V_{x}\) must be orthogonal to \(V_{z}\). Finally, estimating the vector for the new Y-axis (\(V_{y}\)) by applying the cross-product again,  
\[\vvet{V_{y}}=\vvet{V_{x}}\times\vvet{V_{z}} \tag{3}\]  
Figure 7: Coordinate system constructions for the die spaces. (**a**) Simulated axes. (**b**) After the axis orthogonality correction.  
Similarly, the axis orthogonality of X\({}_{\rm d2}\)-Y\({}_{\rm d2}\)-Z\({}_{\rm d2}\) coordinate system can be corrected in same way, forming the new \(U_{x}\)-\(U_{y}\)-\(U_{z}\) system, as shown in Figure 7b. The next step is to find out the relative position and orientation between the \(V_{x}\)-\(V_{y}\)-\(V_{z}\) and \(U_{x}\)-\(U_{y}\)-\(U_{z}\) coordination systems, and then identify three pairs of conjugate points along the axes to complete the registration.
## 2 Material and Methods
### Qualitative Analysis of Aroma Compounds
#### 2.4.3 Compound Identification  
Background subtraction was first performed using the MassHunter B.06.00 software. Metabolite identification was performed manually by comparing retention times and accurate mass spectra (mass difference of less than 5 ppm and two ions) to those of the standards, when available. Tentative annotation of the chromatographic peaks, without a standard, was made by using spectral features (mass difference of less than 5 ppm theoretical value, at least one indicative fragment and isotopic pattern), literature information about chromatographic properties, mass spectra records from external databases such as The National Institute of Standards and Technology (NIST), The Human Metabolome Database (HMDB), MassBank and an internal database for the wine volatiles based on the literature, and by comparing the Kovats retention indices (RI) in NIST database and published literatures. Deconvolution supported by MassHunter B.06.00 software was performed to extractpossible components in a peak if no match was found. For example, \(\beta\)-phellandrene was detected at 18.75 min. The average spectrum of the peak at 18.75 min extracted by conventional manual background subtraction (Figure 1a) could not match with any known compound in the library, due to the co-elution. After deconvolution, ions like \(m/z\) 93.0704, 79.0533, and 65.0371, etc., having similar peak shapes at 18.75 min, were grouped to reconstruct a deconvoluted spectrum (Figure 1b), which could be tentatively identified as \(\beta\)-phellandrene by comparing the spectrum with the NIST library (Figure 1c) and by comparing the accurate mass of the ions from the predicted spectrum (Figure 1d). The compound was further confirmed by comparing the calculated RI with the RI in the published literature (Table 2). Figure 1: Example of \(\beta\)-Phellandrene detection in a blueberry wine sample (**a**) The average spectrum of \(\beta\)-Phellandrene extracted by conventional manual background subtraction; (**b**) the deconvoluted spectrum of \(\beta\)-Phellandrene. (**c**) \(\beta\)-Phellandrene spectrum from NIST library; (**d**) Predicted GC-MS Spectrum-GC-MS (Non-derivatized)-70 eV, Positive (HMDB0036081), obtained from The Human Metabolome Database (HMDB).
# Adaptive Multi-Grained Buffer Management for Database Systems
## 3 Adaptive Multi-Grained Buffer Management
### Adjustment of the P-Buffer  
One key issue in AMG-Buffer is to determine the appropriate size of the P-Buffer. Allocating a large P-Buffer is not effective if most pages in the F-Buffer have a low clustering rate. On the contrary, if most pages have a high clustering rate, we need to allocate a large P-Buffer.  
The F-Buffer is used to collect history access information and determine where the page belongs. Thus, the size of the F-Buffer is static in AMG-Buffer. We denote the memory space of both the P-Buffer and the S-Buffer as \(M\). Next, we let \(W\) be the time window for adjustment and \(S\) be the migration unit for tuning the size ratio of P-Buffer and S-Buffer. \(M_{p}\) denotes the size of the P-Buffer. Then, the proportion of the P-Buffer is \(M_{p}/M\). The basic idea of the tuning algorithm is that the buffer absorbing new tuples frequently requires more memory space to the cache. Thus, we count the number of pages that enter the P-Buffer, which is denoted as \(Ep\). Similarly, the number of pages that enter the S-Buffer in the time window is \(Es\).  
Here, we set two thresholds to trigger the adjustment, namely, \(T_{min}\) and \(Tmax\). If \(|M_{p}/M-Ep/E|\) is below \(T_{min}\), we do not take any actions to resize the P-Buffer. To avoid frequent adjustments, we extend \(W\) to \(2W\) (but not exceed \(W_{max}\)) and shorten \(S\) to \(S/2\) (but not less than \(S_{min}\)). If \(|M_{p}/M-E_{p}/E|\) is more than \(T_{max}\), we first resize the P-Buffer, and then we shorten \(W\) to \(W/2\) (but not less than \(W_{min}\)) and extend \(S\) to \(2S\) (but not more than \(S_{max}\)). Then, we resize the P-Buffer as well as the S-Buffer by adding or removing \(S\) memory space. Algorithm 3 summarizes the process of adjusting the size ratio of the P-Buffer and the S-Buffer in AMG-Buffer.  
Our experimental results show that the proposed adjusting algorithm can make AMG-Buffer adaptive to the change of access patterns. For example, when the workload changes from requesting the pages with a high clustering rate to requesting the pages with a low clustering rate, our algorithm can dynamically adjust the size of the P-Buffer and the S-Buffer to make the whole buffer work efficiently for the current workload. In particular, when requests focus on the pages with a high clustering rate, we will increase the size of the P-Buffer; otherwise, we will increase the size of the S-Buffer.  
The current adjusting scheme is actually an empirical algorithm. Although our experiments suggest the effectiveness of the algorithm, it is more attractive to employ a machine-learning algorithm [29; 30] to make AMG-Buffer learn the access pattern, which can be further used to adjust the size of the P-Buffer.
## 2 Materials and Methods
### Drug Loading and In Vitro Release Studies  
Drug incorporation in hydrogel was performed by dissolving 40 mg of amoxicillin (AM) in 10 mL Plx-g-CMP solution (in PB at room temperature) with two concentrations (13 and 22%, \(w/v\)). Then, samples of 1 mL Plx-g-CMP solution with a solubilized drug were prelevated and placed in separate vials for gelling at 37 \({}^{\circ}\)C. The release studies were performed according to [PERSON] et al. (2003) [PERSON] et al. (2003) [PERSON] et al. (2003). The vials were filled with 10 mL of PB and the temperature and stirring rate were maintained at 37 \({}^{\circ}\)C and 50 rpm, respectively. Samples of the receiving buffer (1 mL) were withdrawn at different time intervals and the drug content was determined by spectrophotometric analysis using an Evolution 201 UV spectrophotometer (Thermo Fisher Scientific Inc., Madison, WI, USA) based on a standard curve of the pure drug in PB at 228 nm (y = 0.0219x; R\({}^{2}\) = 0.9993). The same volume of the fresh receiving buffer was added to replace the volume of the withdrawn samples.  
The release kinetics of AM from Plx-g-CMP hydrogels were analyzed by the following mathematical models:  
Zero-order:  
\[\frac{M_{t}}{M_{\infty}}=k_{0}\,t \tag{3}\]  
Higuchi:  
\[\frac{M_{t}}{M_{\infty}}=k_{H}t^{1/2} \tag{4}\]  
Korsmeyer-Peppas:  
\[\frac{M_{t}}{M_{\infty}}=kt^{n} \tag{5}\]  
Peppas-Sahlin:  
\[\frac{M_{t}}{M_{\infty}}=k_{1}t^{m}+k_{2}t^{m} \tag{6}\]  
where \(M_{t}/M_{\infty}\) is the fraction of drug released at time \(t\), \(k_{0}\) and \(k_{H}\) represent the zero-order and Higuchi release kinetic constants, respectively, \(k\) is a constant defined by the structural and geometric characteristic of the dosage form and \(n\) is the release exponent indicative for the drug release mechanism [48]. For cylindrical geometries, \(n\) = 0.45 suggests Fickian diffusion, \(n\) = 0.9 refers to a non-Fickian with the characteristic of zero-order release, if 0.45 \(<n<\) 0.9 the transport process is anomalous with a superposition of diffusion and swelling controlled drug release. If \(n\) < 0.45, a pseudo-Fickian diffusion with a slow release occurs [49]. In the Peppas-Sahlin model [50], \(k_{1}\) and \(k_{2}\) are the kinetic constants related to the Fickian and non-Fickian diffusional contributions, respectively.  
For establishing the model that statistically best represents the drug release mechanism, the Akaike number, Akaike Information Criterion (_AIC_), [51] defined by Equation (7) was used in the discrimination analysis.  
\[AIC=N\times\ln(SSR)\ +\ 2\times p \tag{7}\]  
where \(N\) is the number of experimental data, \(SSR\) is the square residual sum and \(p\) is the number of parameters in the model.