esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Spoofing Detection Based on Wavelets and Machine Learning
## 3 Methodology
### Proposed Approach  
The proposed integrated approach consists of two steps: (1) the application of the discrete wavelet transform to selected datasets, (2) the application of selected machine learning models to the generated datasets. Figure 1 shows the block diagram of the proposed approach. Firstly, in-phase and quadrature (I/Q) components are extracted from each OAKBAT dataset. The data are split into segments to facilitate the implementation of the algorithm's calculations on limited resources. After that, a level 1 discrete wavelet transform is applied to the I/Q components, and four types of coefficients (approximation, horizontal, vertical, and diagonal) are extracted. As a result of the first step, the generated datasets based on different types of input classification data are obtained. Three types of generated data (images, spectral and statistical features, and image processing and texture analysis features) are used as the input data in machine learning classifiers, including SVM, KNN, and RF. k-Fold cross-validation is applied on all machine learning models. The result of the second step is a signal-type classification (class/output of the model), hence the model return as output whether the signal is authentic or fake. All models are evaluated using standard model performance parameters such as confusion matrix, accuracy, precision, recall, and F1-score.
## 5 Simulation Results
### Testing of the Proposed Control Strategy  
This section tests the proposed GPC-HGPC coordinated controller for the MGT-CCP system. The parameters of the proposed controller are set as follows: for linear GPC controllers, prediction horizon \(N_{1}=20\), control horizon \(M_{1}=10\), error weight coefficient \(q_{1}=1\), control weight coefficient \(r_{1}=0.8\); for the Hammerstein-GPC controller, prediction horizon \(N_{2}=600\), control horizon \(M_{2}=20\), error weight coefficient \(q_{2}=1\), control weight coefficient \(r_{2}=0.1\).  
The PID controller and MPC control are compared with the proposed controller. The parameters of the PID controller are as follows: for the power supply system, the proportional gain \(k_{\rm p}=8\times 10^{-8}\), the integral gain \(k_{\rm i}=8.17\times 10^{-9}\), the differential gain \(k_{\rm d}=0\); for the cooling system, the proportional gain \(k_{\rm p}=197.57\), the integral gain \(k_{\rm i}=1.45\), the differential gain \(k_{\rm d}=1183.39\). Note that the PID controller is optimally tuned using the _Tuner_ function of the PID module provided by Simulink.  
The parameters of the MPC controller are as follows: prediction horizon \(N=39\), control horizon \(M_{1}=5\), sampling time \(t_{s}=20s\), control weight coefficient \(Q_{u}=diag(0.1377,0.1377)\), control rate weight coefficient \(Q_{\Delta u}=diag(0.0726,0.0726)\), output weight coefficient \(Q_{y}=diag(0.0689,38.5596)\).  
The control structure of the comparison methods is shown in Figure 10.  
In order to quantitatively compare the load tracking performance of different controllers, the integrated absolute error (IAE) is used in the following simulations. IAE is given by:  
\[\text{IAE}=\int_{0}^{\infty}\lvert ref(t)-y(t)\rvert dt \tag{25}\]
# Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
## 2 Literature Review
### Fusion-Based Detection Networks  
Fusion networks can be classified into three types: feature level, decision level, and sensor level. Owing to its superior performance, feature-level fusion is the commonly used approach that extracts and combines different features from point clouds and RGB images [46]. CenterFusion [47] network utilizes a 2D detection network to operate on RGB images to extract feature maps followed by a voxel feature encoder. This differs from the traditional approach by following a center-based method for 3D object detection. The network aims to predict 3D object centers in all dimensions. In cases of noisy data, this approach makes the detection process robust. On the other hand, the PointFusion [48] network utilizes RGB images, point cloud information, and GPS/IMU data for localization tasks. 3D CNN layers are used as voxel encoders, and 3D CNN for feature extraction on images. This network follows a multi-model fusion approach where features from various layers are fused to develop final network prediction. RadarNet [49] introduces early fusion and late fusion mechanisms to gather additional information on the radial velocity of an object and obtain more information. To address the sparsity of the RADAR point cloud, CRF-Net [50] projects the radar point clouds on the image plane and launches vertical lines at a height of 3 m, creating additional point cloud information along the lines. MVF-Net [51], and Part-\(A^{2}\) Net [52] fuse information from multiple cameras and LIDAR sensors using multi-view feature fusion. To address the sparsity when utilizing LiDAR point cloud information, SalsaNet [53] was proposed. STD network [54] uses a sparse to dense feature extraction on dense image features and sparse LiDAR information. PFF3D [55] offers an early-fusion method to extract features from LiDAR and camera data using a single backbone structure. The authors of this research, AVOD-FPN [56], propose an RPN structure utilizing multiple modalities to produce high-recall proposals for small classes. This approach aims to increase the detection performance of small objects. The sequential fusion method proposed in PointPainting [57] involves projecting point clouds onto an image-only segmentation network and adding class scores. Fast-CLOCs [58], as the name suggests, is a high-speed fusion detector system that introduces a 3D detector-cued image detector to address limited computation resources in self-driving vehicles. Frustum-PointPillars [59] explores the pillar representations and adopts an early fusion approach.
## 3 Methods
### Data Processing  
#### 3.1.1 Geographical Information Data Processing  
Due to the differences in data sources, spatial resolutions, and projection coordinates, preprocessing operations are applied to all data in order to mitigate the impact of different image resolutions and coordinate references on modeling. These operations mainly include following aspects. (1) Unified Coordinate System: To ensure consistent area preservation during calculations, all data are transformed into the Asia North Albers Equal Area Conic projection coordinate system. (2) Unified Spatial Resolution: For data with spatial resolutions smaller than 1 km, bilinear interpolation is employed to up sample the data to a 1 km resolution.  
In order to transform vector data into quantifiable and meaningful spatial features, we perform density calculations on traffic network data and water system data. Through these operations, we obtain the aggregation degree of traffic and water systems in the kilometer grid [30] (in this article, represented as D_railway, D_Road, and D_Hydrological). The specific calculations are as follows:  
\[D_{i}=\frac{L_{i}}{S_{i}} \tag{1}\]  
In Formula (1), \(D_{i}\) is the network density of line vector data in region \(i\), \(L_{i}\) is the sum of the length of line data in region \(i\), and \(S_{i}\) is the land area of region \(i\).  
Normalized Vegetation Index (NDVI) data is calculated based on the Google Earth Engine (GEE) platform. It involves computing the NDVI index for each time period using all Landsat 8 Surface Reflectance (SR) data from the year 2020. The annual maximum NDVI is derived using the Maximum Value Composite method [49], and this parameter is employed to describe the vegetation coverage in Sichuan Province.  
\[NDVI_{max}=MAX(NDVI_{1},NDVI_{2},NDVI_{3},\dots,NDVI_{n}), \tag{2}\]  
\(NDVI_{max}\) is the maximum NDVI value of the pixel, and \(NDVI_{n}\) indicates the NDVI value at the same pixel on the nth NDVI image of the year 2020.
## 4 Experimental Results
### Experimental Results on AVD8 Dataset  
Figure 11 demonstrates the average time for different feature extraction methods along with proposed PTHOG without TOP-K key frame extraction on AVD8 dataset. PTHOG requires 29,780.52 milliseconds without TOP-K key frame extraction. From these experiments, we can see that PTHOG outperforms SIFT [5] and STPM [22] significantly, while it requires almost similar amount of time to LAID [30], LBP [31] and HOG [28].  
Figure 12a demonstrates the confusion Matrix for the proposed method on the AVD8 dataset for image query while Figure 12b illustrates the confusion Matrix for the proposed method on the AVD8 dataset for video query. For both the image and video queries Santa-Claus and Dog categories show the best result among all the other categories, while Wolf and Tiger categories shows the lowest mean average precision, since these two categories virtual object looks almost similar in shape to each other.  
Figure 13a illustrates the comparison between the proposed method and other existing approaches on the AVD8 dataset for image query and Figure 13b shows the comparison between the proposed method and existing approaches on the AVD8 dataset for video query. The Proposed feature descriptor PTHOG shows 92.87% mean Average Precision (mAP) for image queries and 93.7% mean Average Precision (mAP) for video queries on the AVD8 dataset. From these experiments, we can see that, proposed feature extraction method PTHOG outperforms LAID [30], LBP [31] and HOG [28]  
Figure 11: Average time for feature extraction methods without TOP-K key frame extraction on AVD8 dataset.  
Figure 10: Retrieval results for UCSB dataset: (**a**) Query image and (**b**) Retrieved top-4 resultant videos from database.  
significantly for both image and video queries, while it shows the competitive result with SIFT [5] and STPM [22]. Deep learning based method ImageNet [32] shows the best performance along all, due to its discriminative power. Temporal information is taken in account for STPM [22] and TCSIFT [21], whereas deep spatial information is obtained for ImageNet [32]. However, SIFT [5], STPM [22] and ImageNet [32] require much more computational time than the PTHOG, so these approaches cannot be employed in this application domain, since we need real time interaction. Similar to the Figure 10, Figure 14 also shows the retrieval results for the AVD8 dataset on 3 selected categories and in this experiment the query is an image.  
Figure 14: Retrieval results for AVD8 dataset: (**a**) Query image and (**b**) Retrieved top-4 resultant videos from database.  
Figure 12: (**a**) Confusion Matrix for proposed method on AVD8 dataset for image query and (**b**) Confusion Matrix for proposed method on AVD8 dataset for video query.  
Figure 13: (**a**) Comparison with existing approach on the AVD8 dataset for image query and (**b**) Comparison with existing approach on the AVD8 dataset for video query.
# Secure Control of Linear Controllers Using Fully Homomorphic Encryption
## 3 Preliminaries
### BFV Encryption  
It is assumed that the security-parameter-related noise distribution \(\chi\) is a discrete Gaussian distribution on the ring \(R\), and that the uniform random noise distribution \(\chi^{\prime}\) is also on the ring \(R\). The seven probabilistic polynomial time (_PPT_) algorithms (**SecretKeyGen**, **PublicKeyGen**, **EvaluateKeyGen**, \(\mathbf{Enc},\mathbf{Dec},\mathbf{Add},\mathbf{Mult}\)**) used in BFV encryption are as follows:  
* \(\mathbf{SecretKeyGen}(1^{\lambda})\): input security papameter \(\lambda\), sample \(\mathbf{s}\gets R_{2}\), and output secret key noted as \(sk=\mathbf{s}\).
* \(\mathbf{PublicKeyGen}(sk)\): input secret key, sample \(\mathbf{a}\gets R_{q}\), \(\mathbf{e}\leftarrow\chi\), and output public key \[pk=([-(\mathbf{a}\cdot\mathbf{s}+\mathbf{e})]_{q},\mathbf{a}).\]  
* Version 1: parameters \((sk,T)\): for \(i=0,\cdots,l=\lfloor log_{T}(q)\rfloor\), sample \(\mathbf{a_{i}},R_{q},\mathbf{e_{i}}\leftarrow\chi\), perform the following operation, and return \[rlk=[([-(\mathbf{a_{i}}\cdot\mathbf{s}+\mathbf{e}_{i})+T^{i}\cdot\mathbf{s}^ {2}]_{q},\mathbf{a_{i}}):i\in[0\cdots l]].\]
* Version 2: parameters \((sk,p)\): sample vectors \(\mathbf{a}\gets R_{p\text{-}q}\), \(\mathbf{e}\leftarrow\chi^{\prime}\), and then return \[rlk=([-(\mathbf{a}\cdot\mathbf{s}+\mathbf{e}+p\cdot\mathbf{s}^{2}]_{p\text{-}q },\mathbf{a}).\]
* \(\mathbf{Enc}(pk,\mathbf{m})\): to encrypt a message \(m\in R_{t}\). We set \(\mathbf{p}_{0}=pk[0]\), \(\mathbf{p}_{1}=pk[1]\) and sample \(\mathbf{u}\in R_{2}\), \(\mathbf{e}_{1},\mathbf{e}_{2}\in\chi\), then return the final ciphertext  
Figure 2: Graphical representation of fully homomorphic encryption\[ct=([\mathbf{p}_{0}\cdot\mathbf{u}+\mathbf{e}_{1}+\Delta\cdot\mathbf{m}]_{q},[ \mathbf{p}_{1}\cdot\mathbf{u}+\mathbf{e}_{2}]_{q}).\]
* \(\mathbf{Dec}(sk,ct)\): set \(\mathbf{c}_{0}=ct[0],\mathbf{c}_{1}=ct[1]\) and compute and output the result of the decryption \[m^{\prime}=[\lfloor\frac{t\cdot[\mathbf{c}_{0}+\mathbf{c}_{1}\cdot\mathbf{ s}]_{q}}{q}\rfloor]_{t}\).
* \(\mathbf{Add}(ct_{1},ct_{2})\): return \[([ct_{1}[0]+ct_{2}[0]]_{q},[ct_{1}[1]+ct_{2}[0]]_{q}),\] where \(ct_{1}=\mathbf{Enc}(pk,\mathbf{m_{1}}),ct_{2}=\mathbf{Enc}(pk,\mathbf{m_{2
## 3 Alkaloids
### Oxindole Alkaloids
#### 3.2.2 Pentacyclic Osmidole Alkaloids  
The pentacyclic oxindole alkaloids present in _U. tometosa_ are shown in Figure 3 (**21**-**22**). These compounds possess the identical oxindole group linked to an indolizidine with an additional di-hydropyran structure fused to the six-member ring of the indolizidine structure. This ring structure is substituted with a methyl group at C-19 and an acetyl group branching from the unsaturated bond from the pentacyclic heterocycle. These compounds have been identified in alkaloid studies of _U. tometosa_[10, 50]. Structural determinations were established by comparison with NMR datasets [66, 67]. A single \(N\)-oxide exists from these groups of compounds, Uncarine F \(N\)-oxide (**22**), which is like other \(N\)-oxides of _U. tometosa_ (**17** and **18**).  
The bioactivity of these compounds has been the focus of many investigations. A recent review focusing on the bioactivity of components and extracts from _Uncaria tometosa_ cites a number of sources [13]. A few of the bioactivities referenced include anticancer activity for compound **21e**[68], antineoplastic activity for **21c**[69], antioxidant activities for **21f** and **21g**[70, 71], anti-inflammatory activity for **21f**, and immunomodulating properties for **21b**[72]. The antimicrobial activity of these compounds has not been as thoroughly investigated as other bioactivities, although a few examples of antibacterial evidence were found during the literature search and are presented below.  
Figure 3: Structures of oxindole alkaloids (**15**–**22**). Structures boxed in red indicates compounds mistakenly attributed to _U. tometosa_ in previous sources.  
The stereoisomers **21f** and **21g** demonstrated in vitro activity against two tuberculosis causing mycobacteria strains resistant to the antibacterial medication isoniazid. Isolated from the leaves of _Hallea rubrostipulata_ ([PERSON].) [PERSON] and tested using a broth microdilution method, the results indicated moderate antimycobacterial activity. Compound **21f** demonstrated MIC values of 800 and 400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL against _Mycobacterium madagascariense_ and _M. indicus pranii_, respectively. Compound **21g** demonstrated MIC values of 800 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for both _M. madagascariense_ and _M. indicus pranii_[73].  
One of the few examples of compounds directly isolated from _Uncaria tometosa_ displaying antibacterial activity was reported by [PERSON] and colleagues. The oxindole **21d** isolated from a methanolic extraction of _U. tometosa_ demonstrated moderate in vitro antibacterial activity against Gram-positive bacteria _Staphylococcus aureus_ and Gram-negative _B. subtilis_, with MIC values of 150 and 250 \(\mathrm{\SIUnitSymbolMicro g}\)/mL [74].
## 1 Introduction  
Due to the increasing number of medical cyclotrons and the growing demand for more specialized radio tracers, an increasing number of sites are producing radioiodine, namely \({}^{123}\)I and \({}^{124}\)I. While \({}^{131}\)I is still the workhorse in diagnostics of thyroid disease, \({}^{123}\)I (\(T_{\
icefrac{{1}}{{2}}}\) = 13.2 h) is increasingly used instead,due to better diagnostic features. \({}^{123}\)I has a better suited decay energy of 159 keV _vs._ 364 keV for \({}^{131}\)I, providing higher sensitivity in SPECT due to thinner camera collimators [1]. In addition, since \({}^{123}\)I has no \(\beta\) component, it delivers significantly less dose to the thyroid tissue. This minimizes the risk of thyroid stunning, a phenomenon rendering the thyroid tissue unable to take up iodine, and thus complicates subsequent radiotherapy [2, 3]. \({}^{124}\)I (\(T_{\%}=4.18\) days) has its own merits as a radionuclide for diagnostics. With a half-life of more than 4 days it allows PET studies of processes with slow biokinetics, as well as allowing for more complicated and time consuming labeling synthesis [4, 5, 6] (Table 1).  
A method for producing \({}^{123}\)I and \({}^{124}\)I available to many cyclotron labs, is bombardment of tellurium dioxide and production of iodine via (p,n) reactions. There are several advantages to this approach. Both \({}^{123}\)Te(p,n)\({}^{123}\)I and \({}^{124}\)Te(p,n)\({}^{124}\)I have maximum cross sections below 14 MeV [8], which is within reach of many medical cyclotrons, and only a general solid target setup is needed. The downside is the price of the enriched target material (10-50 $/mg). The entire optimization process associated with setting up an in-house production of iodine can however be done using natural tellurium, in order to keep development costs low. This paper summarizes the findings from research carried out at Herlev Hospital during this optimization process.
## 4 Discussion
### Adaptation Mechanism  
The possibility of adjusting the width of the clock phase is an important feature, which is useful from the point of view of the adaptation mechanism. This time should be set so that the adaptation system can perform all necessary operations. At the end of a given clock phase (falling edge), a given weight, \(i\), in a given neuron, \(j\), is updated, which means that \(w_{i,j}(k)\) is substituted with a new signal \(w_{i,j}(k+1)\). For larger signal resolutions, the data processing time is slightly longer.  
To facilitate the presentation of the results obtained on the basis of the simulations of the adaptation mechanism, the resolutions of the \(x\) and the \(w\) signals have been set to 4. It is sufficient to verify the concept of the circuit. Nevertheless, simulations were carried out also for the resolutions of 8 and 16 bits. Any resolution may be obtained easily by an extension of the lengths of the used summing and subtracting circuits.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & \(f_{\text{max}}[\text{GHz}]\) & \(P[\text{mW}]\) & \(V_{\text{DD}}[\text{V}]\) & **FOM [1/nJ]** & **Technology CMOS** \\ \hline
[30] & 1.2 & 34 & 1.8 & 0.035 & 0.18 \(\mu\)m \\
[32] & 40 & 45 & 1.0 & 0.89 & 0.09 \(\mu\)m \\
[33] & 0.125 & 32 & 1.8 & 0.004 & 0.18 \(\mu\)m \\
[34] & 1.8 & 86.6 & 3.3 & 0.021 & 0.35 \(\mu\)m \\
[35] & 2.0 & 21 & 1.2 & 0.095 & 0.13 \(\mu\)m \\ This work & 0.15 & 0.06 &In all simulations, to facilitate the interpretation of the results, the equivalent decimal values are also provided, which correspond to their 4-bit counterparts. It should be noted that the \(\Delta w(k)\) term is negative when \(x<w\). In these situations, the 4-bit binary signal is expressed in two's complement code.
## 3 The Proposed \((k,n)\)-XVC Scheme with Adjustable Pixel Expansion
### The Proposed Scheme  
Given a \((k,n)\)-OVC scheme with basis matrices \(B_{0}\) and \(B_{1}\), its pixel expansion is \(m\). The scheme meets the contrast and security conditions:  
\[H(\oplus(B_{1}|U)) \geq h\text{ and }H(\oplus(B_{0}|U))\leq l,\ |U|\geq k, \tag{1}\] \[H(\oplus(B_{1}|U)) =H(\oplus(B_{0}|U)),\ |U|\leq k-1, \tag{2}\]  
where \(0\leq l<h\leq m\). The contrast is \(\alpha=(h-l)/m\). According to the contrast condition, it is known that \(\alpha m=\frac{h-l}{m}m=h-l\geq 1\).  
Next, we will give the construction of \((k,n)_{m^{\prime}}\)-XVC scheme, the shadows of the proposed scheme is presented in Construction 1.  
**Construction 1:** the proposed \((k,n)_{m^{\prime}}\)-XVC scheme.  
**Input:** the secret image \(S\), the basis matrices \(B_{0}\), \(B_{1}\) and \(m\) of a \((k,n)\)-OVC scheme.  
**Output:** shadows \(S_{i},i=1,2,\ldots,n\).  
**Construction procedure:** 1. The dealer takes a expected pixel expansion \(m^{\prime}\in[m-2^{k-1}m\alpha+1,m]\);  
2. When encoding a white (resp. black) pixel, the dealer randomly chooses a \(n\times m^{\prime}\) column matrix in \(B_{0}\) (resp. \(B_{1}\)), and do random column permutation on it. The resulting matrix is denoted as \(B_{0}^{\prime}\) (resp. \(B_{1}^{\prime}\)). The \(i\)-row of \(B_{0}^{\prime}\) (resp. \(B_{1}^{\prime}\)) is assigned to a relative shadow \(S_{i}\), where  
\[B_{0}^{\prime}=B_{0}[i_{1},\ldots,i_{m^{\prime}}],\ \ B_{1}^{\prime}=B_{1}[j_{1}, \ldots,j_{m^{\prime}}].\]  
Here, the shadow \(S_{i}\) is sent to participant \(U_{i}\); And the \(B_{0}[i_{1},\ldots,i_{m^{\prime}}]\) denotes the any \(m^{\prime}\) columns of \(B_{0}\). \(B_{1}[j_{1},\ldots,j_{m^{\prime}}]\) denotes the another any \(m^{\prime}\) columns of \(B_{1}\). The chosen columns and their orders are completely independent.
# The Role of Surveillance Imaging in Patients with Advanced Laryngeal Cancer
## 1 Introduction  
Laryngeal cancer is the second most common malignancy of the upper aerodigestive tract [1]. Up to 30% of patients treated for laryngeal squamous cell carcinoma (SCC) are subsequently diagnosed with recurrence [2]. Among recurrent head and neck SCC, patients with laryngeal cancer are considered to have the best prognosis, with salvage rates ranging around 50%, including a relative favorable functional outcome [3, 4]. Around 90% of recurrences in head and neck SCC occur during the first two years after treatment [3, 5]. Hence, a close follow-up of laryngeal SCC survivors is crucial, especially during the first two years, in order to provide timely diagnosis in the case of recurrent disease and enable the best chances for salvage.  
Clinical surveillance by physical examination of patients treated for laryngeal SCC, either surgical or by radiotherapy, can be challenging due to post-treatment tissue-related alterations such as edema, scarring, fibrosis, and neovascularization [6]. While the National Comprehensive Cancer Network (NCCN) guidelines recommend a specific frequency for clinical examination after treatment completion (1-3 months during the first year, 2-6 months during the second year, 4-8 months during years 3-5, and every 12 months after the fifth year) [7], they do not give specific recommendations regarding serial imaging follow-up in these patients.  
Several studies have attempted to explore the yield of imaging follow-up after definitive treatment for head and neck SCC [5; 8; 9]. While imaging was able to detect clinically occult persistent or recurrent disease, no improvement in either disease-specific or overall survival was reported. These studies, however, covered all head and neck SCC subsites, and did not distinguish patients with laryngeal SCC from other patients.  
Patients with advanced-stage laryngeal SCC undergo baseline imaging before treatment initiation with either computerized tomography (CT) with intravenous contrast or positron emission tomography CT (PET-CT). After treatment, a baseline PET-CT is usually performed after 3 months, but no guidelines exist for further imaging surveillance in these patients.  
In this study, we aimed to examine the yield of imaging performed in the post-treatment follow-up of patients treated for advanced-stage laryngeal SCC, regarding detection of recurrent disease and survival.