esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold
## 2 Target Detection in Passive Sonar  
Target detection, is as the separation of a special signal that is \"target\" from the other signals. In other words, in the case of detection, all of the received signals are divided into target and non-target classes by classifier. In general, non-target signals are regarded as noise/clutter and a successful classifier is one that uses (the least) existing knowledge to extract the target signal from the noise.  
Over the years', researchers have used several classifiers to extract target signal from noise, such as support vector machines (SVM), neural networks, and statistical classifier. In this article, according to the statistical of the environment of the sea and the condition of targets (which are mostly fluctuating), Bayesian statistical classifier method has been used. In the proposed method, after pre-processing, the Bayesian filter is trained by the target signal and noise in time and frequency domain. For calculating the coefficients of posterior distribution, MLE (Maximum Likelihood Estimation) algorithm is used. Results of Bayesian classification in each domain are recorded as detection point and fusion is done by Winer adaptive filters. Finally, the appropriate adaptive threshold for detection is calculated. Briefly, the proposed method is shown in Figure 3. In following, ambient noise, radiation noise, and the details of the proposed method and the obtained results are presented.  
Figure 2: Persian Gulf territory.
## 3 RL-Based Multimedia Traffic Routing Architecture
### Architecture and Components  
#### 3.1.1 Infrastructure Plane  
This plane includes three major components 1 Video streaming providers 2 SDN-enabled network 3 Video streaming customers. The first component is responsible for providing various video streaming services, which will be transferred over the SDN network. The second one contains the forwarding switches, which are under the management of the SDN controller and the links connecting them. These elements intend to carry out a set of primary tasks, such as forward the incoming packets to one or more ports or drop the packets. The video traffic is transmitted based on the path information installed in the flow tables of the SDN switches. The SDN switches have no knowledge about the network and account on the control plane and the application plane to occupy and install their flow tables. The final element is in charge of giving feedback. The feedback indicates the overall level of satisfaction with video streaming services which reflects the QoE of the services. The infrastructure plane also represents a reinforcement learning environment that periodically provides information about network topology by reacting to queries received from the control plane.
## 2 Materials and Methods
### MODIS GPP Data  
The study used the satellite-driven MOD17A2 GPP data (V6) based on the concept of a light use efficiency model for comparison, which has been widely used to repeatedly monitor global vegetation conditions, as well as the associated carbon sequestration potentials [46], and as inputs to terrestrial land surface models to infer the energy and ecohydrological processes [17]. The satellite remote-sensing-based vegetation-related dynamic input data of the model used the MOD15A2H data set, including the composite of photosynthetically active radiation (FPAR) and leaf area index (LAI) for 8-day periods. The missing data owing to cloudiness in this product are gap-filled according to the MODIS quality assessment (QA) data layers. Meanwhile, the model used the meteorological data from 6-h National Center for Environmental Prediction/Department of Energy (NCEP/DOE) reanalysis II products to drive the algorithm. The relevant Biome Property Look-up Tables (BPLUT) were also updated across the plant functional types. Currently, these GPP products have several time scales including 8-day, monthly, and annually, and this study used the 8-day mean values, which can be accessed through the website [[https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv006/]([https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv006/) (accessed on 20 October 2021).
## 8 Anonymity and Security Analysis
### Security against Active Attacks  
In principle, we assume ADV knows encryption protocols used by the framework; however, the framework needs to hide _encryption keys_ and _IDs_. Active attacks can be categorized into _soft_ and _hard_. For soft-active attacks, ADV tries to compromise SNs to get some information related to security of the sensors such as _keys_ and _IDs_. Consequently, it will monitor all messages traversing through the compromised nodes to discover the source and the BS locations. ADV hides its presence by acting passively (soft) but once it captures privacy information, it reports the information to an external executer to do further damages (such as killing the [PERSON] in the Panda game). For that, it is harder for the IDS to detect the attack. In hard-active attacks, ADV captures SNs and invasively forge messages, sent replay messages _etc_. Moreover, ADV could load powerful devices with the captured credentials to launch more catastrophic attacks. Hard-active attacks could be detected by IDS; however, it could depend very much on the sophistication of the IDS used. With that, it remains very challenging to countermeasure hard-active attacks. In the following two subsections, we will analyze the security of our framework against active attacks.
## 2 Materials and Methods  
### Study Design  
The NXDC-MEN-301 phase 3 open-label single-arm study is designed to investigate the safety, diagnostic performance, and clinical usefulness of the imaging agent Gleolan(tm) (Aminolevulinic Acid Hydrochloride, ALA HCl, ALA, 5-ALA), an orally administered imaging agent for the real-time detection and visualization of meningiomas during tumor resection surgery. All patients with imaging suggesting meningioma, irrespective of grade or tumor location, are eligible. Key inclusion criteria include age >18 years and normal organ function, with a focus on liver and normal bone marrow function. Key exclusion criteria are hypersensitivity to porphyrins, the use of photosensitizing drugs (St. John's wort, griseofulvin, thiazide diuretics, sulfonylurea, phenothiazzines, sulphonamides, quinolones, and tetracyclines), or planned embolization of tumors prior to resection. Study surgeons are selected based on experience of 5-ALA fluorescence-guided resection and being equipped with a standard microscope for fluorescence-guided resection (Blue400, Zeiss Meditech, Oberkochen, Germany; FL400, Leica Microsystems, Heerbrugg, Switzerland).  
Patients scheduled to undergo meningioma resection receive an oral solution of Gleolan(tm) (5-ALA; 20 mg/kg bodyweight) 3 h (target range 2.5 to 3.5 h, maximally 5 h) prior to anesthesia and then undergo surgery. The exact time point of administration is recorded.  
During the operation, surgeons are required to follow a highly defined sequence of activities for finding fluorescing tissue and during tissue collection as a basis for assessing clinical usefulness and the diagnostic accuracy of fluorescence. These activities are recorded by the integrated high-definition video cameras of either microscope (Blue400, Zeiss Meditech, Oberkochen, Germany; FL400, Leica Microsystems, Heerbrugg, Switzerland) for later peer panel review.  
All time points of assessments and biopsies are documented. Prior to activation of a site, study surgeons at that site are closely educated regarding sampling algorithms but also minimizing tissue exposure under white light to reduce potential photobleaching of tissue porphyrins.  
All biopsies are forwarded for blinded tissue analysis, including Ki67 proliferation indices and other molecular investigations, to the Institute for Neuropathology, University of Munster.
## 4 Results and Discussion
### Another Type of Blind Test  
An additional experiment was conducted to test the reliability of our approach more objectively. In addition to the leave-one-year-out blind tests in Sections 4.1 and 4.2, we carried out another type of blind test using the 10-fold cross-validation with random sampling (Figure 11). We rebuilt a DNN model and calibrated the hyperparameters for a new testing environment. As a result, the 10-fold blind tests showed similar validation statistics to those of the leave-one-year-out blind tests, which means that the DNN model was appropriately optimized for corn yield prediction and offered a nice performance for both experiments (Table 8).  
Figure 8: Actual and predicted corn yields from the leave-one-year-out blind tests for 2006–2015: Red dots, heatwave exceeding five days; black dots, all other cases. (**a**) Multivariate adaptive regression spline (MARS); (**b**) Support vector machine (SVM); (**c**) Random forest (RF); (**d**) Extremely randomized tree (ERT); (**e**) Artificial neural network (ANN); (**f**) Deep neural network (DNN).  
Figure 9: Actual and predicted corn yields from the leave-one-year-out blind tests for 2006–2015: Red dots, heatwave exceeding seven days; black dots, all other cases. (**a**) Multivariate adaptive regression spline (MARS); (**b**) Support vector machine (SVM); (**c**) Random forest (RF); (**d**) Extremely randomized tree (ERT); (**e**) Artificial neural network (ANN); (**f**) Deep neural network (DNN).  
Figure 11: Concept of 10-fold cross-validation with random sampling for the blind tests of corn yield prediction during 2006–2015.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Validation** & **MBE (1)** & **MAE (2)** & **RMSE (3)** & **MAPE (4)** & **Corr.(5)** \\  & **Method** & **(ton/ha)** & **(ton/ha)** & **(ton/ha)** & **(\%)** & **Corr.(5)** \\ \hline \multirow{2}{*}{**Dropout**} & leave-one-year-out & \(-0.017\) & \(0.666\) & \(0.828\) & \(12.9\) & \(0.954\) \\  & 10-fold & \(-0.001\) & \(0.694\) & \(0.934\) & \(12.6\) & \(0.934\) \\ \hline \multirow{2}{*}{**HW \(\geq\) 5 (6)**} & leave-one-year-out & \(0.108\) & \(0.781\) & \(1.033\) & \(15.8\) & \(0.914\) \\  & 10-fold & \(0.019\) & \(0.780\) & \(1.079\) & \(15.9\) & \(0.906\) \\ \hline \multirow{2}{*}{**HW \(\geq\) 7 (7)**} & leave-one-year-out & \(0.101\) & \(0.857\) & \(1.117\) & \(18.9\) & \(0.887\) \\  & 10-fold & \(0.083\) & \(0.863\) & \(1.179\) & \(19.0\) & \(0.875\) \\ \hline \end{tabular} (1) Mean Bias Error; (2) Mean Absolute Error; (3) Root Mean Square Error; (4) Mean Absolute Percentage Error; (5) Correlation Coefficient; (6) heatwaves longer than five consecutive days; (7) heatwaves longer than seven consecutive days.  
\end{table}
Table 8: Summary of comparisons between leave-one-year-out and 10-fold blind tests for the deep neural network models for prediction of corn yield under drought and heatwave conditions during 2006–2015.  
Figure 10: Maps of (**a**) actual and (**b**) predicted corn yields from the leave-one-year-out blind tests for 2007. The Illinois counties outlined in black were affected by the heatwaves for longer than five consecutive days.
# SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing
## 5 Case Studies
### How SPOT Applicable to Operators  
SPOT supports a variety of operators prevalently in DSP programs. This is especially critical in symbolic execution, as any unrecognized operator will obstruct the analysis by one symbolic engine. The first case study is to show that SPOT can support the symbolic analysis for some common operators.  
**Subject Programs**. We build subject programs based on the examples from the Flink website [44], each corresponding to at least one specific operator. The main part of the source code (we have omitted other irrelevant code to save space) is shown in Figure 7.  
We analyze each of these subject programs with JPF-Flink individually, and obtain the analyzing results, including PCs, sampled test data corresponding to each PC, et cetera. One example is the result for the filter program, i.e., the program (c) in Figure 7. It is shown in Figure 8 (some unimportant information has been omitted and marked as the ellipsis). As the result shows, we can get three PCs representing each JDU path. We can also obtain three solid data after solving these PCs. If necessary, more data can be derived by solving PCs continuously. In addition to symbolic execution, we can also adjust SPOT to concolic analysis by setting the parameters in the method from _sym_ to _conc_ (e.g., we can build a new clause \"...FilterTest.lambda50(_conc_)\" to notify the SPF to conduct concolic analysis).  
After analyzing all the subject programs individually with JPF-Flink, we manually check the PCs obtained, and confirm that they have covered all possible JDU paths.  
Figure 7: Snippets of Subject Programs.  
The symbolic analysis is the most source-code involved part among our SPOT solution. The series data synthesizing part, however, is much more general. In other words, it is not strictly relying on the semantics of the source code. For this reason, we will not conduct experiences about how to synthesize stream for each operator here. Instead, the synthesizing methods will be covered in the following case studies.  
In summary, SPOT can support the symbolic analysis for the five common operators, and generate representative data as test data. Meanwhile, SPOT can also support concolic analysis for these operators after some tiny modifications.
## 3 Transmitter Hardware Impairments or 'RF Features' Overview
### I/Q Imbalance Models  
During the baseband-to-passband conversion, the I and Q components (\(x_{I}(t)\) and \(x_{Q}(t)\)) at the transmitter can be modelled via [41; 42]:  
\[x_{I}(t)=A(t)cos(2*pi*f_{c}) \tag{7}\] \[x_{Q}(t)=g_{IQ}A(t)sin(2*pi*f_{c}+\phi_{IQ})\]  
where \(A(t)\) is the baseband amplitude, \(f_{c}\) is the passband carrier frequency, \(g_{IQ}\) is the I/Q amplitude imbalance factor, also known as the gain imbalance factor [42] and measured typically in dB, and \(\phi_{IQ}\) is the I/Q phase imbalance factor, also known as quadrature skew factor [42]. Above, the PN effect was ignored for clarity purposes. The imbalance factors \(g_{IQ}\) and \(\phi_{IQ}\) are transmitter-dependent constants and it is expected that a genuine GNSS transmitter would have lower absolute values \(|g_{IQ}|\) and \(|\phi_{IQ}|\) than a spoofer. For a perfect transmitter, without any I/Q imbalance, one would have \(g_{IQ}[dB]=0\) and \(\phi_{IQ}=0\). Imperfect transmitters have been studied for example in [42], based on multipurpose universal software radio peripheral (USRP) as those that may be used by a Software Defined radio (SDR) spoofer and values below 1 dB and below 8 degrees have been estimated for \(|g_{IQ}|\) and \(|\phi_{IQ}|\) values, respectively.  
Figure 4: The block diagram for the Wiener model and Hammerstein model.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
###### Abstract  
This study presents a lightweight autoencoder-based approach for anomaly detection in digit recognition using federated learning on resource-constrained embedded devices. We implement and evaluate compact autoencoder models on the ESP32-CAM microcontroller, enabling both training and inference directly on the device using 32-bit floating-point arithmetic. The system is trained on a reduced MNIST dataset (1000 resized samples) and evaluated using EMNIST and MNIST-C for anomaly detection. Seven fully connected autoencoder architectures are first evaluated on a PC to explore the impact of model size and batch size on training time and anomaly detection performance. Selected models are then re-implemented in the C programming language and deployed on a single ESP32 device, achieving training times as short as 12 min, inference latency as low as 9 ms, and F1 scores of up to 0.87. Autoencoders are further tested on ten devices in a real-world federated learning experiment using Wi-Fi. We explore non-IID and IID data distribution scenarios: (1) digit-specialized devices and (2) partitioned datasets with varying content and anomaly types. The results show that small unmodified autoencoder models can be effectively trained and evaluated directly on low-power hardware. The best models achieve F1 scores of up to 0.87 in the standard IID setting and 0.86 in the extreme non-IID setting. Despite some clients being trained on corrupted datasets, federated aggregation proves resilient, maintaining high overall performance. The resource analysis shows that more than half of the models and all the training-related allocations fit entirely in internal RAM. These findings confirm the feasibility of local float32 training and collaborative anomaly detection on low-cost hardware, supporting scalable and privacy-preserving edge intelligence.  
anomaly detection; TinyML; autoencoder; federated learning; TinyFL
# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 1 Introduction
This model integrates heterogeneous graph contrastive learning, graph structure learning, and sequence modeling. Specifically, we consider the heterogeneous information between users, POIs, and POI attributes, and construct meta-path-based subgraphs for both users and POIs based on different meta-paths. Then, representation vectors for users and POIs are aligned through two contrastive learning methods, which differ in whether they consider the heterogeneous relation when computing the contrastive loss between different meta-path subgraphs. Next, the representation vectors obtained from contrastive learning are used to reconstruct the corresponding meta-path subgraphs, and graph neural networks are employed to obtain the representation vectors for users and POIs from the reconstructed graphs. Finally, the learned representation vectors for users and POIs are used to learn user sequences, where different types of factors will be included in this stage. In summary, the main contributions of this paper are summarized as follows:  
1. In this paper, heterogeneous graph contrastive learning is introduced into the process of representation learning. Considering the missing information may impact how we construct contrastive views, we use the provided information to construct a heterogeneous graph. Then, meta-paths are used to construct meta-path subgraphs of users and POIs, respectively, and two kinds of contrastive learning methods are adopted to improve the quality of the representation vectors of users and POIs. 2. Considering that the artificially constructed graph may have downsides such as noise and missing edges, graph structure learning is used to continuously update the graph structure, and takes the representation vectors generated by the updated graph as the input of the subsequent sequence modeling. 3. This paper models the users' check-in sequences from the perspective of the graph, better capturing the relation between different check-ins. In addition to the sequential factor, we also include temporal and spatial factors into the modeling process to better capture the users' preferences. 4. A large number of experiments are carried out on the public datasets, and the experimental results confirm the effectiveness of the model.
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 2 RTK Positioning Models
### GNSS Observations  
The range measurements of GNSS mainly include the phase and code observations. If the receiver \(r\) is tracking satellites \(s_{*}=1_{*},2_{*},\cdots,n_{*}\), where \(n\) is the number of satellites of a single GNSS \(*\), such as BDS3, GPS, and Galileo, then the multi-frequency GNSS observations can be given by [15]:  
\[\begin{split}\phi^{s_{*}}_{r,f}&=\rho^{s_{*}}_{r}+ dt_{r}-dt^{s_{*}}-\hat{t}^{s_{*}}_{r,f}+t_{r}+\lambda(\hat{a}^{s_{*}}_{r}+\hat{ \phi}^{s_{*}}_{r,f}-\hat{\phi}^{s_{*}}_{f}+\varphi_{r,f}-\varphi^{s_{*}}_{f})+ dt^{s_{*}}_{TO}+\hat{\epsilon}^{s_{*}}_{r,f}\\ P^{s_{*}}_{r,f}&=\rho^{s_{*}}_{r}+dt_{r}-dt^{s_{*}}+ \hat{t}^{s_{*}}_{r,f}+t_{r}+d^{s_{*}}_{r,f}-\hat{d}^{s_{*}}_{f}+dt^{s_{*}}_{TO} +\hat{\epsilon}^{s_{*}}_{r,f}\end{split} \tag{1}\]  
where \(\phi\) and \(P\) denote the phase and code observations, respectively; \(\rho\) is the geometric distance between the satellite and the receiver; \(dt_{r}\) and \(dt^{s_{*}}\) are the clock bias in the receiver and satellite, respectively; \(i\) and \(t\) are the ionospheric and tropospheric delay, respectively; \(\lambda\) is the wavelength of frequency \(f\); \(a\) denotes the unknown ambiguity; \(\delta^{*}_{r,f}\) and \(\delta^{s_{*}}_{f}\) denote the phase hardware delay in the receiver and satellite, respectively; \(d^{*}_{r,f}\) and \(d^{s_{*}}_{f}\) denote the code hardware delay in the receiver and satellite, respectively; \(\varphi_{r,f}\) and \(\varphi^{s_{*}}_{f}\) are the initial phase biases in the receiver and satellite; \(\epsilon^{s_{*}}_{r,f}\) and \(\epsilon^{s_{*}}_{r,f}\) are the noise of the phase and code observations, respectively; and \(dt_{TO}\) is the time offset from GPS to other GNSSs.  
In this article, an elevation dependent method is applied to weight different types of observations. The empirical stochastic model can be stated as:  
\[\sigma_{\theta}=\frac{\sigma_{0}}{\sin(\theta)} \tag{2}\]  
where \(\sigma_{\theta}\) denotes the prior standard deviation (STD) of undifferenced observations with an elevation of \(\theta\); \(\sigma_{0}\) denotes the prior zenith STD for phase and code observations, which is usually set according to the accuracy of observations. In this article, the custom set of the STD of the observation noise was used, which is 0.003 m for phase observation and 0.3 m for code observation.