esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Automatic Signal Recognition
### Extended Validation for Mixed Signals  
It is validated that the features of the RSSI distribution can be used to categorize Wi-Fi, LTE and DVB-T when the signals are captured individually. This section extends the validation by taking into account mixed signals from multiple technologies. The chosen scenario is the operation of LTE in ISM bands. Section 5.3.1 discusses how the dataset is obtained for the mixed LTE-U and Wi-Fi signals; then, Section 5.3.2 shows how the feature space and algorithm are extended accordingly to cope with the mixed signal as a new signal class; finally the analysis of additional results is presented in Section 5.3.3.
## 3 Results
### Comparison with Reference Laser System  
In order to compare the results from the laser diode to a state-of-the-art fiber laser, we repeated the measurement with a Menlo Systems 1550 nm laser with a pulse duration shorter than 90 fs. The settings of the lock-in amplifier are kept as before, but the step size for the delay-line was reduced to 0.01 mm to resolve the higher bandwidth. The laser power is 45 mW at the emitter and 16 mW at the receiver side. For this measurement, the mesh filter is removed. The measured time-domain trace is depicted in Figure 4a. The system generates a single THz pulse. The low frequency oscillation after the pulse is  
Figure 3: THz TDS time-domain traces (**a**) V\({}_{\mathrm{P2P}}\) = 14.1 mV and (**c**) V\({}_{\mathrm{P2P}}\) = 8.3 mV and spectra (**b**,**d**) with the FPL1009P laser diode. (**a**,**b**) are recorded without high pass mesh-filter. (**c**,**d**) with high pass mesh filter. (**b**) has a peak dynamic range of 51 dB and 1.4 THz bandwidth and (**d**) with filter: 48 dB and 1.3 THz.  
due to the above described unwanted resonance of the antenna leads. The slight ripples after the main pulse originate from absorption due to water vapor.  
Figure 4b shows the retrieved spectrum (black). The peak dynamic range is about 70 dB, while the bandwidth exceeds 4 THz. The water lines are clearly visible. For comparison, the spectrum from the laser diode driven system without mesh is plotted in light red.
## 5 Case Study for Mitigating Security Attacks in Smart Healthcare System
### Intelligence Layer  
The non-malware samples acquired from the previous layer (malware analysis layer) still have network-related vulnerabilities that, if they fall into the wrong hand (attacker), will have severe consequences on the healthcare sector. Therefore, in this layer, we utilize AI algorithms' significant benefits to efficiently classify non-malware samples, according to whether that contain malicious or non-malicious data. AI algorithms, such as logistic regression (LR), support vector machine (SVM), random forest (RF), perceptron, and Naive Bayes (NB), are used for classification purposes [106]. Initially, the AI algorithms are trained using a standard wearable security dataset, i.e., ICU dataset, that has network-related features and a binary class label (0-non-attack and 1-attack) [107]. Here, the standard  
Figure 4: Smart healthcare architecture for mitigating security attacks.  
dataset is first analyzed using a statistical test to observe the distribution of the feature space. For that, a parametric test is applied to the feature space (e.g., tcp.srcport, tcp.dstport, tcp.windows, tcp.checksum, etc.) of the dataset to analyze the normal distribution. Figure 5 show that the dataset feature space does not follow a normal distribution. Therefore, we used a non-parametric test, i.e., the Mann-Whitney U test, to analyse the dependency between dependent and independent features.  
A null hypothesis is created, i.e., a significant correlation between two features. The null hypothesis is rejected if the p-value is smaller than the significance value, i.e., 0.05. We used different features, i.e., mqtt.client and mqtt.conack.flags, to the Mann-Whitney U Test that gives the \(p\)-value of 0.04, which is smaller than the significance. Hence, it rejects the null hypothesis is rejected as there is no strong correlation between mqtt.client and mqtt.conack.flags.  
stat, p_value = mannwhitneyu(data.mqtt.client, data.mqtt.conack.flags) sign = 0.05 if p_value < sign:  print('No significant correlation between two features)') else:  print('Significant correlation between two features)')  
Further, the dataset is preprocessed using various data preprocessing techniques, such as missing values, normalization, and datatype casting. Then, the processed data are forwarded to the AI models, where it is trained and validated using the real-time network data extracted from the non-malware samples. Finally, the non-malware samples containing malicious data are discarded from the proposed architecture, and only the non-malware sample containing non-malicious data is forwarded to the next layer.
## 2 E-Health  
Nowadays, many people are talking about e-health, yet few of them have come up with a definition that explains this relatively new term. This term was rarely used before 1999, and now this term seems similar to a \"buzzword\" describing \"Internet medicine\" and everything related to computer-based medical services. Industry leaders and employees used this term for the first time in marketing rather than academics. They use the term proportionately to other \"e-words\", e.g., e-business, e-commerce, and e-solution [9].  
It described the new possibilities that the Internet opens to healthcare. For instance, Intel defines e-health as \"a collaborative effort by healthcare and high technology industry leaders to realize the full potential of the Internet's role in healthcare delivery\" [10].  
Recent definitions of e-health in the electronic environment place it at the point where medical informatics, public health, and commercial activities converge. It offers medical services and information via the Internet and related technologies. E-health describes not only technical aspects, but also the mindset, and way of thinking to enhance local, regional, and global healthcare using information and communication technologies (ICTs) [11].  
Among the components of e-health are telemedicine, decision support systems, electronic consultations, and electronic health records (EHR). Computer-generated prescriptions have many advantages, such as linkages to programs emphasizing drug or drug groups' hazards. Computers are now fundamental to most healthcare practices that initially give electronically recorded patient information [12].
# A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection +
## 4 Dataset, AE Design and Implementation  
The validity of the cross-device method is assessed by a **twofold** experimentation. First, we pursue a \"conventional\" approach, which consists of training-testing the autoencoder-based IDS with the data of one IoT device each time, which leads to one separate model per device. It is worth pointing out that this approach is used by several related papers presented above. Later, the autoencoder-based IDS is trained with a dataset consisting of the normal traffic of all the IoT devices in hand--and therefore **all-in-one**--that leads to a single model for all the devices. It is worth noting that the notion of all-in-one autoencoder underlies the availability of training data pertaining to different devices. Consequently, training is not expected to happen \"in place\" on single IoT devices. Rather, the proposed approach is _suited_ to the nodes traversed by the traffic generated from different network devices. A typical example is the Cloud-Edge-IoT paradigm: Edge nodes--at the boundary between two networks --implement common functions, such as routing, monitoring, and storage of data passing between networks. In the context of the IoT, Edge nodes encompass a broad range of devices and are eligible to host our all-in-one IDS. As a consequence of deploying the all-in-one IDS onto Edge nodes, IoT devices **are not** subjected to extra computational and energy burden; as said above, energy efficiency is among the principles that underlie the design of our proposal. Moreover, as only passive tracing of network traffic is required, the approach is inherently nonintrusive. In the following, we describe the datasets, tuning and training of both separate and all-in-one autoencoding.
## 4 Results
### CIDS Trust and Privacy-Preserving Analysis
Acting as a decentralized ledger, the blockchain meticulously records all transactions pertaining to model updates and interactions between edge devices and fog nodes. This blockchain-based architecture ensures transparency and immutability, empowering all participants to verify the integrity of the recorded transactions. Through the fusion of FL for privacy-preserving model training and blockchain for transparent transaction recording, the CIDS adeptly averts data breaches and upholds trust within the system. The CIDS leverages a sophisticated blend of FL and blockchain technology within an edge-fog-cloud architecture to fortify its trustworthiness and preserve user privacy. FL ensures that sensitive data remain decentralized, as each edge device trains its local intrusion detection model without sharing raw data. Model updates are aggregated at fog nodes using privacy-preserving techniques such as secure aggregation or differential privacy, safeguarding individual contributions. These aggregated updates are then recorded on the blockchain, serving as a transparent and immutable ledger that meticulously records all interactions and transactions within the system. This innovative fusion of FL and blockchain technology not only prevents data breaches but also instills confidence in the integrity and transparency of the CIDS ecosystem, fostering trust among its participants.
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 3 CLAMBDA-Search Model
### Additional Discussion
Starting from \(b_{2}\), turn to \(b_{1}\) in the anti-clockwise direction. If \(b_{2}\) is negative, then \(\alpha=\alpha+\pi\). In the meantime, the conversion of \(\theta\) and \(\gamma\) is required when converting to the positive right-handed coordinate system if the negative axis is defined. For different baseline configurations, we must use the vehicle coordinate system, the Euler angle definition and DCM in a consistent manner. Otherwise, it will cause unpredictable consequences because of the definition of the Euler angle and coordinate system. So far, the problems of different vehicle coordinate systems, different attitude angle definitions, and the antenna's non-orthogonal errors have been successfully resolved.
## 3 Flightload Calculation Modeling
### Data Sources and Traditional Flightload Calculation Method  
In the calculation of flightload envelope, the efficient panel method was generally used instead of the computationally expensive CFD-CSD coupling method, as millions of flightload conditions needed to be calculated. Figure 4 shows the computational process of the high-order panel method used in this study. Each iteration took approximately 1 min, and each inner loop, which calculated the flightload for a fixed angle of attack, took about 2-4 min. Each outer loop, which calculated the flightload for a fixed load factor, took approximately 5-10 min. The calculation time for each flight condition was more than 5 min, so the total time for millions of flight conditions is indeed observable.  
Figure 2: Structure model of the plane.  
Figure 4: Flowchart of panel method for flightload calculation.  
Figure 3: Aerodynamic model of the plane.
## 1 Introduction
### Related Work
#### 1.1.2 Anomaly Detection and Geospatial Analysis in Real-Estate Data  
Data quality remains crucial for real-estate platforms. While applying isolation forest [16] seems to be the best approach in general for anomaly detection in specific applications such as real estate, some other approaches [17] have detected price manipulation in Greek real estate with 89% accuracy. There are studies [18] that combined multiple anomaly detection algorithms, showing that ensemble methods outperform single approaches by 15-20%. Our implementation builds on these findings with real-time processing capabilities.  
Dynamic urban analysis has evolved from utilizing static administrative boundaries to data-driven approaches. One development [19] used spectral clustering on check-indata. For real estate, in general [20], spatial representations outperform ZIP codes for price prediction by 8%. Recent work [21] on DBSCAN parameter optimization achieved 95% clustering stability, which we incorporate into our dynamic zone generation.
# Estimation of Energy Storage Requirements in an Independent Power System from an Energy Perspective
## 4 Energy Storage Demand Simulation  
This section conducts a series of simulations to assess the energy storage demand during the Spring Festival period in Taiwan over the coming years. The simulation uses actual load data from the Spring Festival of 2023, assuming an annual electricity consumption growth of 2%, to estimate the load for each Spring Festival in the future. To accommodate the most extreme scenarios, solar PV generation is assumed to be at full capacity. The solar power generation is based on the solar energy generation and installation capacity during the summer of 2022, combined with the future installation capacity increases projected by the Energy Bureau, to estimate the annual solar power generation. The wind power generation part directly uses the installed capacity as the basis for estimating the power generation.
# RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones
## 1 Introduction
However, INS based on an MEMS-IMU that suffers from large drift cannot provide satisfactory positioning results for a long period of time [35]. Navigation algorithms based on inertial MEMS sensors are divided into two types in general: pedestrian dead reckoning (PDR) algorithms and INS-based algorithms [36]. PDR estimates the position by counting steps and estimating stride length and heading. Many algorithms, such as peak detection and zero-crossing counting, can be used to detect and count steps [37]. After successfully detecting a new step, PDR uses a kind of step length model to estimate the moving distance of a pedestrian [38]. AHRS algorithms can be used to improve the accuracy of estimating the heading angle, which improves the PDR's performance [39; 40]. INS uses strapdown mechanical equations to estimate position, velocity, and attitude. INS-based algorithms utilize some pseudo-measurements to correct the estimate of position, velocity, biases of sensors, and attitude [41]. For example, a foot-mounted INS has explicit stance phases. The velocity should be zero during the stance phases so the ZUPT algorithm can be used to utilize the zero-velocity to reduce the long-term drifts of IMU [42]. PDR algorithms and INS-based algorithms can be coupled for a more accurate estimate of heading and position [36]. In this paper, we collect observation measurements through a Xiaomi Mi 8. We compare the performance of single-frequency RTK with that of dual-frequency RTK based on the Xiaomi Mi 8 in both static mode and dynamic mode. We also compare the performance of RTK based on the Xiaomi Mi 8 with that based on a dual-frequency NovAtel receiver equipped with an OEM617-CDS-R0G-550 card. Here, we utilize RTKLIB [43] to take the measurements. We also provide a method to assist RTK with an IMU-based pedestrian navigation algorithm for smartphones. Considering GNSS outage and MEMS-IMU integrated into our smartphone, we adopt the Madgwick algorithm, which is one kind of AHRS algorithm to provide the initial attitude of our smartphone and utilize the ZUPT algorithm to aid INS to provide positioning results during GNSS outage. The rest of the paper is organized as follows: In Section 2, the RTK algorithm, the Madgwick algorithm, the ZUPT algorithm, and the procedure of collecting data are introduced in detail. The performance comparison among RTK using the Xiaomi Mi 8, using the NovAtel receiver, and RTK with the assistance of an IMU-based pedestrian navigation algorithm are described in Section 3. Conclusions and future work are summarized in Section 4.
## 3 Results and Discussion
### Mineralogical Properties of Soil  
The collected soils were examined using XRD (Figure 4, Table 3) to determine the major crystalline phases or Pb minerals in the soils and to identify the transformation of Pb minerals. The predominant mineral components are quartz, albite, microcline, muscovite/illite, kaolinite, chlorite, and calcite. Carbonate is known to be one of the crucial sources for anthropogenic Pb [35]. The soils in this study generally contain calcite, representing direct evidence of anthropogenic Pb contribution. Hematite only occurs in the surface soil of the 200 m transect of the sixth shooting lane. Minerals carry toxic elements. As shown in the element maps, the minerals in the soil are enriched in Pb, Cd, Ni, and Zn. However, although the soils have high Pb concentrations, Pb minerals, such as hydrocerussite (Pb\({}_{3}\)(CO\({}_{3}\))\({}_{2}\)(OH)\({}_{2}\)), massicot (PbO), hydroxypyromorphite (Pb\({}_{10}\)(PO\({}_{4}\))\({}_{6}\)(OH)\({}_{2}\)), corussite (PbCO\({}_{3}\)), and angesite (PbSO\({}_{4}\)), were not detected by XRD analysis in this study. This can be explained by the gradual transformation of Pb into secondary mineral phases in soils in shooting ranges [3]. The transformation processes are pH-dependent; Pb is soluble under acidic conditions and poorly soluble under neutral and alkaline conditions. In summary, transformation products of acidic soils have a high solubility and high mobility, whereas transformation products of basic soils have a low solubility and attach to the bullet surface or remain in the surface soil [14]. For example, cerussite dissolves at low pH [41] and cerussite and/or hydrocerussite in acidic soil (pH = 4.5) easily dissolves and transforms into ionic Pb\({}^{2+}\)[9]. The soils in the study area are basic; therefore, the gradual transformation into Pb secondary minerals did occur.  
Figure 6: Plots of (**a**) \({}^{208}\)Pb/\({}^{206}\)Pb vs. \({}^{206}\)Pb/\({}^{207}\)Pb and (**b**) \({}^{206}\)Pb/\({}^{207}\)Pb vs. Pb (mg/kg) for soils from 6 th lane of shooting range with drain ditch and background.
## 3 Methods and Data Sources
### Regression Model Building and Testing
#### 3.2.2 Performance of Prediction Model  
There are a series of evaluation indicators for regression models, mainly from two aspects: On the one hand are the traditional error indicators, which can effectively detect the error value between the current model prediction results and the real results, among which are mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (MSE). On the other hand is the overall evaluation of the model's overall fitting effect and explanatory ability, which is called R2, ranging from 0 to 1, indicating the percentage of variation in the outcome variables that can be explained by the predictor variables, i.e., the square of the correlation coefficient; the higher the value means the better the effect of the model, and the formula for them is explained below:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|Mwm_{i}-Mwm_{i}^{\prime}| \tag{1}\]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\left(Mwm_{i}-Mwm_{i}^{\prime}\right)^{2} \tag{2}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{Mwm_{i}-Mwm_{i}^{\prime}}{Mwm _{i}}\right| \tag{3}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}\left(\text{y}_{i}-\text{y}_{i}\right)^{2 }}{\sum_{i=1}^{n}\left(\text{y}_{i}-\text{y}_{i}^{-}\right)^{2}} \tag{4}\]  
where \(n\) is the number of data sets, \(Mwm_{i}\) is the actual garbage yield for data \(i\), and \(Nwm_{i}^{\prime}\) is the estimated garbage yield for data \(i\).
## 4 Simulation Tests and Results
### Case Study 5  
The aim in the fifth case study was to maximize the coverage of a two-dimensional, squared area of \(50\times 50\). The number of the available sensors was equal to 40 and their sensing range was equal to 5. The grid dimensions were \(1\times 1\), the size of the population in the GA algorithm was equal to 600, and the swarm size in the PSO algorithm was adjusted to 600. The results derived are synoptically presented in Table 7.  
Figure 17: Optimal node positioning with the use of the PSO algorithm in case study 4.  
Figure 18: Best fitness value for each iteration of the PSO algorithm in case study 4.
## 3 Results and Discussion
### Spacing Ratio
Twenty sampling points were selected for each wind turbine, as shown by the dots in Figure 10c. The comparison curves are shown in Figure 11. The wake velocity recovery is different for different spacing ratios. Compared to standalone wind turbines, the wake recovery speed of the group is faster, especially for the L/D = 2 cases, where it has recovered to 92% of the inflow wind speed at \(\mathrm{x}/\mathrm{D}=1.5\) downstream, almost regaining the original wind speed. Under other spacing conditions, the wake has recovered to the original wind speed at \(\mathrm{x}/\mathrm{D}=2\)-3. This is an advantage that standalone wind turbines do not possess [32] and provides more references for downstream wind turbine array arrangements. Figure 11: Velocity normalization curves with different spacing ratios: (**a**) L/D = 1.2, (**b**) L/D = 1.5, (**c**) L/D = 2. Figure 10: Wake velocity contour of sections with different spacing ratios: (**a**) L/D = 2, (**b**) L/D = 1.5, (**c**) L/D = 1.2.