esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
All three levels of addresses are created hierarchically.16 bits short addresses are assigned to a RFD at the time of deployment. A EUI-64 identifier is formed based on the 16 bits short address, and finally 128 bits global addressing is based on the EUI-64 bits identifier. Thisaddressing scheme will uniquely identify the IP-WSN nodes and at the same time reduce different types of consumption. The main contributions of this paper can be summarized as follows:  
1. A global IP-WSN architecture is proposed, by which we introduce our addressing and routing scheme. 2. We propose a global addressing scheme for the individual IP-WSN nodes which is generated in a hierarchical fashion. We also propose a routing scheme based on the hierarchical addressing. 3. We propose software architecture, and the respective message formats, network model and evaluate the performance of the proposed protocol architecture. 4. We address the applicability of mobility issues of individual or a group of IP-sensor nodes in a global patient care scenario. We propose the addressing format for the patient care scenario considering different mobility perspectives. We also focus on the routing scenario in all the mobility cases. 5. Mathematical analysis and simulation experiments are conducted to show the effectiveness of the proposed scheme. Our simulation results show that the proposed scheme effectively reduces the signaling and mobility cost, end to end delay in terms of the number of IP-WSNs and hops as compared to MIPv6 and PMIPv6, respectively. There is also a significant improvement in energy consumption for data payload and IP-WSN node density in the network. The rest of the paper is organized as follows: Section 2 reviews the background related to PMIPv6 and 6 LoWPAN. The proposed protocol architecture, along with IP-WSN based patient care application, mobility scenarios, addressing details and message formats are presented in Section 3. Section 4 describes performance analysis and evaluation, Section 5 shows the experimental and simulation results, and finally Section 6 concludes the paper.
## 4 Communication Technologies for Seamless Connectivity in Smart Farming
### Availability and Challenges of Communication Technologies
#### 4.2.3 Comparison in Terms of IoT Factors  
Some parameters including, device lifetime and latency, quality of service, coverage and range, cost, payload length, and scalability should be taken into consideration when it comes to using a proper LPWAN technology for IoT applications [71]. These parameters for NB-IoT, Sigfox and LoRaWAN are compared in Table 2 and discussed below:  
* **Coverage and Range:** The highest coverage range (at least 40 km) is related to Sigfox, and only one Base Station is enough to cover the entire area. The lowest range (at most 10 km) is obtained using NB-IoT which is not adapted for rural regions, and it is only used for LTE infrastructure. LoRaWAN has a coverage range of at most 20 km [72].
* **Latency:** NB-IoT offers a low IoT latency connectivity, while a low bidirectional latency is provided by Sigfox and LoRaWAN at the expense of increased energy consumption [73]. As a result, the best solution for IoT applications with low latency connectivity and latency insensitive applications can be NB-IoT, LoRaWAN-Class-C, and LoRaWANClass-A and Sigfox, respectively.
* **Battery life:** The lifetime of NB-IoT based devices is lower than LoRaWAN and Sigfox ones. This is because the energy consumption of NBIoT based devices is more than Sigfox and LoRaWAN devices, as they need to handle QoS and synchronous communication [40].
* **Quality of service (QoS):** Some applications need the Qos requirements, Sigfox and LoRaWAN are suitable for such applications, while for those applications that need QoS requirements NB-IoT is preferred [74].
* **Scalability and Payload length:** Sigfox, NB-IoT, and LoRaWAN provide high scalability. NB-IoT not only provides higher scalability than LoRaWAN and Sigfox but also maximum payload length. About 50K connected devices per Base Station is supported by RaWAN and Sigfox while NB-IoT supports twice as much as this number of users [70]. NB-IoT allows the highest payload length of 1600 bytes, while the Sigfox allows the lowest data transmission up to 12 bytes.
* **Deployment model:** Many countries and cities take advantage of completed LoRaWAN and Sigfox' ecosystems. LoRaWAN is deployed in 42 countries while Sigfox is used in 31 ones [72]. However, NB-IoT was published under rollout to set up its network over the world. Sigfox, NB-IoT, and LoRaWAN technologies are still in the final phase. Public network operation and local network placement via Base Stations are provided by these three technologies.
## 3 Results
### Statistical Validation  
Based on the spatiotemporal matching of the SAR observations and drifting buoy measurements shown in Figure 1, a total of 107 SAR scenes were collocated with the drifting buoy data, resulting in 177 matched pairs. Figure 7 presents the histograms of the corresponding SAR-retrieved wind speed, ECMWF wind direction, radar incidence angles, and wave parameters, including the SWH, wave direction, and wave number derived from the WW3 model.  
Figure 6: Comparison of SAR-retrieved radial OSC velocities with corresponding SMOC values. Colorbar represents number of data points.  
Figure 7: Histograms of collocated parameters: (**a**) SAR-derived wind speed, (**b**) ECMWF wind direction, (**c**) radar incidence angle, (**d**) WW3-simulated SWH, (**e**) wave direction, and (**f**) wave number.  
The wind speeds in the matched dataset range from 0.2 m/s to 16.7 m/s, with wind directions predominantly between 0\({}^{\circ}\) and 90\({}^{\circ}\). A similar distribution is observed for the wave directions, which also cluster primarily within this angular sector. The SWH values vary from 0.3 m to 6.1 m, indicating that the dataset covers a board spectrum of sea states as classified by the Douglas Sea Scale. The wave numbers range from 0.02 to 0.39 rad/m, corresponding to wavelengths between 16 m and 314 m, thereby capturing a wide range of wave conditions. Additionally, the radar incidence angles are nearly uniformly distributed between 30\({}^{\circ}\) and 46\({}^{\circ}\).  
This matched dataset is further used to statistically validate the accuracy of the radial OSC velocity retrievals derived from the Doppler shift measurements. As shown in Figure 8a, the comparison between the SAR-retrieved radial current velocities using the DPDop model and the drifting buoy measurements yields a bias of 0.01 m/s, a RMSE of 0.18 m/s, a MAE of 0.16 m/s, and a correlation coefficient of 0.96. The low error metrics and strong correlation indicate that the DPDop-based SAR retrievals provide reliable estimates of the radial OSC velocities.  
For comparison, the CDOP model was also applied to remove the wave-induced Doppler shifts, and the resulting radial OSC velocities were evaluated against the same set of drifting buoy observations, as shown in Figure 8b. In this case, the bias, RMSE, and MAE were 0.05 m/s, 0.29 m/s, and 0.24 m/s, respectively, with a correlation coefficient of 0.77. The reduced accuracy of the CDOP model is likely attributed to its dependence solely on the wind field inputs, without consideration of the wave parameters. This limitation hinders its ability to fully account for the wave-induced Doppler effects, resulting in larger retrieval errors compared to the DPDop model.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 3 System and Attack Models
### Attack Model  
The purpose of an attacker is to gain any benefit by passing the location verification. For example, the attacker announces false location information of itself to take packets from any targeted sensor when geographical routing is used to deliver data. In the other example of the tracking application, the attacker can report its false location to lie about the target's movement. To successfully cheat regarding its location information, an attacker can do the following behaviors:  
* An attacker can compromise any sensor. The compromised sensor is called a malicious sensor and defined as \(m_{i}\), where \(i=1,2,...,K\). The total number of malicious sensors in the entire network is \(K\ll N\).
* A malicious sensor, \(m_{i}\), can report false location information intentionally by sending a message including its forged location, \(fdata_{i}=\{s_{i},L_{c}^{i},\mathit{nonce}_{i}\}\), where \(L_{c}^{i}\
eq L_{\mathit{true}}^{i}\).
* The attacker tries to eavesdrop on the communications of sensors to get the necessary information to bypass the location verification.
* The attacker's ability is constrained to the compromised sensor. This means that the attacker can listen within its communication range, \(r\).
## 3 Predictive Torque Control with Switchable Delta-Star Winding Configuration  
### Considerations about Flux, Torque, and Speed Control  
#### 3.1.1 Control of Flux by Active Voltage Vectors  
PTC's principle is based on the conventional DTC strategy, which utilizes the stator voltage vector equation in the stationary \(\alpha\beta\) system for the flux and torque control. At higher speeds, i.e., higher supply frequencies, the magnitude of the stator (i.e., the leakage and magnetizing) reactance becomes much more significant than the stator resistance. Therefore, neglecting the stator resistance and considering finite differences (denoted as \(\delta\)), the stator voltage equation can be written as  
\[\frac{\delta\psi_{1}}{\delta t}\approx\underline{u}_{1}\ \ \ \Longrightarrow\ \ \delta\underline{v}_{1}\approx\underline{u}_{1}\delta t. \tag{23}\]  
Figure 4: Equivalent circuit of the inverter and delta-connected load.  
The magnitude of change of the stator flux linkage vector caused by applying an arbitrary active voltage vector for a time duration \(\delta t_{0}\) can be expressed in the case of a star-connected machine (\(K=2/3\)) as  
\[\delta\psi_{1(\Upsilon)}\approx\frac{2}{3}U_{\rm DC}\delta t_{0}, \tag{24}\]  
and in the case of a delta-connected machine as  
\[\delta\psi_{1(\Delta)}\approx\frac{2}{\sqrt{3}}U_{\rm DC}\delta t_{0}. \tag{25}\]  
A comparison of (24) and (25) shows that  
\[\delta\psi_{1(\Upsilon)}=\frac{1}{\sqrt{3}}\delta\psi_{1(\Delta)}. \tag{26}\]  
For a constant DC-link voltage value and a fixed duration of an active inverter voltage vector, the absolute value of change of the stator flux linkage vector is \(\sqrt{3}\) times higher in case of a delta-connected winding.
## 3 Multi-Domain Informative Path Planning
### Obstacle Avoidance Path Re-Planning
#### 3.2.2 Obstacle Avoidance
In the scapegoat tree method, the obstacle spots are inserted into the original _treTree_, whereas the replanning method constructs a new KD tree from obstacle spots. ```
Input: Obstacle, Obstacle_C, \(\text{segpath}\); treeTree; Output: Clusters of collision free sets around obstacles: object; \(\text{N}\leftarrow\text{number of obstacle spots}\) for\(i=1\) to \(N\)do \(\text{nTree}\leftarrow\text{Scapegoat\_insert}\;(obstacle_{x}^{i},obstacle_{y}^{i},obstacle_{z}^{i})\); \(\text{NC}\leftarrow\text{number of spots in obstacle\_C}\) \(\text{R}\leftarrow\text{v}el_{1}\) whileobset to emptydo \(\text{R}\leftarrow\text{R}+\text{v}el_{L}\) for\(\theta=0:\frac{\pi}{10}:\pi\)do for\(\varphi=0:\frac{\pi}{10}:2\pi\)do \(set_{x}=obstacle\_C_{x}^{i}+R\sin\theta\cos\varphi\) \(set_{y}=obstacle\_C_{y}^{i}+R\sin\theta\sin\varphi\) \(set_{z}=obstacle\_C_{z}^{i}+R\cos\theta\) \(Target\leftarrow(set_{x},set_{y},set_{z})\) \(\text{setdist}\leftarrow\text{kd\_search}(\text{Target, nTree})\) let setdist / \(\sqrt{2}\) be the size of \(\text{Target's corridor}\); if\(\text{Target's corridor}\) coincide with neighbour spots' in \(\text{seg\_path}\)then \(\text{obset}+=\text{target}\)
1 return Obst
```  
**Algorithm 3**Use scapegoat tree to get collision-free spots around obstacles. With _obset_ determined, our best path is then updated as \(\Phi\;\{p_{1},p_{2},\ldots\text{obsets},\ldots p_{n+1}\}\). The problem is then extended to a GTSP problem again: picking a sequence of spots within the \(n+1\) group. All these groups of spots except _obset_ contain only one spot, while _obset_ contains all the available spots on the free sphere. After that, an updated best path \(\Phi\) becomes the input for the trajectory generation algorithm.
## 5 Discussion
Aircraft reconnaissance data of the Atlantic basin, Northeast and North Central Pacific basin can be obtained from the National Hurricane Center (NHC) at its website ([[http://www.aoml.noaa.gov/hrd/data_sub/hurr.html](http://www.aoml.noaa.gov/hrd/data_sub/hurr.html)]([http://www.aoml.noaa.gov/hrd/data_sub/hurr.html](http://www.aoml.noaa.gov/hrd/data_sub/hurr.html))). With the operational deployment of the Stepped Frequency Microwave Radiometer (SFMR), hurricane reconnaissance and research aircraft provide near real-time observations of the 10 ocean-surface wind-speed, both within and around tropical cyclones [50]. In total, 13 matchups were collected in 2014-2015 (see Figure 12 below). Since the matchups were too few to recalculate the model coefficients, we directly used the coefficients obtained from Northwest Pacific region. In Figure 12, we can see that the model-estimated TC intensities did not perform well compared to the aircraft reconnaissance data. The differences among the model-estimated MWS, best-track, and aircraft measurements were very large, with MAE > 12 m/s and RMSE ~8 m/s. Both were much worse than the performance in the Northwestern Pacific Ocean (as shown in Figure 7). Hurricanes and typhoons have some differences in their structure and formation mechanisms. It is indicated that the coefficients of the proposed model were more suitable for typhoons than for hurricanes. Thus, the coefficients of the proposed model need to be tuned with enough matchup samples before its application to TCs in basins other than the Northwestern Pacific Ocean. In addition, the current comparison among SSMIS-only, HY2A-only, and their combined methods is not very rigorous. Only one parameter was tested for the SSMIS-only and HY2A-only methods, and the one-parameter approach is not the best option for estimating the TC intensity. In the future, we will need to consider more comprehensive and solid comparisons between the proposed model and other estimation models, using more sufficient ground true data.
## 4 Experimental Results and Analysis
### Evaluation Metrics  
The performance evaluation of the consider DL models is performed using the Accuracy, Precision, Recall, F1-Score, False Alarm Rate, True Negative Rate, and False Negative Rate. The basis for these evaluation metrics is the different attributes within the confusion matrix shown in Table 3. The TP and TN instances in the confusion matrix represent the correct prediction of Anomaly and Benign instances, respectively. Similarly, FN and FP instances are incorrect predictions of a classifier as Benign and Anomaly instances. The different evaluation metrics considered in this study are detailed in [74; 75; 76].  
**Accuracy:** It is calculated as the ratio of the correctly classified records to the total number of records.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
Figure 4: Numerical features of the Bot-IoT dataset arranged in descending order based on MI score.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**PREDICTED CLASS**} \\ \cline{3-4}  & & **ANOMALY** & **BENIGN** \\ \hline \multirow{2}{*}{**ACTUAL CLASS**} & ANOMALY & True Positive (\(TP\)) & False Negative (\(FN\)) \\  & BENIGN & False Positive (\(FP\)) & True Negative (\(TN\)) \\ \hline \hline \end{tabular}
\end{table}
Table 3: A Confusion Matrix.  
**Precision:** It is the ratio of correctly predicted Anomaly instances to all the instances predicted as Anomaly.  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
**Recall:** It is defined as the ratio of all the correctly predicted Anomaly instances to all the actual Anomaly instances.  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
**F1 Score:** It provides the harmonic mean of the _Precision_ and _Recall_ to examine the accuracy of the system using a statistical technique.  
\[F1\ Score=2\bigg{(}\frac{Precision\times Recall}{Precision+Recall}\bigg{)} \tag{8}\]  
**False alarm rate (FAR):** It is defined as the ratio of wrongly predicted Anomaly instances to all the Benign instances.  
\[FAR=\frac{FP}{FP+TN} \tag{9}\]  
**True-negative rate (TNR):** It is the ratio of the correctly predicted Benign instance to all the instances that are Benign.  
\[TNR=\frac{TN}{FP+TN} \tag{10}\]  
**False-negative rate (FNR):** It denotes the miss rate and shows the possibility of the classifier missing the anomaly instances. It is the ratio of wrongly predicted Benign instances to all the actual Anomaly instances.  
\[FNR=\frac{FN}{TP+FN} \tag{11}\]
## 2 Moon Raw Material Surveying Strategy
### Water Ice Deposits
In work [43], simulations of this process were carried out for water ice deposits on the Moon and Mercury. When looking for deposits of water ice and volatile substances on the Moon, you should first look for cold traps. These usually occur in craters in permanently shadowed regions (PSR). However, not every cold trap will contain water ice, or it may occur in deeper layers such that remote surface survey results suggest that the location is dry. In satellite imaging of such places, techniques should be improved to enable the analysis of deeper layers.
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.16. Telemedicine in Urology  
The advancement of telemedicine in urology, accelerated by COVID-19, has integrated remote solutions with a focus on clinical guidelines, practical impact, and future directions. Guidelines for online consultations in uro-oncology have been established to optimize remote care delivery while maintaining quality [196]. A review assessed the impact of the pandemic on urological practices and offered recommendations for adapting to new healthcare realities [197]. Research has identified key barriers to telemedicine implementation, such as technological limitations and reimbursement issues [198; 199], while examining patient perceptions revealed positive attitudes towards convenience but concerns about care quality. A recent review evaluated the evolution of telemedicine in urology and proposed enhancing teleurology through AI, remote monitoring, and personalized care models [200]. The pandemic has catalyzed telemedicine adoption in urology, prompting guideline development and practice changes. Research highlights implementation challenges while showing positive patient attitudes towards teleurology accessibility. Recent analyses have suggested incorporating digital technologies to improve outcomes, although more empirical studies are needed to evaluate the impact of telemedicine on urological outcomes in Poland and beyond.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 3 UWB Positioning System
### Optimization Filters and Fusion-Based Algorithms for IPSs  
Since the emergence of indoor positioning systems, and due to the margin of error in determining the correct location, important attempts have been made to reduce the error resulting from the delay time in the antennas or from the passage of waves through obstacles. The antenna delay time occurs when signals are exchanged between ultra-wideband units; a time delay occurs in transmission and reception as the signal propagating through its analog circuits suffers from delays in the transmitting and receiving antennas. Such delays can lead to range estimation errors between system units and, ultimately, affect the accuracy of real-time positioning systems unless these errors are measured and corrected [23]. Moreover, the delay resulting from the passage of signals through obstacles leads to an error in the accuracy of the system's performance. This causes incorrect measurements of the wave propagation time (ToF), through which the distances between the system units are calculated to determine the objects' location. Many methods, algorithms, and filters have been used to reduce this type of error. In the following sections, the most important of them are briefly explained.
## 2 Materials and Methods  
### Determination of the Polyamine Content of Commercial EN Formulas via HPLC  
Sixteen commercial EN formulas, all currently being administered to patients in the ICU at the Hospital Universitario Central de Asturias in Spain, were obtained to analyse their polyamine content. Multiple samples from the same batches and from different batches were analysed when available.  
The amines were determined by means of HPLC using a pre-column derivatisation method, as previously described [45]. From each EN formula sample, 950 uL was taken and treated with perchloric acid for 10 min at 4 \({}^{\circ}\)C to reach a final concentration of 15.8%. The extracts were then centrifuged at 10,000\(\times\)\(g\) for 30 min at 4 \({}^{\circ}\)C, later 0.2 mL of supernatant was collected and the standard concentration (1.25 uL of a concentration of 1 mM) was added. The sample was neutralised with 0.3 mL of a saturated solution of NaHCO\({}_{3}\), and the samples were dansylated overnight (\(\sim\)16 h) with 0.5 mL of a solution containing 5 mg/mL of dansyl chloride in acetone. The next day, 0.1 mL of 100 mg/mL proline was added for 30 min; then, 0.5 mL of toluene was added. Finally, 0.4 mL of the organic phase was removed and dried under a nitrogen atmosphere at 42 \({}^{\circ}\)C in a thermoblock (Techne Dri-Block DB3, Cambridge, UK) and then resuspended in 0.2 mL of acetonitrile (VWR, Rosny-sous-Bois-cedex, France).  
The HPLC was a Shimadzu Prominence (Shimadzu, Kyoto, Japan) using a C\({}_{18}\) (2.5 um, 3.0 \(\times\) 75 mm) reverse-phase column (XBridge, Waters, Milford, MA, USA) at room temperature between 21 and 23 \({}^{\circ}\)C and equipped with two LC-20 AD pumps, a fluorescence detector (RF-20A), a SIL-20 AD HT autosampler injector at 4 \({}^{\circ}\)C, and a DGU-20A3 degasser (all from Shimadzu, Kyoto, Japan). The binary gradient used a flow rate of 0.55 mL/minutes as follows: 0-0.5 min, 0% B; 0.5-10 min, 75% B; 10-11.30 min, 75% B; and 11.3-11.50 min, 0% B. Solvent A was 45% acetonitrile and solvent B was 100% acetonitrile. The injection volume was 5 uL.  
The duration of the chromatogram was 14 min. The identification of the compounds was performed using their retention time in minutes (standard: 5.88; \(N\)-acetyl putrescine: 1.56; \(N\)-acetyl spermidine: 6.11; \(\beta\)-phenylethylamine: 6.82; isoamylamine: 7.04; putrescine: 7.38; cadaverine: 7.83; \(N\)-acetyl spermine: 9.41; tyramine: 9.89; spermidine: 10.38; spermine: 12.41) and a wavelength of excitation of 365 nm and emission of 510 nm. All measurements were made using the Shimadzu LCsolution Version 1.25 software. The data analysed were the means of duplicate injections. The quantification of the biogenic amines was performed using 1,3-Diamino-2-hydroxypropane as an internal standard, expressed as nmol/mL of the EN formulas.