esa-sceva/satcom-chunk-collection huggingface dataset:
# Heterogeneity of Ex Vivo and In Vivo Properties along the Length of the Abdominal Aortic Aneurysm
## 3 Results
### Ex Vivo Analysis
#### 3.1.3 Markers of Inflammation  
A subgroup of 7 patients allowed for the collection of aortic tissue specimens for which immunohistochemical analysis was feasible. Cell count for inflammation was therefore performed on 46 aortic samples, while several specimens had a very thin or absent media layer determining their exclusion from the analysis. The average cell count was 37. 8 \(\pm\) 49.4 cells/mm\({}^{2}\) and 183.1 \(\pm\) 429.5 cells/mm\({}^{2}\) for the helper T-cells (CD4+) in the media and adventitia layer respectively, 42.0 \(\pm\) 49.9 cells/mm\({}^{2}\) and 92.8 \(\pm\) 127.3 cells/mm\({}^{2}\) for cytotoxic T-cells (CD8+), and 22.2 \(\pm\) 29.1 cells/mm\({}^{2}\) and 45.3 \(\pm\) 37.9 cells/mm\({}^{2}\) for the macrophages (CD68+). Figure 6 shows a representative immunostaining image from IHC analysis with inflammatory infiltrate of CD4+, CD8+, and CD68+ cells for two AAA specimens.  
Overall, the adventitia layer showed a significantly higher presence of inflammation markers compared to the media layer (\(p<0.05\) for CD4+, \(p<0.05\) for CD8+, \(p<0.01\) for CD68+). The adventitia layer also showed an effect of position observed for the CD4+ and CD8+ markers, with specimens collected from the central regions of the aorta, located in the aneurysm dilatation, showing higher content of the two inflammatory infiltrates (Figure 7a,b). Of note, the samples collected from region 2 corresponding to the aneurysm neck showed statistically significant lower presence of CD4+ and CD8+ markers in the adventitia layer compared to samples in position 4 (\(p=0.036\) and \(p=0.004\) for CD4+ and CD8+ respectively). No significant differences were observed in the media layer in terms of inflammation and inflammatory infiltrate content with respect to position.  
Figure 5: Heterogeneity of content along the circumference of the AAA for the aortic wall constituents (**a**) elastin, (**b**) smooth muscle cells, and (**c**) proteoglycans in the media of histological aortic samples. * indicates \(p\) value \(<0.05\), ** indicates \(p\) value \(<0.01\).
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 3 Results and Analysis  
To verify the reliability and stability of the multi-function hydrophone, an experiment was conducted in Wuyuan Bay, Xiamen City, Fujian Province, China. The deployment of the experiment is shown in Figure 10a. The depth of the experimental area was about 10 m. The distance between the transmitter and the proposed hydrophone was about 1 km. And seven multi-function hydrophones were deployed at different depths with a uniform space of 1.2 m. Two transducers with different center frequencies were used to evaluate the performance, the source level of the two transducers is 190 dB @ uPa. The depth of the transmitters was about 5 m. In the experiment, the sea was relatively calm with level 2 sea conditions.  
Figure 10b shows the sound speed profile (SSP). Due to the sun exposure, the SSP shows a negative gradient.  
Figure 8: Schematic diagram of M-ary spread spectrum.  
Figure 9: Control interface of the host computer.  
The gain of AD620 was fixed at 10 V/V, and the gain of LTC6910-2 was 64 V/V, so the total gain was 56.1 dB (640 V/V). In mode 1, the sampling frequency was 128 kHz, while in mode 2 and mode 3, the sampling frequency was 64 kHz.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 6 State-of-the-Art of CR-Based UAVs  
We conducted a review in November 2020 in search of works involving UAVs and SDR and/or CR. The main idea was to find the state-of-the-art of CR-based UAVs. As SDR-based UAVs represent somehow a step backwards for CR-based UAVs, they were also included in this review. We evaluated a total of 520 publications from four different digital libraries. However, the majority of these articles were identified marginally related to the scope of this work. Thus, the final studies dataset obtained after analyzing them regarding the following exclusion criteria consisted of 64 articles:  
1. non-English publications. Although we used English language keywords, we found a few publications in other languages. We had to exclude them merely because we would not be able to analyze them in depth;
2. papers that are not downloadable online;
3. publications that are not related to CR, SDR, or UAVs as defined in this work (other research fields);
4. papers focusing on CR or SDR applications other than UAVs wireless communication. Figure 11 outlines the number of publications per year, among those selected in this review. It shows an increasing interest over the recent years in this research field. Although the year of 2020 was evaluated from January until November, it already has a high number  
Figure 10: Data collected using the CR platform. It shows a map of the level in dB measured within the frequency range from 432 MHz to 441 MHz throughout the time. of publications. Note that a number of papers might have been published from January 2020 to November 2020, but were not yet available online. Figure 12 presents keywords graph and occurrence mapping for all 64 articles selected on this review. We consider keywords with at least two occurrences and at least one link to other keywords. Thereby, the keyword mapping consists of 34 keywords in total. In Figure 12, it is possible to note an expressive amount of links correlating UAV-related keywords and SDR-related keywords. However, although CR-related keywords are present in Figure 12, there are few links correlating them to UAVs. Thus, Figure 12 outlines the paucity of studies on the integration of CR and UAVs. In order to measure how fit each selected publication is to CR-based UAVs research field, we analyzed each paper regarding the following aspects:  
1. the work presents a theoretical analysis (Q1);
2. the work presents practical results (Q2);
3. the work involves a real SDR-based UAV (Q3);
4. the work is centered on CR-based UAVs (Q4);
5. the work involves a real CR-based UAV (Q5). For each publication, each aspect is analyzed, and it is evaluated as \"yes\", if the aspect is present in the paper, or \"no\" otherwise. Table 2 presents all selected publications in this review, as well as their publication year and aspects analysis. Figure 11: Related publications per year. The year of 2020 was evaluated from January until November. Figure 12: Keywords graph and occurrence mapping for all 64 articles selected on this review.
## 6 Conclusions  
(1) The research results regarding the effectiveness of geo-electrochemical integrated technology in mineral exploration along the L00 line demonstrate that this technology can accurately identify the locations of deep ore bodies. It is evident that a technical approach primarily based on geo-electrochemical extraction and measurement--complemented by soil ionic conductivity and thermally released soil mercury, is both feasible and effective in identifying concealed carbon-silicon-mudstone uranium minerals within the study area;  
(2) Based on the geological characteristics, geochemical features, and genetic analysis of the deposit in the study area, the formation of carbonate rocks and siliceous rocks, interlayer compression fracture zones, cross-cutting fault zones, and surrounding rock alteration are identified as key geological indicators in prospecting. Abnormalities in primary ore-forming elements such as U, Th, and Mo serve as direct prospecting indicators, while W, As, and Pb function as indirect indicators. Additionally, soil ionic conductivity and thermally released soil mercury are considered significant prospecting indicators;  
(3) Based on the convergence sites of U-Th-W-Mo-As-Pb anomalies and in conjunction with metallogenic geological conditions, one key anomaly verification area, one Class A comprehensive anomaly area, two Class B comprehensive anomaly areas, and one Class C comprehensive anomaly area were delineated. With respect to the Guangzhou deposit, this provides crucial technical support and a theoretical foundation for the principles of \"exploring the edges and identifying the bottom, delving deep and uncovering blind spots\" and \"connecting points to form a surface\" in the uranium mining areas in northern Guangxi.  
Figure 13: The comprehensive geological geodesic anomaly profile of Line 11.  
**Author Contributions:** Writing--original draft preparation, X.Z. and Y.M.; Writing--review and editing, M.W. and Q.L.; Collecting the samples, X.Z., Y.M., Y.J. ([PERSON]), W.Y., J.Z. and M.W.; Software and data processing, X.Z. and Y.J. ([PERSON]); Funding acquisition, M.W. and Q.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 42163004) and the Guangxi Natural Science Foundation (2025 GXNSFAA069927).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks
## 2 NOMA System Model
#### Problem Formulation  
The optimization problem is formulated for resource allocation. In mobile communication networks, the optimization aims to maximize the system's capacity with minimum power allocation to achieve Throughput maximization. In the NOMA system, each subchannel is assigned by power \(P_{n}\), and the throughput over subchannel \(n\) is defined as \(R_{n}\). The total throughput is given as:  
\[R_{Total}=\sum_{n=1}^{N_{\text{nc}}}R_{n} \tag{7}\]  
In the downlink NOMA system, only two users are multiplexed on the same subchannel to reduce the complexity of the SIC performance. In this system, two users share the same subchannel \(n\) while considering \(G_{1,n}\geq G_{2,n}\), and the throughput of the subchannel with respect to \(P_{n}\) can be expressed in (8).  
\[f(P_{n})=B_{\text{sc1,n}}log_{2}(1+\delta_{n}P_{n}G_{1,n})+B_{\text{sc2,n}}log _{2}\bigg{(}1+\frac{(1-\delta_{n})P_{n}G_{2,n}}{1+\delta_{n}P_{n}G_{2,n}}\bigg{)} \tag{8}\]  
where \(\delta_{n}\) is the proportional power factor for the two multiplexed users on subchannel \(n\). In general, the \(\delta_{n}\in(0,1)\) and the user with high channel gain always performs SIC. The proposed user pairing scheme is deployed to ensure the optimality of the power allocation scheme and guarantee that more power is assigned to the second user who is experiencing weaker channel conditions. In the system, the throughput optimization problem is formulated as below:  
\[\max_{P_{n}>0}\sum_{n=1}^{N_{\text{nc}}}\sum_{m=1}^{U_{n}}R_{m,n}(P_{m,n})=\sum _{n=1}^{N_{\text{nc}}}R_{n}(P_{n}) \tag{9}\]  
subject to:  
\[\textbf{C}_{1}:\sum_{n=1}^{N_{\text{nc}}}P_{n}\leq P_{Max} \tag{10}\]  
\[\textbf{C}_{2}:\sum_{n=1}^{N_{\text{nc}}}R_{m,n}(P_{n})\leq R_{Min} \tag{11}\]  
The objective function is limited by the summation of the subchannels power not exceeding the maximum transmit power \(P_{Max}\) according to \(C_{1}\). Also, the minimum user's throughput is ensured as a minimum target throughput in \(C_{2}\). The resource allocation in NOMA is considered a non-convex and NP-hard problem. Therefore, finding the global solution within the polynomial-time is a challenge. The subchannel, user assignment, and power allocation are separated to solve the coupled problem.  
Figure 2: SIC application in NOMA for each multiplexed user.
## 4 Energy and Power Optimization Allocation
### Step-by-Step Optimization Scheme
#### 4.2.2 Optimization for Asynchronous Networks  
For the single-slot asynchronous ISAL network, there are three steps in the step-by-step scheme, and the power optimization allocation strategy is as follows:  
Step 1: Radar Localization  
\[\mathfrak{A}_{7}^{\rm SDP}: \min\quad\operatorname{tr}(\mathbf{H}_{\rm r})_{\mathbf{p}_{\rm r}}\] (66) s.t. \[\mathbf{H}_{\rm r}\succeq 0 \tag{67}\] \[\left[\begin{array}{cc}\mathbf{H}_{\rm r}&\mathbf{I}\\ \mathbf{I}&\mathbf{J}_{\rm R}^{\rm R}\end{array}\right]\succeq 0\] (68) \[\sum_{j\in\mathcal{N}_{\rm a}\cup\mathcal{N}_{\rm r}}P_{j}\leq E_{ \rm step1}/1\] (69) \[0\leq P_{j}\leq P_{\rm r,max},\forall j\in\mathcal{N}_{\rm r}\] (70) \[0\leq P_{j}\leq P_{\rm a,max},\forall j\in\mathcal{N}_{\rm a}; \tag{71}\]  
Step 2: Clock Offset Estimation  
\[\mathfrak{A}_{8}^{\rm SDP}: \min\quad\operatorname{tr}(\mathbf{H}_{\rm r,\tau})_{\rm r}\] (72) s.t. \[\mathbf{H}_{\rm r,\tau}\succeq 0 \tag{73}\] \[\left[\begin{array}{cc}\mathbf{H}_{\rm r,\tau}&\mathbf{I}\\ \mathbf{I}&\mathbf{J}_{\rm sub}\end{array}\right]\succeq 0\] (74) \[\sum_{j\in\mathcal{N}_{\rm a}\cup\mathcal{N}_{\rm r}}P_{j}\leq E_ {\rm step2}/1\] (75) \[0\leq P_{j}\leq P_{\rm r,max},\forall j\in\mathcal{N}_{\rm r}\] (76) \[0\leq P_{j}\leq P_{\rm a,max},\forall j\in\mathcal{N}_{\rm a}; \tag{77}\]  
Step 3: Target Sensing  
\[\mathfrak{A}_{9}^{\rm SDP}: \min\quad\operatorname{tr}(\mathbf{H}_{\rm r,\tau,t})_{\rm p_{\rm t}}\] (78) s.t. \[\mathbf{H}_{\rm r,\tau,t}\succeq 0 \tag{79}\] \[\left[\begin{array}{cc}\mathbf{H}_{\rm r,\tau,t}&\mathbf{I}\\ \mathbf{I}&\mathbf{I}_{\rm\theta}^{\rm asyn}\end{array}\right]\succeq 0\] (80) \[\sum_{j\in\mathcal{N}_{\rm a}\cup\mathcal{N}_{\rm r}}P_{j}\leq E_ {\rm step3}/1\] (81) \[0\leq P_{j}\leq P_{\rm r,max},\forall j\in\mathcal{N}_{\rm r}\] (82) \[0\leq P_{j}\leq P_{\rm a,max},\forall j\in\mathcal{N}_{\rm a}, \tag{83}\]  
where \(\mathbf{H}_{\rm r}\), \(\mathbf{H}_{\rm r,\tau}\), and \(\mathbf{H}_{\rm r,\tau,t}\) are the auxiliary matrices; the total energy \(E_{\rm total}=E_{\rm step1}+E_{\rm step2}+E_{\rm step3}\); \(\mathbf{J}_{\rm sub}=(\mathbf{I}_{\rm\theta}^{\rm asyn})_{1:2N_{\rm r}+| \mathcal{N}_{\rm r}|,1:2N_{\rm r}+|\mathcal{N}_{\rm r}|}\); and there is prior knowledge of \(\mathbf{J}_{\rm e}^{\rm R}\) obtained from step 1 in \(\mathbf{J}_{\rm sub}\) in (74), as well as a prior knowledge \(\mathbf{J}_{\rm sub}\) obtained from step 2 in \(\mathbf{I}_{\rm\theta}^{\rm asyn}\) in (80).
## 2 System Description and Problem Statement
### Active and Reactive Power Control  
As described in Figure 2, the voltage control system whose design is the goal of this work is a \"slave\", one in a hierarchical scheme, where voltage references come from active and reactive power control blocks, based on P/V Q/\(\omega\) droops (DR-based WPP requires P/V Q/\(\omega\) droop instead of P/\(\omega\) Q/V droop to avoid a centralised control to change the off-shore ac voltage for power transmission by the DR [20]). The active power transmission depends on the DR voltage, which can be calculated as follows:  
\[V_{Rdc0}-V_{Rdc}=\frac{3}{\pi}B\omega_{0}L_{TR}I_{Rdc}+2 BL_{TR}\frac{dI_{Rdc} }{dt} \tag{3}\]  
where \(V_{Rdc0}=(3\sqrt{6}/\pi)BNV_{Fd}\), \(B\) is the rectifier bridges number, \(N\) and \(L_{TR}\) are the DR transformer ratio and leakage reactance and \(V_{Fd}\) is the off-shore ac voltage in dq frame.  
The power control design is proposed in [21]. The power controllers are the following:  
\[|V_{\mathbf{C}}^{*}|=\left(K_{P}+K_{I}\frac{T_{s}}{z-1}\right)(P_{WT}^{*}-P_{ WT}) \tag{4}\]  
\[\delta=\left(K_{P}+K_{I}\frac{T_{s}}{z-1}+K_{D}\frac{z-1}{z}\right)(Q_{WT}^{* }-Q_{WT})+\omega_{0}\frac{T_{s}}{z-1} \tag{5}\]  
The power control tuning does not consider the inner \(\mathcal{H}_{\infty}\) voltage control loop. Nevertheless, as examples later show, there are interactions because time constants are similar in some cases; we would recommend checking possible design alternatives, using a combined voltage/power multivariable control that, however, is intentionally out of the scope of this contribution.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
## 3 Proposed Method: Policy-Based Smart Contract Management for IoT Privacy Preservation
### Predefined Smart Contract Types
For instance, a sudden health data spike might trigger an automated policy adjustment to send urgent notifications to healthcare providers (urgent smart contract). Additionally, users can customize preferences to control data sharing frequency, data types, and the use of private channels.
## 3 Transmission Point Selection in Multi-Band Multi-Connectivity Operation
### Problem Statement  
Our objective is to select transmission points (or base stations), providing maximizing the system performance. In other words, our problem (i.e., transmission point selection) can be formulated as minimizing the total amount service time, denoted by \(T\), which is dependent on the achievable data rate \(r_{k}^{n}\) if it is served by TP \(n\). Thus, we can write the solution as follows:  
\[\min T=\min\Biggl{\{}\sum_{n\in\mathcal{N}}T_{n}\Biggr{\}}=\min \Biggl{\{}\sum_{n\in\mathcal{N}}\sum_{k\in\mathcal{K}_{n}}t_{k}^{n}x_{k}^{n} \Biggr{\}},\] (4) subject to \[\sum_{n\in\mathcal{N}}x_{k}^{n}\leq C,\forall k\in\mathcal{K}, \tag{5}\] \[x_{k}^{n}\in\{0,1\},\forall n\in\mathcal{N},\forall k\in \mathcal{K},\] (6) \[x_{k}^{n}=0,\forall n\in\mathcal{N},\forall k\
otin\mathcal{K}_{ n}, \tag{7}\]  
where \(C(1\leq C\leq N)\) is the allowed number of connections for user \(k\). In addition, \(\mathcal{K}_{n}:=\mathcal{K}_{n}\cup\{k\}\) and \(x_{k}^{n}=1\) when TP \(n\) is selected and \(x_{k}^{n}=0\), otherwise. For instance, if \(C=1\), only one connection is allowed, i.e., the network operates with a single connectivity.  
Figure 1: Examples of multi-band multi-connectivity operation in mobile communication networks: (**a**) data transmission with a connectivity from single band (e.g., user 1 with FR2, user 2 with FR3, and user 3 with FR1), (**b**) data transmissions with two connectivities from two bands (e.g., user 4 with FR2 and FR3 and user 5 with FR1 and FR2), and (**c**) data transmissions with three connectivities from three bands (e.g., user 6 with FR1, FR2, and FR3).
## 2 Background Study
### Rainfall Rate Missing Data  
#### 2.8.1 Temporal Missing: Time Series (TS)  
The rain attenuation prediction requires long-term local rainfall rate information. However, in all parts of the world, such rainfall rate datasets are not available. In addition, even though the rainfall rate or rain attenuation datasets are recorded for a specific experimental setup, some parts of the dataset might be missing. To address these issues, a rainfall rate or attenuation time series needs to be generated. Fortunately, there are techniques available such as the synthetic storm technique (SST) [63] and enhanced synthetic storm technique (ESST) [64], and 2-D SST [65]. Rain attenuation prediction models such as the global synthetic storm technique (GSST) model [66] and the ESST model [64] use such types of time series generation procedures. Through applying such a procedure, as stated earlier, the missing datasets of any local climate can be generated.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 6 On-Field Measurement Campaigns  
A first live test campaign was conducted in order to assess the capability of NGeneApp to catch RF disturbances in real-time and real-life environment. Figure 7a shows the test setup: a smartphone equipped with NGeneApp, a pocket-size FE and a portable patch antenna. We started to map the RF interferences, walking along the streets of the center of Turin, Italy, using this small and lightweight portable equipment. The PSD estimates are displayed on the smartphone and sent to the remote server with a rate of 1 H, increased to 5 Hz when a disturbance is noticed. The GUI and a sample spectrum of received signal are represented in Figure 7b,c, respectively.
## 3 Testbed Implementation,Prototype, and Software Development
### Software Development  
For processing the data from the lead-acid batteries in the microcontroller, C programming language is used. The data as shown in Table 1 is processed in the microcontroller using C language. The different data fields of the battery such as the Battery ID, Temperature, Humidity, Acid Level Sensor, Remaining Capacity, Full Charge Capacity, Voltage, State of Charge, and the Average Current  
Figure 11: Final developed board of the proposed prototype.  
Figure 10: Schematics of the different components of the proposed system.  
as shown in Figure 9 constitute one data frame of the battery. The same data from an individual battery is cumulated into one data frame and sent periodically. The individual data frame of the battery is sent from the Wizfi to the server for further manipulation for a meaningful representation of the battery data display.  
C\(\sharp\) programming language is used to design the TCP/UDP server graphical user interface (GUI) for displaying the battery data. For our proposed battery monitoring system, we have designed both TCP- and UDP-based server socket programming. UDP is an alternate solution to the more commonly used TCP. The major difference between the two, with respect to sending data is that while the TCP has handshaking, the UDP does not have a handshaking mechanism. However, the UDP is more efficient than the TCP when sending the same data to multiple receivers.  
Choosing between the TCP and UDP is often an issue when streaming data over a network. The TCP will guarantee that all data is received at the logging side if a sensor is sending data to a logging application, for a point-to-point application. A more advanced TCP server-client code is needed if multiple clients would like the same guaranteed service. We get more network traffic once the data is transmitted to each client. If there are multiple clients that can tolerate some data loss, owing to the lack of handshaking, we can implement the communication using UDP. If the clients only listen to the stream, then it is far easier to use UDP for distributing one stream to many clients than using TCP, implementation-wise. A server socket programming framework for our proposed system is shown in Figure 12.
## 2 System Architecture
### Zigbee Communication Protocol  
The ZigBee communication protocol was developed by Honeywell [7]. The protocol saves power, is cost-effective, supports large network nodes and multiple networks, and is fast, stable, and secure. The technology used by ZigBee is based on the Institute of Electrical and Electronics Engineers 802.15.4 communication protocol standard, which specifically defines the relevant network architecture for the physical layer and the media access control layer. The ZigBee specification further enhances security by designing additional measures in the upper layers, complementing the IEEE standard. Zigbee and other IEEE 802.15.4-based wireless personal area networks are vulnerable to various security threats and attacks. The IEEE 802.15.4 specification provides security services, such as data confidentiality, message integrity, and protection against replay attacks. The keys used in ZigBee security include master keys, link keys, and network keys. Zigbee has significant potential for deployment in wireless sensor networks and the Internet of Things. The ZigBee Alliance has additionally formulated the network layer and application layer. The ZigBee network supports three frequency bands: 2450 (worldwide), 915 (United States), and 868 MHz (Europe). The features of ZigBee include ultra-low power consumption with low data rate transmission, fewer compatibility issues with devices and platforms, and support for encryption and authentication protocols. Table 1 presents a summary of the international frequency bands of the ZigBee communication protocol. The 2.4 GHz band is a free frequency band specifically designated for industrial, medical, and scientific research purposes (ISM band). It can be used without the need for an application or licensing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency** & **Region** & **Channel** & **Transmission Rate** \\
**(MHz)** & **Region** & **(Numbers)** & **(kbps)** \\ \hline
868 & Europe & 1 & 20 \\
915 & USA & 10 & 40 \\
2450 & Global & 16 & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 1: International frequency bands of the Zigbee communication protocol.  
Figure 2: Operation of the proposed home monitoring system.