esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 4 System Model
### Network Model
If we note \(m_{i}\in\mathbb{N}\) the slot in which node \(i\) transmits, and \(L\) the frame length, we can minimize \(L\) as follows:  
\[\text{Minimize }L \tag{2}\]  
Subject to:  
\[L-t(i) \geq\max\tau_{ijh} \forall j\in N(i);\forall h\in\Theta(i,j) \tag{3}\] \[t_{i}-t_{j}-M\delta_{ij} \geq\tau_{ijh}+1-M \forall i\in V;\forall j\in N(i);\forall h\in\Theta(i,j)\] (4) \[t_{i}-t_{j}-M\delta_{ij} \leq\tau_{ijh}-1 \forall i\in V;\forall j\in N(i);\forall h\in\Theta(i,j)\] (5) \[t_{j}-t_{k}-M\omega_{ijk} \geq\tau_{ikh}-\tau_{ijg}+1-M \forall i\in V;\forall j,k\in N(i);\forall h\in\Theta(i,k); \forall g\in\Theta(i,j)\] (6) \[t_{j}-t_{k}-M\omega_{ijk} \leq\tau_{ikh}-\tau_{ijg}-1 \forall i\in V;\forall j,k\in N(i);\forall h\in\Theta(i,k); \forall g\in\Theta(i,j)\] (7) \[\delta_{ij} \in\{0,1\} \forall i\in V;\forall j\in N(i)\] (8) \[\omega_{ijk} \in\{0,1\} \forall i\in V;\forall j,k\in N(i) \tag{9}\]  
where \(M\) is a sufficiently large constant, and \(\delta_{ij}\) and \(\omega_{ijk}\) are binary variables that are used to implement the inequality conditions in the ILP solver, e.g., constraints (8) and (9). Constraint (3) limits the minimum length of the frame to the maximum transmission delay of the edges, in such a way that it considers both transmission and reception of the message in each neighbor node. Constraints (4) and (5) prevent the Tx-Rx conflict, making that messages arriving from neighbor nodes do not have to collide with the message being transmitted in the node. Constraints (6) and (7) are used to prevent collisions of type Rx-Rx. Thus, neighbor nodes should transmit their messages in such a way that they do not collide on any node within transmission range. In the case of these four constraints, all possible edges are explored to consider all possible multipath cases. The use of TDMA supposes a strict clock synchronization and also the network topology must be known by the nodes. As mentioned in Section 1, in [14] the authors introduced a hierarchical synchronization protocol, where the network topology is discovered and the nodes learn about it. Once \begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(V\) & the set of nodes \\ \(u,\,v,\,i,\,j,k\) & nodes \\ \(N(i)\) & the set of neighbor nodes of \(i\) \\ \(\Theta(u,v)\) & the set of edges between \(u\) and \(v\) \\ \(t_{i}\) & the slot in which node \(i\) transmits \\ \(m_{ij}\) & a message from node \(i\) to node \(j\) \\ \(\tau_{uzh}\) & the transmission delay between \(u\) and \(v\) \\ \(P_{ij}\) & transmission period of message from node \(i\) to \(j\) \\ \(D_{ij}\) & deadline of message from node \(i\) to \(j\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model notation. the node's clocks have been synchronized and the slot assignment computed, message transmissions are used to keep updated the network topology. Within each message, clock synchronization messages can be included in the header together with information about the aliveness of the nodes.
## 3 Materials and Methods
### The Shelf-Life Experimental Plan  
The shelf-life study was carried out, including three factors of potential variability:  
1. EVOO samples: Two samples from 2023 were selected and labelled as EVOO1 and EVOO2.
2. Storage time: The oils were analyzed at four different storage times over one year:Time 0 (t0): Immediately after production.  
Time 1 (t1): After 2 months of storage.  
Time 2 (t2): After 5 months of storage.  
Time 3 (t3): After 12 months of storage.  
These time points (2 months, 5 months, and 12 months) were chosen instead of regular intervals (e.g., 4, 8, 12 months) to better capture early-stage changes in oil quality.  
Bottle size: Two bottle sizes were selected: 250 mL (B250) and 500 mL (B500), both provided by Fara Srl Vetrerie E Cristallerie (Montespertoli, Italy). This choice was based on the hypothesis that larger bottles better preserve oil quality, similarly to what happens in other beverages like wine [43; 44].  
All samples were stored under ideal conditions: no light exposure, no exposure to oxygen other than the bottle headspace, room temperature (i.e., <25 \({}^{\circ}\)C over the whole storage period). Each type of sample was stored and analyzed in triplicate for a total of 48 samples (2 EVOOs \(\times\) 2 bottle sizes \(\times\) 4 storage times \(\times\) 3 replicates). The following chemical parameters were analyzed: free acidity, peroxide value, spectrophotometric indices, fatty acid composition, tocopherols, phenolic compounds, and VOCs.
# Assessment of a Mid-Fidelity Numerical Approach for the Investigation of Tiltrotor Aerodynamics
## 1 Introduction
Indeed, the scope of the work is to investigate the suitability of a mid-fidelity solver for the preliminary design of complex rotorcraft architectures, such as tiltrotors, which require a huge number of numerical simulations. In particular, the work was performed in the framework of the FORMOSA project, which aimed at the design of the novel wing movable surface system of the NextGen Civil Tiltrotor aircraft developed within the framework of the Clean Sky 2-H2020 Programme. In the present paper, the results of the DUST simulations over a complete tiltrotor model in different flight conditions are compared with the results that were obtained using a numerical approach with a higher fidelity, such as URANS simulations. In particular, the test case considered in the present work is the XV-15 aircraft due to the accessible and comprehensive data available in the literature. Indeed, this vehicle was commonly considered as a reference to validate fluid dynamics code for tiltrotor applications, as achieved, for example, by [PERSON] et al. [32]. The numerical activity performed with DUST considered the steady-state conditions of the complete XV-15 tiltrotor aircraft in hover, conversion, and cruise conditions to be compared with the recent results that were obtained with the URANS CFD solver presented in the works by [PERSON] et al. [16,17]. Hereafter, the results obtained from these reference works will be outlined as CFD results. The extensive assessment described in the present work clearly indicates the limits and robustness of a mid-fidelity numerical approach with respect to higher-fidelity CFD solvers for the investigation of the main interacting flow features that characterise tiltrotor aerodynamics. Moreover, the results that are presented in this work indicate that mid-fidelity solvers can be considered to be valuable tools for engineers for use in the preliminary phase of the design process of unconventional VTOL aircraft. Consequently, the results presented in this work represent a thorough assessment of a novel milestone in the computational fluid dynamics tools used by the aerospace community for the development and study of the aerodynamics of novel aircraft architectures. The paper is organised, as follows. Section 2 outlines the numerical approach implemented in DUST. Section 3 provides the description of the numerical models implemented for the study of the XV-15 tiltrotor aerodynamics, along with the validation of the parameters used for DUST simulations by means of comparison with the experimental data available for the single rotor in hover. Section 4 presents the main results that were obtained in DUST compared with the CFD simulation results available in the literature, starting from a simplified configuration made up of a single wing and rotor to the complete tiltrotor aircraft in different flight conditions. Conclusions are drawn in Section 5.
###### Abstract  
The microwave wireless power transfer (MWPT) technology has found a variety of applications in consumer electronics, medical implants and sensor networks. Here, instead of a magnetic resonant coupling wireless power transfer (MRCWPT) system, a novel MWPT system based on a frequency reconfigurable (covering the S-band and C-band) microstrip patch antenna array is proposed for the first time. By switching the bias voltage-dependent capacitance value of the varactor diode between the larger main microstrip patch and the smaller side microstrip patch, the working frequency band of the MWPT system can be switched between the S-band and the C-band. Specifically, the operated frequencies of the antenna array vary continuously within a wide range from 3.41 to 3.96 GHz and 5.7 to 6.3 GHz. For the adjustable range of frequencies, the return loss of the antenna array is less than \(-15\) dB at the resonant frequency. The gain of the frequency reconfigurable antenna array is above 6 dBi at different working frequencies. Simulation results verified by experimental results have shown that power transfer efficiency (PTE) of the MWPT system stays above 20% at different frequencies. Also, when the antenna array works at the resonant frequency of 3.64 GHz, the PTE of the MWPT system is 25%, 20.5%, and 10.3% at the distances of 20 mm, 40 mm, and 80 mm, respectively. The MWPT system can be used to power the receiver at different frequencies, which has great application prospects and market demand opportunities.  
microwave wireless power transfer; antenna array; power transfer efficiency; frequency reconfigurable +
Footnote †: journal: _articles_
# An Energy-Efficient Fail Recovery Routing in TDMA MAC Protocol-Based Wireless Sensor Network
## 5 Results and Discussion  
In this section, we discuss the performance analysis of our fail recovery routing approach. To evaluate, we compare the simulation results of the proposed method with the existing ESCFR and DCFR algorithms. These algorithms forward the data of sensor nodes to the base station using a backbone formed by a particular set of nodes. The nodes in the backbone are selected based on a cost function. Before the actual data forwarding, each node uses the cost function to identify the minimum power for the current transmission and the neighbor node with the maximum remaining energy. This backbone can be changed at any time if the result of the cost function becomes less optimal for the corresponding chain of the backbone.  
Many previous articles such as References [21; 22; 23; 24] report that the network lifetime drastically changes when the variation in the number of nodes or in the node's transmission range occurs. Therefore, during the simulation, we use these network parameters as a varying argument.
# Enhancing the Privacy of Network Services through Trusted Computing
## 3 A Trusted and Backward-Compatible Addressing Service
### Optimization for SGX  
We use DoTT to encrypt and decrypt the DNS stub and NS to ensure the privacy and security of DNS messages. However, SGX is only an extended instruction set of CPU, which can only perform various arithmetic operations and lacks the network communication function required by TLS. Therefore, we started a network listening process and a network request process in the untrusted part of DoTT, one for receiving DNS query requests, the other for forwarding requests to NS and receiving resolution results. The ECALL/OCALL overhead of SGX will also reduce the processing performance of DoTT. To reduce the forwarding operation of ECALL/OCALL between the system and the enclave, we also started two threads in the DoTT enclave, one of which will immediately respond and decrypt the message after the external process receives the query request. When receiving the resolving result, the other thread will use the session key to perform an encryption operation and return the result to a DNS client in the form of ciphertext.  
We set up a cache in the enclave to improve the resolving speed of DoTT. Every time DoTT responds to a DNS query request, it first checks the previous response list. It directly extracts the result from the list and encrypts them if the request has been parsed before, thus omitting the interaction with NSs. However, all available physical memory of an enclave is limited to a maximum of 128 M. Thus, the cache list will soon be full. To overcome this problem, we maintain a cache list with a size of 20 M. If the list exceeds this size limit, we use sgx_seal to save part of the cache on a disk. When a new DNS request is processed next time, if no resolution record is retrieved from the list of caches in memory, we use sgx_unseal to load 1M records from the disk into the enclave at a time. We will initiate a domain name resolution request to NSs only after retrieving the cache in memory and disk. Thus, the interaction with NSs is minimized. With the release of next-generation SGX hardware and its support for dynamic memory [43], the impact of memory on the DoTT will be weakened.  
Intel SGX SDK provides a standard library for multithreading. Multithreading is an effective way to achieve concurrency. DoTT is an I/O-intensive application as a networking service. However, it has to utilize the OCALL/ECALL mechanism to handle external network requests. The dual I/O workload may degrade response performance. When the OCALL operation of an SGX function is completed in the background, the DoTT can perform other processing operations. When a response process enters the enclave, DoTT will create a poll object and put it into the waiting queue. The poll uses a red and black tree as the index structure of ECALL and OCALL queues. The poll queue and the scheduler will check whether a thread needs to be weakened due to the callback finish of the system call or enclave response. If there are no ECALL/OCALL executes, the scheduler will be recycled to the thread pool.
## 3 Results and Discussion
### Method Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feed**} & \multirow{2}{*}{\begin{tabular}{c} **Added** \\ **Concentration** \\ **(\#g/g)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Recovery Rate** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Intra Batch** \\ **Coefficient of** \\ **Variation (\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Inter Batch** \\ **Coefficient of** \\ **Variation (\%)** \\ \end{tabular} } \\ \cline{2-4}  & 0.05 & 90.01 & 3.82 & 4.09 \\ \cline{2-4}  & 0.5 & 90.42 & 4.75 & 6.74 \\ \cline{2-4}  & 5.0 & 95.55 & 5.78 & 6.92 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Concentrated \\ chicken feed \\ \end{tabular} } & 0.1 & 90.18 & 9.07 & 10.38 \\ \cline{2-4}  & 1.0 & 83.64 & 5.57 & 7.72 \\ \cline{2-4}  & 10.0 & 91.16 & 9.82 & 10.52 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Chicken \\ premix feed \\ \end{tabular} } & 0.5 & 89.35 & 5.46 & 7.28 \\ \cline{2-4}  & 5.0 & 92.73 & 7.78 & 8.95 \\ \cline{2-4}  & 50.0 & 95.00 & 9.12 & 10.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Recovery rate and coefficient of variation in doramectin added to feed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feed**} & \multicolumn{2}{c}{**Added**} & \multirow{2}{*}{**Recovery Rate**} & \multicolumn{2}{c}{**Intra Batch**} & \multicolumn{1}{c}{**Inter Batch**} \\  & **Concentration** & **(\%)** & **(\%)** & **Coefficient of** & **Coefficient of** \\  & & & & **Variation (\%)** & **Variation (\%)** \\ \hline \multirow{2}{*}{Pig compound} & 0.05 & 83.96 & 8.33 & 9.90 \\ \cline{2-5}  & 0.5 & 81.92 & 6.78 & 7.92 \\ \cline{2-5}  & 5.0 & 83.27 & 5.29 & 6.67 \\ \hline \multirow{2}{*}{Concentrated pig feed} & 0.1 & 80.18 & 8.16 & 9.76 \\ \cline{2-5}  & 1.0 & 82.79 & 6.54 & 7.88 \\ \cline{2-5}  & 10.0 & 95.55 & 6.87 & 9.04 \\ \hline \multirow{2}{*}{Pig premix feed} & 0.5 & 99.65 & 9.95 & 10.56 \\ \cline{2-5}  & 5.0 & 97.38 & 8.61 & 9.90 \\ \cline{2-5}  & 50.0 & 97.01 & 6.72 & 8.75 \\ \hline \multirow{2}{*}{Vitamin supplements} & 0.05 & 90.22 & 9.14 & 10.37 \\ \cline{2-5}  & 0.5 & 89.67 & 7.60 & 8.65 \\ \cline{2-5}  & 5.0 & 85.45 & 6.85 & 7.74 \\ \hline \multirow{2}{*}{Cattle concentrate} & 0.1 & 83.76 & 6.69 & 7.80 \\ \cline{2-5}  & 1.0 & 82.39 & 10.75 & 11.13 \\ \cline{2-5}  & 10.0 & 82.72 & 9.17 & 10.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._
###### Abstract  
When marine animals and underwater acoustic sensor networks (UASNs) share spectrum resources, problems such as serious harm caused to marine animals by underwater acoustic systems and scarcity of underwater spectrum resources are encountered. To address these issues, a mammal-friendly underwater acoustic sensor network channel power allocation algorithm is proposed. Firstly, marine animals are treated as authorized users and sensor nodes as unauthorized users. Considering the interference level of sensor nodes on authorized users, this approach improves network service quality and achieves a mammal-friendly underwater communication mechanism. Secondly, to maximize the utility of unauthorized users, the algorithm incorporates a network interference level and node remaining energy into a game-theoretical framework. Using channel allocation and power control, a game model is constructed with a unique Nash equilibrium point. Finally, through simulation, it can be found that the proposed algorithm can obtain a stable optimal power value, and with the increase of network load, the system capacity of the proposed algorithm is significantly improved than that of the traditional cognitive radio technology and the common spectrum allocation algorithm, and the transmitted power of nodes can be controlled according to the size of the residual energy, so as to comprehensively improve the overall performance of the network.  
underwater acoustic networks; channel allocation; power control; mammal-friendly +
Footnote †: journal: Applied Sciences  
literature [5; 6] applied the cognitive underwater acoustic network technology, but did not consider the impact of underwater acoustic communication networks on the survival of marine mammals.  
To tackle the aforementioned challenges, this paper proposes a game-theoretical and mammal-friendly underwater network channel power allocation algorithm. It treats marine mammals as primary users and underwater sensor nodes as secondary users. The algorithm aims to maximize the capacity and performance of secondary users by establishing a utility function. By incorporating network interference levels and node remaining energy into the game-theoretical framework, the algorithm allocates channels and adjusts the transmission power of underwater communication nodes. This design aims to achieve a mammal-friendly and system-performance-optimized underwater acoustic communication network.  
The rest of this paper is organized as follows: In Section 2, we will mainly introduce the current research on channel allocation and power control. In Section 3, we will introduce the new mammal-friendly power control (MFPC) algorithm in detail. Then, we will compare the results through simulation experiments in Section 4 to verify the superiority of the new MFPC algorithm, and finally make a prospect and summary in Section 5.
###### Abstract  
In an offshore operational environment, complex loads such as the hook load, stand load and wind load play a crucial role in the structural strength and reliability of the offshore derrick. Previous studies have mainly focused on the effect of a specific load on the strength of the derrick by using commercial software. Therefore, the influencing mechanism of each complex load on the strength and reliability of the offshore derrick is urgently necessary to explore. Not only is the strength numerical model of the JJ315/45-K typical derrick established and conducted via APDL (ANSYS Parametric Design Language), but also the stress-strength reliability simulation is explored and plotted using Python code and APDL. Furthermore, the influence and contribution of each load to the strength and reliability is determined. With the increase in hook load, the positions of maximum reliability risk appear in section II-1. With the increase in stand load or back wind load, the positions of maximum reliability risk appear in the bottom section. In addition, with the decrease in the columns' height, the stress proportion of the hook load decreases by 73.5%, the stress proportion of the stand load increases by 22.6%, and the stress proportion of the back wind load changes slightly. We then wind speed is less than 20 m/s, the hook load mainly affects the minimum reliability index of the derrick. Moreover, when the wind speed is more than 20 m/s, the back wind load mainly affects the minimum reliability index of the derrick. This study provides a thorough explanation of the strength distribution law and the reliability of derricks under complex loads.  
complex load; offshore derrick; reliability model; Python; reliability analysis Article
# Mdpj _sciences_
### Related Works
However, during the computation of the master session key \(K_{AUSF}\), the use of the long-term key \(K\) and the secret key \(sk_{HN}\) introduces vulnerabilities concerning PFS in the event of key leakage. Additionally, the reliance on timestamps imposes a time synchronization burden, making it unsuitable for mobile communication environments such as roaming. Lastly, there may be compatibility issues arising from differences with the existing standards. **The 5G-AKA-FS**[16] improved the key exchange process by generating ephemeral ECIES keys during communication, aiming to achieve PFS while maintaining compatibility with existing standards. Specially, it reuses the UE's ECIES key pair generated to encrypt the SUPI so that the overall communication and computation overhead are reduced. Consequently, the proposed protocol not only provides strong security compared to the previously mentioned protocols but also excels in terms of compatibility and efficiency. However, it does not address the threat posed by quantum computing due to the lack of PQC application. **The WirelessPQCSim**[17] addresses potential PQC real-world implementation issues by applying a hybrid PQC during the SUCI calculation process. Nonetheless, it faces compatibility challenges and does not achieve PFS, as it only focuses on the SUCI computation phase rather than the entire 5G authentication protocol. **The EAP-AKA'-FS** was introduced to address the lack of PFS in the original EAP-AKA'. Although this protocol provides some improvements, it still does not account for PQC, leaving it vulnerable to quantum threats. **The EAP-AKA'-PQC** is currently under standardization as a response to these quantum threats. This protocol adopts the X-wing algorithm [18], which is considered to be one of the most promising candidates for hybrid PQC. By doing so, it aims to mitigate potential real-world implementation challenges while addressing the quantum threat. Table 1 explains a summarized comparison of the protocols discussed: