esa-sceva/satcom-chunk-collection huggingface dataset:
# Proxy-Based Adaptive Transmission of MP-QUIC in Internet-of-Things Environment
## 3 Proposed Proxy-Based Adaptive MP-QUIC Transmission
### Data Transmission  
When a client sends data packets, the proxy performs the data transmission operations, as described in Step 10-Step 13 of Figure 2. The data transmission between the proxy and the server will be performed adaptively to the current network conditions, based on the information obtained by the path management and connection management.  
In Step 10 of Figure 2, a client sends an HTTP/3 packet to the proxy by using QUIC. The HTTP/3 header will include the Hash ID information that was assigned by the proxy (Step 6 of Figure 2). By referring to the connection management table (Table 2), the proxy can determine the MP-QUIC path that will be used for data forwarding to the server, which depends on the priority type.  
Once the MP-QUIC path is determined, the proxy (connection manager) increases the Hits values in the connection management table, as described in Step 11.1-Step 11.3 of Figure 2. Then, the data packets will be forwarded to the server by using the determined MP-QUIC path (Step 12 of Figure 2). In turn, the server responds with the data packets to the client by way of the proxy.  
The remaining data transmissions between the client and the server via the proxy will be performed in a similar way, as described until now. During data communication, the tables for path management and connection management will continue to be updated and maintained for further subsequent data transmission of the new clients.
## 3 Energy and Information Beamforming
### Statistical Max-SINR Scheme
#### 3.1.2 Information Beamforming Design  
By substituting (20) into (14), the information beamforming problem can be expressed as follows:  
\[\begin{split}&\max_{\mathbf{w}_{DJ}}\frac{\mathbf{w}_{DJ}^{H} \mathbb{E}\left[P_{l}\mathbf{h}_{l}\mathbf{h}_{l}^{H}\right]\mathbf{w}_{DJ}}{ \mathbf{w}_{DJ}^{H}\left(\sum\limits_{k=1,k\
eq l}^{L}\mathbb{E}\left[P_{k} \mathbf{h}_{k}\mathbf{h}_{k}^{H}\right]+\sigma^{2}\right)\mathbf{w}_{DJ}},\\ &\text{s.t.}\left\|\mathbf{w}_{DJ}\right\|=1.\end{split} \tag{21}\]  
Again, (21) is in the form of generalized Rayleigh quotient problem, which can be solved as:  
\[\mathbf{w}_{DJ}=\mathbf{u}_{\text{max}}\left\{\left(\sum\limits_{k=1,k\
eq l}^ {L}\mathbb{E}\left[P_{k}\mathbf{h}_{k}\mathbf{h}_{k}^{H}\right]+\sigma^{2} \mathbf{I}_{N_{x}N_{y}}\right)^{-1}\mathbb{E}\left[P_{l}\mathbf{h}_{l}\mathbf{ h}_{l}^{H}\right]\right\}. \tag{22}\]  
Since the transmit power \(P_{l}\) is correlated with the channel \(\mathbf{h}_{l}\), there is no closed-form expressions for the expectations in (21). Therefore, the information beamforming based on (22) requires numerical evaluation with high computational complexity.  
To overcome this challenge, below, we propose a closed-form solution for \(\mathbf{w}_{DJ}\) based on the asymptotic property of the sensor's transmit power \(P_{l}\).  
**Lemma 1**: _When the number of AP antennas tends to infinity, i.e., \(\left\{N_{x},N_{y}\right\}\rightarrow\infty\), the transmit power of sensor \(l\) converges to:_  
\[P_{l}\approx\frac{\eta\tau}{1-\tau}p_{l}\frac{\beta_{l}K_{l}}{K_{l}+1}N_{x}N_{ y}-\frac{E_{l}^{p}}{\left(1-\tau\right)T}. \tag{23}\]  
See Appendix A.  
Inserting (23) into (22), we get a closed-form solution for \(\mathbf{w}_{DJ}\) as follows:  
\[\mathbf{w}_{DJ}=\mathbf{u}_{\text{max}}\left\{\left(\sum\limits_{k=1,k\
eq l} ^{L}\left(\frac{\eta\tau}{1-\tau}p_{k}\frac{\beta_{k}K_{k}}{K_{k}+1}N_{x}N_{y} \ -\frac{E_{k}^{p}}{\left(1-\tau\right)T}\right)\mathbf{C}_{k}+\sigma^{2} \mathbf{I}_{N_{x}N_{y}}\right)^{-1}\mathbf{C}_{l}\right\}. \tag{24}\]  
Even if closed-form expressions are available, the computation of beamformers in (20) and (24) has complexity \(\mathcal{O}\left(N_{x}^{3}N_{y}^{3}\right)\), which becomes challenging when the number of AP antennas grows large. Moreover, to implement the beamforming scheme, the required number of RF chains at AP is equal to \(N_{x}N_{y}\). Since AP is hardware complexity limited, it is very important to reduce the number of required RF chains at AP for energy and information beamforming. This will be considered in the next subsection.
## 4 Access Control Policy Conflict Detection and Resolution
### Compatible Policy of TSABAC  
Due to the complexity of the multilayer rail transit system, different access control models may be used in different security domains. To get a universal expression of different access policy models, the TSABAC model can be expanded into a compatible TSABAC form. We specify the different policy characteristics as the condition attributes, that is _CA_ = {_modifier_, _ea_, _Ts_, _State_}. As such, the compatible policy can be expressed as:  
\[pol=\text{$<$}\text{$sa$, $oa$, $ea$, $T$, $S$, $op$, modifier$, $T_{s}$, $State$, $eff$}> \tag{11}\]  
where:  
* _modifier_ exhibits the characteristics of the DAC model. In this case, the permissions possessed by the subject can be divided into _private_ and _public_ permissions. Public permissions can be passed on, while private permissions cannot.
* _sa.rank_!= \(\Delta\) and _oa.rank_!= \(\Delta\) represents an MAC model. In this case, the security levels of the subject and object are pre-assigned by administrators, and they are used as identifiers to check whether the information flow in the access control policy is one-way.
* _sa.role_!= \(\Delta\) represents an RBAC model. In this case, permissions are transferred through roles. The policy conflict detection in RBAC is mainly based on the flow direction of role permissions.
* _Ts_ exhibits the characteristics of the TBAC model. _Ts_ = {_t1_, _t2_,..., _tn_} represents a set of tasks, and _t1_ represents a subset of tasks. In this case, the relationships between tasks can be synchronous, mutually exclusive, sequential, or dependent on delegation of authority.
* _State_ exhibits the characteristics of the UCON model, and it represents a set of states, i.e., _State_ = {_state1_, _state2_,..., _staten_}. In this case, conflicts are determined by checking the obligations of the access subject.
* _ea_ shows the characteristics of the ABAC model, where _ea_ is the attribute set of the environment.
* Other access control models are described using ABAC attributes, which are unified for description.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 5 Simulations, Results, and Discussions
### Factors for HSN Bandwidth
#### 5.5.2 Results and Discussion
Regardless of the service frequency, the sum of the time slots allocated to the FC traffic and the aggregate of the time slots allotted to all Li-Fi traffic will never surpass the duration of the small cycle period. \[W(fc,i)=\min\Bigl{(}(Allocated\ Slot-Protocol\ cost)*Rate_{fc},Traffic.Buffer \Bigr{)} \tag{38}\]  
\[ \begin{split} W(lifi,i)\\ =\sum_{j=1}^{lift}\min\Bigl{(}\bigl{(}Allocated\ Slot_{j}-Protocol \ cost\bigr{)}*Rate_{lifi},Traffic_{j}.Buffer\Bigr{)}\end{split}  \tag{39}\]  
Above all, prior to reaching saturation, a larger number of transmissions led to a greater allocation of bandwidth within the constrained wireless links. However, once saturation was reached, additional transmissions could not acquire additional bandwidth  
Figure 14: Bandwidth performance with wireless links. within the limited wireless links. Overall, the bandwidth expanded as the number of wireless links increased. This is because the network's topology, the number of links, and the link rate determine the maximum throughput of the network. Meanwhile, the throughput and scheduling method of the transmissions determines whether the current effective bandwidth can reach or approach the theoretical limit.
## 2 Materials and Methods
### Study Design  
Based on the monthly datasets (NPP, ET and WUE) from 2001 to 2010, we defined mean monthly baseline values for NPP, ET and WUE under normal conditions (i.e., monthly SPI values between -0.99 and 0.99). For each month during 2001-2008, all pixels under normal conditions defined by monthly SPI value between -0.99 and 0.99 were masked, which were used to extract monthly baseline values for NPP, ET and WUE (Equation (1)). Each monthly baseline layer in all years (2001-2008) was averaged to develop a mean monthly baseline layer (Equations (2)-(4)). On the basis of a drought class map (Figure 2b), we calculated the monthly baseline condition for NPP, ET and WUE by averaging over drought classes.  
\[\text{Flag}_{j}\ =\ \left\{\begin{array}{l}0;\text{if SPI}_{j}>0.99\text{ and SPI}_{j}<-0.99\ (j\ =\ 1,\ 2,3\ldots,n\times 12)\\ 1;\text{if SPI}_{j}\leq 0.99\text{ and SPI}_{j}\geq-0.99\ (j\ =\ 1,\ 2,3\ldots,\ n\times 12) \end{array}\right. \tag{1}\]  
\[\text{NPP\_baseline}_{i}\ =\ \frac{\sum_{k\ =\ 1}^{n}\left(\text{NPP}_{i+(k-1)\times 1 2}\times\text{Flag}_{i+(k-1)\times 12}\right)}{\sum_{k\ =\ 1}^{n}(k\times\text{Flag}_{i+(k-1)\times 12})}\ (i\ =\ 1,2,3 \ldots 12) \tag{2}\]  
\[\text{ET\_baseline}_{i}\ =\ \frac{\sum_{k\ =\ 1}^{n}\left(\text{ET}_{i+(k-1) \times 12}\times\text{Flag}_{i+(k-1)\times 12}\right)}{\sum_{k\ =\ 1}^{n}(k\times\text{Flag}_{i+(k-1)\times 12})}\ (i\ =\ 1,2,3 \ldots 12) \tag{3}\]  
\[\text{WUE\_baseline}_{i}\ =\ \frac{\sum_{k\ =\ 1}^{n}\left(\text{WUE}_{i+(k-1) \times 12}\times\text{Flag}_{i+(k-1)\times 12}\right)}{\sum_{k\ =\ 1}^{n}(k\times\text{Flag}_{i+(k-1)\times 12})}\ (i\ =\ 1,2,3 \ldots 12) \tag{4}\]  
where, \(n\) equal to 8, which denotes the non-drought years from 2001 to 2008; \(k\) is the year number from 1 to \(n\); \(i\) is the month index. So \(\text{Flag}_{i}\) represented baseline condition (non-drought), and the NPP\(\_\)baseline\({}_{i}\), ET\(\_\)baseline\({}_{i}\) and WUE\(\_\)baseline\({}_{i}\) denoted that average non-drought NPP, ET and WUE at time \(i\), respectively. So, we defined the three variables: NPP\(\_\)baseline, ET\(\_\)baseline and WUE\(\_\)baseline from January to December are the baseline seasonal variations of NPP, ET and WUE.  
The monthly drought condition for NPP, ET and WUE was calculated from monthly ecosystem measures (NPP, ET and WUE) in the years 2009 and 2010. All monthly drought layers were summed to develop two annual drought layers (2009 and 2010) that can be compared with the normal conditions. Deviations from baseline conditions are denoted by \(\Delta\) for all variables. We calculated the ecosystem measures under normal and drought conditions by averaging over drought class regions (i.e., three classes: moderate drought, severe drought and extreme drought). To remove the effects of possible changes in vegetation types, we classified the time series of mean monthly NPP, ET and WUE under normal conditions and drought conditions for each vegetation types, which were used to explore the drought impacts on carbon and water fluxes of south-western China's forests.
# Review of Indoor Positioning: Radio Wave Technology
## 3 Radio Signals-Based Positioning
### Wi-Fi Technology  
Wi-Fi, which is a wireless local area network (WLAN), is a well known technology in broadband communications, specifically for machine-to-machine schemes and human communication [93]. The Wireless Ethernet IEEE 802.11 (Wi-Fi) devices generally transmit over 2.4 GHz, nevertheless, now 5 GHz is extensively being utilized for transmission due to less interference, less noise, higher constant connection, and enhanced speed [94]. The Wi-Fi network is available through mobile devices such as laptops, tablets, mobile phones and others in consequence of an active saleable off-the-self simple infrastructure for an IPS [95; 96]. Wi-Fi signal is used to focus the problem of indoor positioning and tracking, due to the ubiquitous placement of Wi-Fi access points, low cost over other indoor wireless technologies, low energy consumption, and without additional hardware requirements [35; 97]. Several algorithms and ranging parameters have been presented to increase Wi-Fi-based IPS; however, most of the algorithms and measurement solutions need large computing properties and specific hardware [98]. Wi-Fi localization algorithms are introduced, including an AOA-based algorithm (triangulation) [99], trilateration algorithm [1, 100], RSSI-based fingerprinting algorithm [101; 102] and CSI-based fingerprinting algorithm [98; 103].  
Among the algorithms, the fingerprinting algorithm and the trilateration algorithm are often employed in Wi-Fi-based indoor localization. However, fingerprinting localization algorithms give the best performance and attract the researcher's attention due to easy implementation, low complexity, no need for the LoS measurements of APs and specialized hardware [104]. The average localization errors are described as 2-3 m in Wi-Fi-based positioning algorithms [92]. Wireless signals of Wi-Fi access points can protect huge areas, however, they need multipart hardware and software collaboration with each other [38]. In addition, Wi-Fi-based positioning implementation can be extremely affected by environmental effects such as the geography of the barrier, people's mobility or crowdedness, and weather [92]. The multipath failing of Wi-Fi signals affects the time-varying RSSI of signals that influence the precision of the Wi-Fi location. Furthermore, Wi-Fi scanning time, around 3-4 s in common smartphones, gives the low quality of its services in the context of a refreshment time [92].
# Transfer Learning Based on Clustering Difference for Dynamic Multi-Objective Optimization
## 4 Experiments
### Running Speed
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Functions** & **Settings** & **DNSGA-II-A** & **DNSGA-II-B** & **TCD-DMOEA** & **FPS** & **T-DMOEA** & **KF-DMOEA** & **Winner** \\ \hline  & S1 & 0.12549 & 0.18466 & **0.0825** & 0.79948 & 0.16762 & 0.18591 & TCD-DMOEA \\ DF1 & S2 & 0.14103 & 0.14997 & **0.02630** & 1.00778 & 0.22634 & 0.20192 & TCD-DMOEA \\  & S3 & 0.06910 & 0.07078 & **0.01770** & 0.34875 & 0.17491 & 0.15986 & TCD-DMOEA \\ \hline  & S1 & 0.07984 & 0.11012 & **0.00677** & 0.56862 & 0.1088 & 0.12259 & TCD-DMOEA \\ DF2 & S2 & 0.09846 & 0.09856 & **0.00821** & 0.55064 & 0.1808 & 0.14677 & TCD-DMOEA \\  & S3 & 0.04331 & 0.05206 & **0.00519** & 0.33104 & 0.1315 & 0.10625 & TCD-DMOEA \\ \hline  & S1 & 0.36036 & 0.31444 & 0.06176 & 0.46092 & 0.315 & 0.38022 & TCD-DMOEA \\ DF3 & S2 & 0.31344 & 0.30096 & 0.22788 & 0.56408 & 0.4117 & 0.36451 & TCD-DMOEA \\  & S3 & 0.34277 & 0.35370 & **0.05804** & 0.28151 & 0.3406 & 0.37636 & TCD-DMOEA \\ \hline  & S1 & 1.33552 & 1.29683 & 0.92736 & 4.09048 & 1.5689 & 1.20196 & TCD-DMOEA \\ DF4 & S2 & 0.85006 & **0.85114** & 1.45397 & 1.78053 & 1.8344 & 1.86927 & TCD-DMOEA \\  & S3 & 1.29904 & 1.28637 & **1.03469** & 2.13623 & 1.5869 & 1.98173 & TCD-DMOEA \\ \hline  & S1 & 0.09852 & 0.18511 & **0.02306** & 0.35646 & 2.3146 & 1.3592 & TCD-DMOEA \\ DF5 & S2 & 1.67522 & 1.68125 & **0.023217** & 2.02082 & 2.6205 & 3.20962 & TCD-DMOEA \\  & S3 & 0.05721 & 0.07235 & **0.02274** & 0.25696 & 2.4713 & 3.30728 & TCD-DMOEA \\ \hline  & S1 & 5.50747 & 7.96408 & **0.46630** & 11.8778 & 5.5982 & 6.2692 & TCD-DMOEA \\ DF6 & S2 & 7.32493 & 8.29587 & 0.16436 & 12.4986 & 7.6292 & 8.57652 & TCD-DMOEA \\  & S3 & 2.81471 & 3.45856 & **0.21433** & 5.66312 & 6.7379 & 6.55881 & TCD-DMOEA \\ \hline  & S1 & 4.94594 & 8.21914 & **0.51930** & 10.0823 & 8.8403 & 7.4592 & TCD-DMOEA \\ DF7 & S2 & 7.47332 & 7.84210 & 0.33669 & 11.3676 & 4.2387 & 8.67890 & TCD-DMOEA \\  & S3 & 2.19079 & 3.20643 & **1.13082** & 5.96671 & 4.0323 & 8.9454 & TCD-DMOEA \\ \hline  & S1 & 0.83967 & 0.88058 & **0.06311** & 0.87168 & 0.78817 & 1.10952 & TCD-DMOEA \\ DF8 & S2 & 0.86939 & 0.85910 & **0.25914** & 0.85785 & 0.7993 & 1.41749 & TCD-DMOEA \\  & S3 & 0.88772 & 0.89634 & **0.05962** & 0.86689 & 0.8026 & 1.64865 & TCD-DMOEA \\ \hline  & S1 & **1.44013** & 1.449086 & 2.03433 & 1.94736 & 2.5958 & 2.81085 & DNSGA-II-A \\ DP9 & S2 & **1.26994** & 1.75797 & 1.67976 & 2.24801 & 2.7079 & 3.17112 & DNSGA-II-A \\  & S3 & **1.99835** & 1.60138 & 2.33938 & 1.66393 & 2.3714 & 3.28642 & DNSGA-II-A \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The MICD values of six comparative algorithms with different dynamic test settings.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 2 Related Works
Unlike previous studies that focus mainly on algorithmic benchmarks or protocol integration at the transport layer, our proposed protocols demonstrate how existing applications can be practically migrated to achieve quantum resistance at the application layer.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 6 Numerical Results
### UE to UE Communications Scenario  
We start with the simplest considered scenario of UE to UE communications. Recall that in this scenario, all the machines crossing the LoS path may potentially block it as the height of both UEs is equal and less than the constant height of machines. Figure 6 illustrates the impact of the machine dimensions (i.e., width and length), \(1/\lambda_{x}=1/\lambda_{y}\) and the transparency probability \(\kappa\) on the blockage probability \(p_{B}(l_{x},l_{x})\), for different values of the UE coordinates. Note that \(\kappa=0.9\) in Figure 5(a) while \(1/\lambda_{x}=1/\lambda_{y}=3\) m in Figure 5(b) while the site occupancy probability is set to \(\
u=1\) in both illustrations.  
Figure 5: Accuracy assessment of the proposed model.  
Figure 6: _Cont_.  
Analyzing the presented results, we observe the expected trends. First of all, the blockage probability decreases with the size of the machine as the LoS path can intersect fewer number of machines. Secondly, the transparency probability of a machine drastically affects the LoS blockage for given UE coordinates and machine dimensions. However, for medium-to-large separation distances between UEs, \(\sim\)14 m and \(\sim\)42 m corresponding to UE location \((10,10)\) and \((30,30)\) respectively, the value of the blockage probability is greater than 0.8 even for \(\kappa=0.8\). This implies that direct UE-UE communications can be feasible at rather small inter-UE distances or in less dense machine deployments.
# Field Test Study on Vertical Bearing Characteristics of Coal Rock
## 3 Results
### Bi-Directional Load Test
#### 3.2.2. Mobilized Tip Resistance  
The load cell of the bi-directional load test is placed at the tip of the pile and is in direct contact with the coal rock. It is equivalent to the deep plate load test. Therefore, according to the test curve of the lower pile, the bearing capacity of coal rock can be obtained:  
\[q_{i}=\frac{Q_{\text{di}}}{\frac{\pi}{4}d_{\text{l}}^{2}} \tag{4}\]  
where \(q_{i}\) is the average tip resistance per unit area (kPa); \(Q_{\text{di}}\) is the downward loading value (kN); and \(d_{\text{l}}\) is the diameter of the bottom plate of the load cell (m).  
Since both of the pile tip elevations are about the same, the unit tip resistance-displacement curves of TP1 and TP2 have good consistency, as shown in Figure 13. It can be seen from Figure 13 that the curves are all steep-drop-types; the test points before failure are close to a straight line. The displacements corresponding to the ultimate bearing capacity are 18.79 mm and 18.82 mm for TP1 and TP2, and they are approximately 1% of the diameter of the bottom plate of the load cell, which is 18 mm.  
From Figure 13, it can be seen that the unit tip resistance-displacement curve of coal rock is close to the ideal elastic-plastic curve:  
\[q=\left\{\begin{array}{c}ks\ (\leq q_{\text{u}})\\ q_{\text{u}}\end{array}\right. \tag{5}\]  
where \(k\) is the coefficient (kPa/mm); \(s\) is the pile tip displacement (mm); \(q_{\text{u}}\) is the limit unit tip resistance (kPa).  
In Figure 13, linear fitting is performed on the test points before failure, and the obtained curve has a good correlation with the test points. The slopes \(k\) of the load transfer functions of TP1 and TP2 are 233.7 kPa/mm and 274.3 kPa/mm, respectively; the limit unit tip resistance \(q_{\text{u}}\) of TP1 and TP2 is 3458 kPa for both.  
When designing pile foundation, the safety factor of the bearing capacity of the pile tip usually takes a value of two, and the design value of the unit tip resistance is 3458/2 = 1729 kPa.  
Figure 13: Relationship between unit tip resistance and displacement: (**a**) TP1; (**b**) TP2.
## 1 Introduction
To address the attitude control problem of quadrotor aircraft, the attitude control strategy of a quadrotor aircraft based on ADRC is designed in this paper, which takes into account the internal and external disturbances of the system. Secondly, in order to solve the problem of too many parameters of the ADRC controller and the difficulty of manual adjustment to achieve the optimum, a quadrotor ADRC parameter optimization algorithm based on FOFPSO is proposed in this paper. The algorithm adds the characteristics of fuzzy logic and fractional order to the particle swarm optimization algorithm, which improves the global and local exploration ability of the particle swarm optimization algorithm, avoids particles falling into the local optimal value, and improves the accuracy of the solution. It has the advantages of fast convergence speed and high solution accuracy, as well as making it difficult for particles to fall into the local optimal value. Finally, in order to verify the effectiveness of the algorithm, a simulation experiment is designed, and the disturbance of turbulent wind field is added to verify the disturbance rejection performance of the controller. Then, in order to verify the feasibility of the controller in the quadrotor aircraft experimental platform, this paper adopts the model-based design method, deploys the controller model on the Pixhawk4 hardware platform and verifies the attitude tracking and disturbance rejection performance of the controller.