esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel FFT_YOLOX Model for Underwater Precious Marine Product Detection
## 3 Experiment and Discussion
### Results and Comparison  
We evaluated the proposed FFT_YOLOX for the object detection task on the merged URPC dataset. For this task, the original YOLOX-S model was adopted as the base detector. For fair comparison, we trained all models across 300 epochs by using SGD with weight decay 0.0005, momentum 0.9, and mini-batch of 8 on one 3080 Ti GPU.  
The detection results of FFT_YOLOX model are illustrated in Figure 3. From Figure 3, the effectiveness of the model is evaluated qualitatively. The most of precious marine products can be detected precisely, including small and indistinct objects which are hard to recognize even by human beings. Moreover, the qualitative comparison results of the original YOLOX-S and the proposed FFT_YOLOX model are shown in Figure 4. The proposed FFT_YOLOX model can detect more scallop and echinus from the difficult underwater environment, which the original YOLOX-S model cannot recognize. It owes to the capability of the global capturing feature of the FFT_Filter.  
For quantitative evaluation, the 0.5, 0.75, and 0.5\(-\)0.95 AP metrics are adopted for evaluation, and the AP of small, medium and large objects are also taken for performance comparisons. From Table 1, it can be seen that the proposed FFT_YOLOX model outperforms the original YOLOX-S across all AP metrics (The best results are illustrated with bold format). Moreover, each class AP (0.5\(-\)0.95) comparisons are shown in Table 2 (The best results are illustrated with bold format). The APs of holdurian, echinus, and starfish of the proposed model are higher than original YOLOX-S. It demonstrates that, due to the combination with global filters, the proposed FFT_Filter has stronger ability of feature ex  
Figure 3: The detection results of FFT_YOLOX model.  
traction with fewer model parameters and lower FLOPs than the original YOLOX-S model.
# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
## 6 Simulation
### Prediction Effect of Parameter Value Prediction Network  
The modification effect of the parameter modification network is shown in Figure 11(a). After the filtering of parameters, the control parameters still have great fluctuations and may lead to large errors in parameter fitting and prediction. As shown in Figure 11(b), after the parameter modification network, the modified control parameters are relatively close to the truth value. It can be seen from Figure 11(c) that, based on the modified control parameters, the prediction effect of the control parameter value is close to the truth value, and it can help to more accurately integrate trajectories by iteration.  
The modification effect of lateral and longitudinal filter trajectories is shown in Figure 13. As can be seen from Figure 13a,b, before modification, both the lateral and longitudinal tracking trajectories of the target have large tracking errors. However, after parameter correction, the modified trajectory of the observation is closer to the truth trajectory than the tracking trajectory. Since the initial state and filtering control parameters are modified by the initial and last state modification network and parameter modification network, it makes the modified filtering trajectory generated by the integration more accurate, which is of great significance for the accurate analysis of influence factors. This method improves the precision of the intention inference and the accuracy of trajectory prediction.
## 3 Multi-Criteria Decision Making (MCDM) Method
### Simple Additive Weighting (SAW)  
We can obtain the weights of each criteria using the AHP. However, the AHP does not use the value from actual measurement in the decision making process. Since our protocol needs to calculate a single routing cost, we need an additional method to normalize the actual value. To achieve this, we use the SAW method [19] with AHP. The objective of the SAW method is to calculate the weighted sum of performance ratings on each alternative. The SAW method performs the following two steps.  
* The first step is to evaluate the alternatives. Given the neighbor table as shown in Table 2, we must evaluate whether each criterion should be minimized or maximized to obtain a high rating. In this example, we assume that \(C_{1}\) and \(C_{2}\) should be minimized, and \(C_{3}\) should be maximized.  
* The second step is to construct a normalized decision matrix. To do this, both \(C_{1}\) and \(C_{2}\) are normalized as [19], \[e_{ij}=\frac{Min(C_{ij})}{C_{ij}}\] (2) where \(e_{ij}\) is the normalized value corresponding to row \(i\) and column \(j\). The \(Min(C_{ij})\) indicates the minimum value of \(C_{ij}\). On the other hand, \(C_{3}\) is normalized as [19], \[e_{ij}=\frac{C_{ij}}{Max(C_{ij})}\] (3)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**AHP Scale (\(C_{ij}\))** & **Rating** & **Reciprocal** \\ \hline Equal importance & 1 & 1/9 \\ Equally to moderately & 2 & 1/8 \\ Moderate importance & 3 & 1/7 \\ Moderately to strong & 4 & 1/6 \\ Strong importance & 5 & 1/5 \\ Strongly to very strong & 6 & 1/4 \\ Very strong importance & 7 & 1/3 \\ Very strong to extremely & 8 & 1/2 \\ Extreme importance & 9 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scales for pairwise comparison matrix.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Alternative** & \(C_{1}\) & \(C_{2}\) & \(C_{3}\) \\ \hline N1 & 13 & 234 & 0.8 \\ N2 & 7 & 203 & 0.4 \\ N3 & 9 & 270 & 0.5 \\ N4 & 16 & 300 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of a neighbor table.  
where the \(Max(C_{ij})\) denotes the maximum value of \(C_{ij}\). The results of normalization are shown in Table 3.
# A Review on Internet of Things for Defense and Public Safety
## 4 Operational Requirements
### Deployment Features  
One of the biggest constraints in a battlefield environment is power consumption. IoT devices are likely to be powered by batteries or solar power, and charged on-the-move from solar panels, trucks, or even by motion while walking. In either case, they should last for extended periods of time (at least for the duration of the mission). Therefore, devices and sensors need to be power-efficient, and end-users have to use them appropriately. Likewise, it is not easy to recharge IoT devices periodically or swap out batteries in deployed devices. Even in the case of body-worn devices, it is impractical to expect soldiers to carry additional batteries on top of their current equipment.  
The exploitation of emerging embedded hardware within the military, probably through specialized software components designed to run on those innovative platforms, could lead to a significant increase in processing power and a decrease in energy consumption.  
Furthermore, design values (e.g., power cell size or transmission capabilities) and equipment should fulfill the requirements imposed and be compliant with the considerations from military standards (e.g., MIL-STD 810G, MIL-STD 461F, MIL-STD-1275). IoT devices should be ruggedized and prepared to operate under extreme environmental conditions. Nevertheless, a non-negligible share of  
Figure 5: Operational Capabilities assessed to cover mission-critical scenarios.  
devices is already designed for harsh industrial environments, and, thus, they would be relatively well suited for the adoption in defense environments.
## 3 Feature Extraction
### GPS Features  
The following features are extracted from the GPS receiver.  
* The median carrier to noise ratio value of one minute of GPS data. The carrier to noise ratio is an estimate of the received power most GPS/GNSS (Global Navigation Satellite System) receivers provide for each tracked satellite. A low carrier to noise ratio indicates that there are obstacles such as building walls in the satellite-receiver path. The carrier to noise values are much lower indoors than outdoors [14].
* The minimum elevation angle value from the satellites that are used for getting the GPS position fixes. The elevation angle is the angle between the local horizontal plane and the user-satellite vector. In urban canyons satellites with a small elevation angle are rarely tracked.
* The maximum speed value from one minute of GPS data. Preferably a Doppler-based speed estimate computed by the receiver, but can also be derived from two consecutive position fixes. High speed values indicate that the device is in a moving vehicle.
* The best horizontal accuracy value of the GPS position fixes, _i.e._, the smallest horizontal accuracy value that the GPS device outputs. Most receivers provide this kind of value, the best accuracy is obtained in rural areas with a clear sky view.
* Time to first fix (TTFF), _i.e._, the time passed from the time of power-up to the instant when the first location measurement is obtained.  
Figure 2: During the second trial, user were able to give input on whether the classification was correct or incorrect.
## 6 A Feasibility Analysis of the Optimization Method for the Finished State
### Setting the Optimization Parameters  
The determination of reasonable optimization parameters proceeds iteratively. Initially, approximate ranges for each constraint are established empirically. A trial calculation is then performed for a single constraint (e.g., displacement) to refine its acceptable range. This process is repeated for subsequent constraints (e.g., cable tension), holding the previously determined ranges constant. This iterative approach extends to all constraint types. Detailed calculations are presented in Section 7. Based on these trial results, the final optimization parameters are as follows: the cable force \(1800\) kN \(<T_{\rm i}\leq 0.4T_{\rm pdi}\) (\(T_{\rm pdi}\) is the damage force of cable \(i\)); the cable force distribution parameter \(|P_{\rm i}|\leq 300\) kN; the vertical displacement of the main girder nodes \(|\delta_{\rm gi}|\leq 150\) mm; the horizontal displacement of the pylon \(-30\) mm \(\leq\delta_{\rm pi}\leq 0\) mm; the normal stresses in the top and bottom flanges of the main girder \(|\sigma_{\rm gi}|\leq 120\) MPa; and the normal stresses on the sides of the pylon \(\sigma_{\rm pi}\leq 0\).  
Through trials, the parameters needed during the optimization process can be set as follow: the initial slack variable vector \(\mathbf{w}^{(1)}=\big{(}1,\cdots,1\big{)}_{\rm m}^{\rm T}\); the initial Lagrange multiplier vectors \(\mathbf{y}^{(1)}=\big{(}1,\cdots,1\big{)}_{\rm m}^{\rm T}\) and \(\mathbf{v}^{(1)}=\big{(}1,\cdots,1\big{)}_{\rm T}^{\rm T}\); the initial iterative cable force vector \(\mathbf{T}^{(1)}=\big{(}1000,\cdots,1000\big{)}_{\rm T}^{\rm T}\); and \(p=0.95\), \(\delta=0.30\), \(\alpha=4.00\), \(\beta^{(1)}=2.00\), \(\theta=0.80\), and the convergence criterion \(\varepsilon=0.001\).
## 2 Related Work  
### Topology-Based vs. Geographic-Based Routing Protocols  
Multi-hop wireless networks are commonly classified into topology-based and geographic-based routing [20]. Topology-based routing protocols can be reactive (on-demand), proactive (table-driven), or hybrid. Reactive routing protocols (such as DSR and AODV) maintain routing paths that are currently in use. On the other hand, table-driven routing protocols (like OLSR--optimized link state routing protocol) uphold all the routing paths in the network topology. Hybrid routing protocols (e.g., ZRP) combine the strengths of both on-demand and table-driven routing protocols. Protocols such as on-demand and table-driven require maintenance of the entire network topology and routing table information of all nodes for packet forwarding [21]. When applied to vehicular networks, specific protocols are deemed unsuitable for VANETs due to the dynamic nature of vehicle communication and vehicles' high speed and mobility, which can result in elevated communication congestion. However, geographic routing protocols [21; 22] are regarded as highly adaptable to high speeds and mobility, making them suitable for VANETs. Greedy perimeter stateless routing (GPSR) [9] is a prominent geographically based routing pro tocol in VANETs. GPSR employs two routing strategies for packet forwarding: greedy forwarding and perimeter forwarding. The greedy forwarding strategy directs packets to neighboring nodes closest to the destination, gradually moving them toward the target. If, during forwarding, no neighbor is closer to the destination than the current node, it encounters a local maximum. In such cases, the perimeter forwarding strategy is utilized to navigate on a right-handed basis. GPSR demonstrates high adaptability and scalability by maintaining a one-hop-based table. However, while it exhibits excellent routing performance on highways, it often encounters greedy forwarding failures in environments due to various obstacles like buildings, trees, and other impediments. Consequently, perimeter forwarding is frequently employed, increasing hop count and elevating end-to-end delay.
# Optimal Proactive Caching for Multi-View Streaming Mobile Augmented Reality
## 3 System Model
### Mathematical Programming Formulation
#### 3.2.1. Wireless Channel Modeling and Achievable Data Rate  
According to Shannon formula, the achievable wireless data rate for the user in a 5G access point is \(B_{j}\log_{2}(1+\gamma_{rj})\), where \(B_{j}\) is the bandwidth allocated to the user's resource block and \(\gamma_{rj}\) is the Signal to Interference plus Noise Ratio (SINR) of the user \(r\) at the node \(j\). We denote \(P_{j}\) as the current resource block power, \(H_{rj}\) as the channel gain, \(N_{j}\) as the noise, \(a\) as the path loss exponent and \(d_{rj}\) as the Euclidean Distance between the user and the access router in the cell. Furthermore, we utilize a nominal Rayleigh fading channel to model the channel between a 5G access point and the users [28]. In this case, the channel gain \(H_{rj}\) can be written as follows [29],  
\[H_{rj}=\sqrt{\frac{1}{2}}(t+l) \tag{19}\]  
where \(l^{2}=-1\), \(t\) is a random number following the standard normal distribution. Then, the SINR \(\gamma_{rj}\) in a 5G wireless network can be written as follows [29; 30],  
\[\gamma_{rj}=\frac{P_{j}H_{rj}^{2}d_{rj}^{-a}}{N_{j}+\sum_{i\in\mathbf{M},i \
eq j}P_{i}H_{ri}^{2}d_{ri}^{-a}} \tag{20}\]  
Thus, the wireless transmission delay in a mobility event can be written as follows,  
\[\begin{split}&\sum_{r\in\mathbf{R}}\frac{F_{yr}+\sum_{s\in \mathbf{S}_{r}}\sum_{l\in\mathbf{L_{rs}}}h_{rl}^{s}O_{l}}{B_{f(r)}\log_{2}(1+ \gamma_{rj(r)})}+\\ &\sum_{r\in\mathbf{R}}\sum_{k\in\mathbf{K}}u_{f(r)k}\frac{F_{yr}+ \sum_{s\in\mathbf{S}_{r}}\sum_{l\in\mathbf{L_{rs}}}h_{rl}^{s}O_{l}}{B_{k}\log _{2}(1+\gamma_{rk})}\end{split} \tag{21}\]
# Phase Offset Tracking for Free Space Digital Coherent Optical Communication System
## 1 Introduction  
In an optical wireless communication system, the received optical signal is always weak as a result of the geometric loss, atmospheric absorption, and misalignment loss. In addition, the inhomogeneities in pressure and temperature changes in the atmosphere causes turbulence resulting in the variations in refractive index. Thus, the optical signal arriving at the receiver fluctuates [1; 2; 3; 4]. To improve the performance of the receiver, the coherent detection in the receiver can be employed. With the coherent detection method, the weak received signal is amplified after mixing with the local laser, by which the reliability of the receiver is enhanced [5; 6; 7; 8]. In order to implement the coherent receiving, the phase offset and the frequency between the signal and the local laser need to be measured and estimated.  
An obvious means to estimate the phase offset is to employ the optical phase lock loop (OPLL) [9; 10]. However, the OPLL raises the cost and cannot compensate for the channel impairment adaptively. With the recent advances of high-speed analog-to-digital converter and high-performance digital signal processor, the receiver using digital signal processing (DSP) algorithm can implement the coherent receiving, eliminating the need for OPLL [11; 12; 13; 14; 15]. The Viterbi-Viterbi phase estimation (VVPE) algorithm is the most commonly used method to measure the phase offset, and it has been demonstrated to be effective for coherent receiver within a certain range of SNR [16; 17]. Once the SNRis lower than this range, the performance of VVPE will be degraded severely by the noise and may not meet the system requirement. In this paper, a new algorithm combining VVPE with the Kalman filter is presented for satellite-to-ground downlink. With both constant parameter channel and atmosphere turbulence channel, this new method can reduce phase offset estimation error greatly with low SNR. When the SNR is under 10 dB, comparing the traditional VVPE, the error is lowered more than 60% by this new method. In addition, the performance of this new algorithm is less affected by the atmosphere turbulence than the traditional algorithm.
# Research Challenges for the Internet of Things: What Role Can OR Play?
## 3 How can OR Support the Future IoT?
### Application of General Systems Thinking to the IoT
#### 3.2.1 General Systems Theory (GST)/Complexity Theory  
General Systems Theory (GST) was founded by [PERSON], an organismic biologist, in the mid-1920s with the intention of identifying and describing systems concepts which could be applied across a range of disciplines, initially to the physical, biological and human sciences and later to the social sciences [73]. These concepts include parts/wholes/sub-systems, system/boundary/environment, structure/process and emergent properties [2]. Complexity Theory is a branch of GST applicable to Complex Adaptive Systems (CAS), which is a type of system that exhibits unpredictable behavior arising from non-linear spatio-temporal interaction among a large number of components and sub-systems [74]. It was developed in the 1970s in a range of disciplines including biology, chemistry, mathematics and economics. In the 1990s it was extended in the US by [PERSON] [75], [PERSON] [76], and others and is seen as an approach which may assist in tackling the seemingly intractable problems of the modern world including overpopulation and the depletion of the earth's resources, business globalization, the emergence of terrorism, and the extremely complex problem of global warming. In this paper we take a holistic view of the IoT as a CAS, which is why it needs to be tackled by multiple methods drawn from the mathematical techniques of OR and several Systems Thinking approaches, and many other disciplines including law, economics and sociology. We discuss in more detail how GST and Complexity Theory can be applied to the IoT in Section 4.4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Systems Thinking Approach** & **GST/** & **Self-Organizing** & **Cybernetics/** & **Soft** & **CST/** \\
**Complexity** & **Dependy** & **Systems** & **System** & **Systems** & **Multimethodology** \\ \hline Network Design & x & x & x & & x \\ Complex Adaptive System & x & x & x & & x \\ Self Organizing System & x & x & x & & x \\ Intelligence \& Context & x & x & x & x \\ Awareness & & & & x & x \\ Software Development & & & & x & x \\ Network & & x & x & x & x \\ Management/Operations & & & & x & x \\ Technology Transfer & & & & x & x \\ Politics/Cross Border Data Flows & & & & x & x \\ Work Restructuring & & & & x & x \\ Industry Investment in IoT & & & x & x & x \\ Ethical \& Legal & & & & x \\ Framework of IoT & & & & x & x \\ Security \& Privacy & & & & x & x \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cross referencing the main IoT multi-disciplinary Research Challenges to which the various Systems Thinking Approaches may make an important contribution, marked as x.
## 2 Background
### Consensus Algorithm for Decentralized Trust Management
#### 2.5.6 RAFT  
RAFT is commonly used due its fault-tolerant nature, simplicity, and efficiency in distributed systems. In RAFT, the network is divided into three types of nodes: leader, candidates, and followers. In this algorithm, every new node must be added to the leader, which is also responsible for maintaining the log in the network, and the algorithm clones it in the network. In the case of a transaction, the leader broadcasts the write request to the followers and verifies its response. On confirmation from all of the nodes, the new transaction is added. A candidate is a node who wants to become a leader, and the leader is chosen on the basis of the votes of the followers. The candidate having the maximum number of followers will become the leader and the previous leader becomes a follower because it lost its majority. Figure 4 explains the complete process of the algorithm.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 4 Search Algorithm for Late-Binding Node Selection
The DCHA is given in Algorithm 1 and can be divided into three steps:  
In the initialization step, the algorithm starts with the most valuable router of flow \(j\) as the selected router (\(\hat{K}_{j}\),), i.e., the router which minimizes the utility of flow \(j\). We then need to calculate the bandwidth resources of the switching path and the space resources of the MFT corresponding to each router acting as a late-binding node. In general, resource constraints will now be violated, and the initial choice of selected routers is adapted to satisfy router resources by repeatedly improving on the most violated capacity constraint \(\hat{I}\) as follows. In the progress stage, the initially selected flow in the router with the most serious resource violation is adjusted by repeatedly improving the most violated resource constraint such that the adjusted result conforms to the resource constraint of the routers. First, the selected resource violates the most serious router \(\hat{I}\). Subsequently, for the selected flows in \(\hat{I}\), we calculate the increment (\(\Delta_{kj}\)) of Lagrange multipliers (\(\mu_{\hat{I}}\)) generated by these flows (\(j\)) selecting other routers (\(k\)). The calculation of \(\Delta_{kj}\) is determined by both the bandwidth resources of the switching path and the space resources of the MFT. Eventually,option \(k^{*}\) of flow \(j^{*}\) causing the smallest increase in Lagrange multipliers (\(\mu_{j}\)) is chosen for exchange. The above process is repeated until a router is selected for each flow to meet resource constraints. After the progress phase, the router may still have some bandwidth resources or space resources. These spare resources can be used to improve the solution by replacing some selected flows with more valuable ones. Therefore, in the adjustment phase, for each flow \(j\) we check whether there are other routers \(k\) than the selected late-binding node \(\hat{K}_{j}\) that are more valuable and also check whether the resource constraints are met after replacing the late-binding node of flow \(j\) with \(k\). Among all exchangeable results (\(\delta_{k}^{j}\)), the option \(k^{\prime}\) of flow \(j^{\prime}\) causing the largest increase in the router value is exchanged with the selected item of that flow (\(\hat{