esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions
**Abbreviations**  
3 GGP  
Third generation partnership project  
5 GPP  
Fifth generation partnership project  
5G network  
Fifth generation network  
AIMS  
Advance interference mitigation strategy  
AKA  
Authentication and key agreement  
AMF  
Access and mobility function  
AUSF  
Authentication server function  
AS  
Access stratum  
BLE  
Bluetooth low energy  
BPSK  
Binary phase shift keying  
CBRS  
Citizen broadband radio service  
CIAA  
Confidentiality, Integration, Authenticticity, and Availability  
CK  
Cipher Key  
EAP  
Enhanced authentication protocol  
E2E  
End-to-end security  
EC-GSM-IoT  
Enhanced coverage-global system for mobile Internet of Things  
ECC  
Elliptic Curve Cryptography  
ED
End device  
EPC  
Evolved packet core  
eMBB  
Enhanced mobile broadband  
eMTC  
Enhanced machine type communication  
eNB  
4G/LTE Base ststion  
GFSK  
Gaussian frequency shift keying  
gNB  
5G Base ststion  
IK  
Integrity Key  
IoT  
Internet of Things  
IIoT  
Industrial Internet of Things  
K  
Secret key  
KAMF  
Access and mobility function (AMF) key  
KNASint  
Integrity key for the non-access stratum (NAS)  
KNASenc  
Confidentiality key for the NAS signalling protection  
KUpint  
Integrity key for the access stratum (AS), also denoted by UP  
KUpnet  
Confidentiality key for the AS or UP  
KRRCint  
Integrity key for the radio resource control (RRC)  
KRRCenc  
Confidentiality key for the RRC  
KNAIWF  
Key for the Non-3 GPP Inter Working Function (NJWF)  
KALSF  
Key for authentication server function (AUSF)  
Key for the Security anchor function (SEAF)
## 4 Analysis of the Experiment Data
### Channel Temporal Coherence and Cluster Analysis
The three-dimensional plot of the CIR and the temporal coherence analyses at 3405 m are given in Figure 19.  
Figure 19: (**a**) Temporal variability of CIR at 3405 m (the inset is a partial enlargement of the results of D and S); (**b**) temporal coherence versus lag time; (**c**) temporal coherence versus lag time for selected clusters. Figure 17: (**a**) Temporal variability of CIR at 1805 m (inset is a partial enlargement of the results of B and SB); (**b**) temporal coherence versus lag time; (**c**) temporal coherence versus lag time for selected clusters. The temporal coherence of the channels was high (>0.7) due to D, with its high temporal coherence (>0.85) and high energy contribution to the channel. B displayed the highest temporal coherence (Figure 19c), as observed previously, indicating that B was more stable than the other channels. The temporal coherence of path clusters related to the sea surface was lower than that of D and B. These path clusters related to the sea surface were considered random clusters. It is worth mentioning that the phase changes of all clusters in the direct-arrival zone were high-energy clusters, and their phase changes could be analyzed over time [11]. Figure 20 plots the temporal phase changes of different path clusters (relative to the initial signal-sampling time) at 3405 m.  
D and B exhibited less phase fluctuation than S and SB. Sea surface fluctuations changed the grazing angles of the paths in S and SB, thereby randomizing their phase changes. The slow phase changes of D in the direct-arrival zone explain the obvious amplitude changes but high temporal coherence of D in this zone. The above analysis led to the following conclusions:  
(1) The temporal coherence was very low (0.2-0.6) in the shadow zone but very high in the direct-arrival zone, which received high-energy path clusters (0.6-0.8). (2) At different depths, random fluctuations of the sea surface always deteriorated the temporal coherence of clusters related to the sea surface, but the temporal coherence of B was consistently high. (3) The temporal coherence of a single-path cluster affected the temporal coherence of the channel only when the cluster obviously contributed to the channel. A path cluster with high randomness largely contributed to the channel energy and degraded the temporal coherence of the channel.
# Co-Firing of Fast Pyrolysis Bio-Oil and Heavy Fuel Oil in a 300-kW\({}_{\text{th}}\) Furnace
## 3 Results and Discussion
### Combustion Tuning  
Before the subsequent experiments were conducted, the air flow rate in each case was tuned to meet the minimum amount of excess oxygen required for combustion, which is the so-called combustion tuning process. In each case, stable combustion was maintained for at least thirty minutes before taking data. The minimum amount of excess oxygen required for combustion was determined by the minimum CO emissions from the flue gases measured at the exhaust [22].  
The formation of nitrogen oxides has a clear relevance with excess oxygen and temperature in combustion. NO concentration decreases with decreases in excess oxygen. However, CO formation increases rapidly when the excess oxygen is reduced below a critical point. For a complete combustion situation, the excess oxygen must be adjusted above this critical point. Below the critical point, the combustion situation is in an incomplete combustion condition. Then, there is smoke generation.  
The emulsion of 2.5% of bio-oil in heavy fuel oil was chosen for testing to determine the oxygen requirement for complete combustion and to gradually adjust the amount of combustion air. The variations in the emissions in the flue gas with excess oxygen concentration are shown in Figure 3.  
The fuel flow rate (\(\dot{Q}_{F}\)) was fixed at 20 L/h in the combustion tuning and stable combustion processes. As shown in Figure 3, points A, B, C, D, E, and F correspond to the air flow rates (\(\dot{Q}_{A}\)) of 214, 212, 210, 207, 205, and 204 Nm\({}^{3}\)/h, respectively. The excess oxygen concentration decreased with decreases in the flow rate of combustion air, from A (214 Nm\({}^{3}\)/h) to F (204 (Nm\({}^{3}\)/h). For combustion air that was greater than the critical point for complete combustion (point C in the figure), the amount of CO formation was nearly 0 ppm. However, when the amount of combustion air was less than that of complete combustion air, a large amount of CO was generated (as shown at the F-point). Increasing the combustion air led to an increase in the excess oxygen concentration. As expected, NO formation increased with increases in the amount of excess oxygen (from F to A). Therefore, the NO emission decreased with decreases in the combustion air. Formation of SO\({}_{2}\) depends on the sulfur content of the fuel oil itself. Hence, when using the same fuel, the SO\({}_{2}\) emissions almost maintain a constant value. The results show a better adjustment of combustion air at point B. With an air flow rate of 212 Nm\({}^{3}\)/h, NO formation was not high, and CO was low. This condition was adopted for subsequent experiments, because it achieves the optimum operating condition that meets the minimum excess oxygen requirement with the least flue gas heat loss.  
Figure 3: Combustion tuning process for burning the emulsion of 2.5% bio-oil + 97.5% heavy fuel oil (HFO).
## 4 Improved Tight Combination Model
### Time Synchronization Correction  
In fact, the CPU clocks of the positioning base stations are different. Even if the clocks can be corrected periodically, the synchronization error and the positioning error can hardly be completely eliminated.  
In general, a time offset of \(1\) ns with an optical speed can cause a 30 cm position error theoretically. To minimize the positioning error, the clock offsets of all on-field base stations can be corrected before the base station ranging. Therefore, although the difference between the non-synchronized clock only exists at the picosecond level, with light speed, it can still cause a few meters' error in the ranging result. Among them, the offset between the crystal clock and the system global clock, as well as the antenna delay, are the two remarkable factors leading to the UWB clock offset. The overall clock model is shown in Equation (17) [22]:  
\[t^{M}=\alpha_{f}^{M}t+\beta_{t}^{M}\pm\tau^{M} \tag{17}\]  
where \(\alpha_{f}^{M}\) is the normalized process based on the system reference clock, and the value is approximately 1; \(\beta_{t}^{M}\) is the clock offset based on the system reference clock; and \(\tau^{M}\) is the antenna delay error.  
To reduce the errors above, a counter can be used to synchronize the timestamp from a slave base station to the master base station. When calculating the time synchronization for the \(k\)th moment, the clock drift (offset) rate between time \(k\) and \(k-1\) can be calculated. For wireless clock synchronization \(k\), its clock drift rate is  
\[y_{k}=\frac{T_{B_{k}}-T_{B_{1k-1}}}{T_{A_{k}}-T_{A_{k-1}}} \tag{18}\]  
When the timestamp signal is received between the wireless clock synchronization moments \(k\) and \(k+1\), the value of the timestamp of the slave base station is \(C_{k}\). At this moment, the timestamp of the slave base station receiving the label signal is synchronized to the timestamp of the master base station, and the synchronization time is  
\[T=T_{A_{k}}+\frac{C_{k}-T_{B_{k}}}{y_{k}}+T_{AB} \tag{19}\]  
where \(T_{AB}\) is the flight time between the base master station A and slave station B, which is determined by the distance between these two base stations.
## 1 Introduction  
Liquid crystals (LCs) [1], as tunable elements, are increasingly being used for non-display applications, including intelligent windows, tunable phase-retarders, terahertz bandgap fibers, and spatial-light modulators [2; 3; 4; 5]. Owing to the advantages of low cost and high compactness, phase-only spatial light-modulators (SLMs) have additional potential applications in areas such as adaptive optics and wave-front control [6; 7; 8]. Two of the most common are liquid crystal on silicon (LCOS) and optically addressable types. The LCOS type is reflective and is an electrically addressed type, which means it can easily cause spectral distortion because of Fabry-Perot interference and the black-matrix effect generated by the two-dimensional (2D) periodic opaque electrodes, which can make the beam quality worse. Compared to an electrically addressable spatial-light modulator, the optically addressable spatial-light modulator (OASLM) does not require complex addressable electrode circuits. The filling factor of this type reaches 100%, which means that the generated phase distribution is continuous, not multistep. Its high resolution and simple manufacturing process renders OASLM an ideal alternative for wave-front modulation of laser beams [9].  
An OASLM with parallel-aligned liquid-crystal light valves (LCLVs) works in phase-only mode and the influence of driving conditions on the phase-modulation capability of the OASLM is decisive for its performance. Traditional measurement of the phase retardation of a laser is performed using a Mach-Zehnder interferometer [10], Twyman-Green interferometer [11], and wave-front sensor (WFS) [12]. The first two devices are very sensitive to mechanical vibration and air turbulence, and the WFS is limited by its inherently restrictive measurement accuracy range. [PERSON] et al. proposed the unwrapping method to characterize the retardance function of liquid-crystal variable retarders(LCVRs) [13]. This method can quickly and accurately obtain the original, continuous function of the voltage-retardance relationship by removing discontinuities and is applicable to measure the retardance variation of the SLM.  
In this study, the impact of driving conditions, including the frequency of the applied voltage and the write-light irradiance on the modulation ability of phase-only OASLM, is investigated. 1\(\lambda\) phase-control ability can be acquired using the home-made OASLM under an applied voltage of 14 V at 200 Hz. In order to monitor the phase-control ability of the OASLM, a detection system including a charge-coupled device (CCD) and WFS was constructed and the aforementioned unwrapping procedure was applied to deduce the phase modulation from the transmittance. The modulation result of a binary image with variable frequencies, and the modulation transfer function (MTF), indicate that the proposed system has a spatial resolution of 200 \(\upmu\)m for modulation measurement and a minimum distinguishable contrast of 1 mm. This detection system can correctly monitor the modulation results of the 2D chessboard grayscale image.
# A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
## 3 System Model
### Calculation of MAC Layer Trust
The monitoring node \(i\) can detect a retransmission by observing a repeated sequence number in the head of frames. It monitors the number of retransmissions of node \(j\) during the time period of \(\Delta t\), which is denoted by \(y_{\overline{ij}}(t)\). It can also obtain the average number of retransmissions \(\overline{y(t)}\) during the time period of \(\Delta t\), by monitoring the number of retransmissions of its neighboring nodes. \(\overline{y(t)}=\frac{1}{n}\sum_{k=1}^{n}y_{ik}(t)\), where \(y_{ik}(t)\) means the number of retransmissions of node \(k\) during the time period of \(\Delta t\), node \(k\) is one of the neighboring nodes of node \(i\) and \(n\) denotes the number of neighboring nodes of node \(i\). Then, the relative deviation of the number of retransmissions of node \(j\) can be calculated by:  
\[RD_{\text{num\_retr}}(t)=\frac{\overline{y(t)}-y_{\overline{ij}}(t)}{\overline {y(t)}} \tag{11}\]  
Figure 2: 802.11 Distributed Coordination Function (DCF) operation. RTS = Request to Send, ACK = Acknowledgment, CTS = Clear To Send, DIFS= distributed inter-frame spacing, SIFS = short inter-frame spacing. and the number of retransmissions trust can be expressed as:  
\[T_{ij}^{num\_retr}(t)=\left\{ \begin{array}{rl}1-RD_{num\_retr}(t),&\text{if }y_{ij}(t)<\overline{y(t)}\\ 1,&\text{else}\end{array} \right.. \tag{12}\]  
If the number of retransmissions of node \(j\) is less than the average number of retransmissions, its trust value will decrease.
## 3 Preliminaries
### Energy Model
#### 3.1.1 Analysis of the Transmission and Receiving Energy Consumption Model  
The energy model of the transmission and receiving energy consumption that is used in this study is based on similar radio model, as used in our previous study [50]. Once it is sent or received, a bit of data to make energy consumption minimize according to distance for this model, the energy consumption of a sensor node for transmission, and processes \(E_{TX/RX}\) is measured as Equation (1). Figure 2 illustrates the network energy consumption model. \(l\) is data size, \(E_{ecc}\) represents the energy depletion per bit of the node to electronically work the transmission or reception, \(e_{fs}\) and \(e_{amp}\) indicate the types of radio amplifiers for free space and multiple routes, respectively. \(E_{TX/RX}(l,d)\) denotes the energy consumption in sending and receiving data for the \(l\) bits.  
\[E_{TX/RX}(l,d)=\left\{\begin{array}{cc}&lE_{ecc}+l_{fs}d^{2},\ d<d_{0}\\ &lE_{ecc}+l_{amp}d^{4},\ d\geq d_{0}\end{array}\right. \tag{1}\]  
We assume that the energy that is depleted for sending and receiving data are the same with each other. The reference distance (\(d_{0}\)) is computed, as Equation (2).  
\[d_{0}=\sqrt{\frac{e_{fs}}{e_{amp}}} \tag{2}\]  
In this study, unlike our previous study, the acknowledgement of energy consumption was added to energy consumption. When the data is transmitted to a point, the acknowledgment message that the data is delivered is transmitted. Therefore, the energy that is consumed at this stage should be calculated. For a transmission from a case \(i\) to the case \(i-1\); the average number of desired packet transmissions is calculated, as Equation (3).  
\[N_{p}=T_{p}\sum_{i=1}^{k}\frac{1}{\left(1-p\right)^{i-1}} \tag{3}\]  
where \(T_{p}\) is the number of packets to transmit, \(p\) is the probability value [0,1] of packet transmission, and \(k\) is the number of waited transmissions. The number of acknowledgement transmissions can be expressed, as in Equation (4).  
\[N_{a}=\frac{T_{p}}{p} \tag{4}\]  
In addition, acknowledgement energy consumption cost \(E_{ack}\) for a \(T\) periyod can be given in Equation (5). Finaly, the total energy consumption \(E_{T}\) is calculated, as in Equation (6).  
\[E_{ack}=\ TN_{p}N_{a} \tag{5}\]  
\[E_{T}= E_{TX/RX}(l,d)+\ E_{ack} \tag{6}\]  
Figure 2: Energy distribution structure.
## 3 Results  
### Comparison  
We conducted object detection tests using the proposed AMS_YOLO and AMSpiking-VGG, comparing their performance with benchmarks to validate the effectiveness of the spatial channel attention mechanism in object recognition. Additionally, the time step was set as 1 **(T = 1)**. A comparison of some detection results is shown in the Figure 6.  
We compare our proposed AMS_YOLO and AMSpiking-VGG models with current efficient spiking natural networks. As presented in Table 1, both network architecture and training methods significantly impact object detection performance. Additionally, it is evident that the attention mechanism and neuronal variants have a positive impact on detection accuracy.  
Figure 6: The AMS_YOLO predict result on COCO2017 dataset. As shown by the visualization results, the detection accuracy of each category performs well, reaching an all-category average of 0.335 and 0.173.  
Given that CBAM [25] can effectively capture key images feature through channel attention modules and spatial attention modules, it focuses on important global and local perspectives, respectively. Additionally, it is a lightweight module that does not significantly increase the computational load on the network, achieving a good balance between performance and computational cost.As shown in Table 1, Spiking-YOLO achieved an mAP(0.5) of 0.257 [15], and a Bayesian optimization, and EMS_YOLO achieved 0.211, and 0.328, respectively [17]. Surprisingly, our proposed network achieved the best performance of 0.335, which is 0.007 higher than the EMS_YOLO method.It also represents that AMS_YOLO optimized by the PLIF [5] and CBAM mechanisms achieved a high mAP(0.95) of 0.173 on the COCO2017 dataset, which is a 6.7% improvement in accuracy compared to EMS_YOLO. Notably, the proposed network is more computationally efficient, with a lower parameter count. Moreover, the parameters of EMS_YOLO are 6.2M [17] and our AMS_YOLO has 5.9M, which results in a 4.83% reduction in consumption.  
We also compare our AMSpiking-VGG network with the baseline spiking-VGG network to further validate the effectiveness of the attention mechanism module. On the GEN1 [24] dataset, the results of the AMSpiking-VGG network are compared with those of the baseline network. Results indicated an improvement in mAP from 0.158 to 0.200, reflecting an enhancement of 11.4% in detection performance. These results demonstrate that the CBAM module effectively integrates spatial and channel information, and results in improvements in accuracy. Furthermore, the Spiking-VGG [16] has 12.2M parameters and AMSpiking-VGG only has 12.7M, so our optimized network did not require as much computational consumption or as many parameters.
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 3 System Model
### The Proposed DLC Protocol: The Underlying Structure
Furthermore, node \(n_{k}\) updates the CM table, the CH table is also updated, and then the cluster is formed. The obtained clustering table can be summarized in Table 2, where \(\texttt{I}_{\texttt{ID}}\) is the node ID, Stat is the node status (CM, GW, or CH), \(\texttt{CH}_{\texttt{ID}}\) is the CH ID to which the node belongs, and CM is a list of the node IDs of CM belonging to the same cluster as the current node, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\texttt{N}_{\texttt{ID}}\) & Stat & \(\texttt{CH}_{\texttt{ID}}\) & CM \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Clustering table of the proposed DLC protocol. Figure 3: The Flowchart of the Proposed DLC Protocol. Taking into account the dynamic and random movement of nodes in VANETs, it is common for nodes to frequently switch over between clusters. Nevertheless, with the proposed clustering algorithm, member nodes including the source and GW nodes exhibit minimal switching between clusters. This stability can be associated with the similarity in mobility patterns among CM. Table 3 shows the summary of the packet list involved in the proposed DLC protocol.
## 5 Verification of Comprehensive Evaluation Model for Honing Processing Quality
### Determining the Weight of Influencing Factors of Honing Machining Quality
The data in Table 5 are passed through the algorithm in Table 3, which could obtain the weights of each attribute of the sub-criteria layer C with respect to the top layer A, as shown in Table 6 The weights are normalized to obtain \(w_{t}\). Firstly, the SPA matrix is processed using \"mean + variance\". Secondly, the \"mode\" of the plane vector set is calculated and normalized, and finally, the integral weights of elements at each layer are obtained, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Evaluation Objective** & **Factor of Evaluation** & **Tra-FAHP Weight** \\ \hline \multirow{8}{*}{Material removal amount C4} & Abrasive grain abrasion D1 & (0.028, 0.041,  
From the data in Figure 3, it can be concluded that the honing speed, honing depth, feed rate, and cross-axis angle are controllable parameters in the actual machining process. The honing speed has a weight value of 45.4% in the overall evaluation of micro-edge honing characteristics, which significantly impacts the quality of honing operations. Additionally, abrasive grain fracturing (7.5%), abrasive grain shedding (11.5%), and abrasive grain size (7.3%) are affected by the honing wheel life and the hardness of the workpiece. In different honing conditions, only by choosing reasonable characteristic process parameters can the honing ability of the honing wheel be brought into full play and good honing effect be obtained. To further confirm the significance of the factors, sensitivity analyses were performed through the trapezoidal fuzzy set-pair analysis method.