esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method  
In this section, we describe our proposed method in detail. As discussed below, we divide our developed method into the following interconnected steps along with a brief description. Figure 2 shows the flow of the proposed method. In addition, Algorithm 1 shows more details of our developed method.  
To test our method, we gather our own dataset from challenging Pakistani traffic environments. This dataset was collected over a period of two months in different cities of Pakistan. As shown in Figure 2 that the gathered data is preprocessed and augmented. Later it is trained by our model. Meanwhile, the YOLO-v5 model is built and trained. Our collected data is from an unknown distribution in Pakistani traffic. Therefore, it is now tested on the YOLO-v5 model. After the YOLO-v5 is applied, we then investigate and analyse our detector. To aid readers' understanding, below we describe the steps and details of our developed method.  
```
1.Input: A test image with one or more visible vehicles.
2.Execute the algorithm in following order to get the desired result.
3.begin
4.Gather data
5.do
6.Categorize data into LDT and HDT scene images as indicated by Figure 3a,b.
7.Annotate data to identify classes of objects as shown by Figure 4a,b.
8.end
9.begin data preprocessing and augmentation
10.do processing
11.Preprocess that data and split into train, validation, and test set
12.do augmentation
13.Standardize the image and video data from step (3) to step (8) up to \(416\times 416\) pixels.
14.Crop dataset between 0% and 30% zoom.
15.Saturate dataset between \(\pm 25\%\).
16.Vary brightness, such as darken and brighten the images between \(\pm 25\%\).
17.end
18.end
19.begin YOLO-v5
20.do
21.Install all Yolov5 repositories to be ready for running object detection training & inference.
22.Download custom Yolov5 Object detection data.
23.Configure model and architecture.
24.begin Training
25.Train custom YOLO-v5 detector
26.Use training parameters as: use COCO dataset weights
27.image size: 416 \(\times\) 416 pixels, batch size: vary as 5, 10, and 20, epochs: vary as 100, 300, and 500, Configuration: use as per YOLO-v5s, YOLO-v5m, or YOLO-v5L, Weights: use pre-trained COCO dataset,
28.end
30.Run YOLO-v5 inference on test images.
31.python detect.py -weights runs/train/exp/weights/best.p,img 416,conf 0.1.
32.end
33.end
34.Output: An image with detected vehicles through a bounding box around.
```  
**Algorithm 1**Pseudo code of the proposed vehicle detection algorithm  
Figure 2: Flow of the proposed method.
## 4 Discussion
### Data Distribution (Statistical Data Heterogeneity) Challenge  
FL can solve the limited data quantity issue by combining data from each client without directly sharing each client's private data. However, FL also faces statistical data heterogeneity challenges due to data distribution at each client. The data distributions at each client are likely to be different, leading to poor global model performance ([PERSON] et al., 2020; [PERSON] et al., 2020). [PERSON] et al. (2020) demonstrated that the data distributions might considerably decrease FL model performance due to weight divergence induced by different population distributions. Within an FL environment, data distribution is frequently classified into IID and non-IID. Non-IID can result from an imbalance in the amount of data quantity, features, or labels. Non-IID is a common occurrence in the medical domain. Various medical tools manufacturers, different calibrated techniques, and different medical data acquisition techniques are the main reasons why each medical institution generates nonidentical data distribution. For instance, [PERSON] et al. (2020) described how each medical institution uses various brain scanner manufacturers and instructions for each patient when taking autism brain imaging data. Specifically, during data acquisition, one medical site instructs patients to keep their eyes open while others instruct them to close their eyes during scanning. In the following subsection, we describe the non-IID characteristics and mitigation methods.  
Figure 8: The typical medical data partitions scenario for federated transfer learning (FTL). One party is a medical institution, while the other is a healthcare application located in a different region. They share neither the patients’ index \(\mathcal{I}_{j}\
eq\mathcal{I}_{k}\) nor features of medical diagnosis \(\mathcal{X}_{j}\
eq\mathcal{X}_{k}\).
## 3 Proposed Neural-Network-Based Decoding Scheme  
### Proposed Neural Network  
This paper propose a neural network named CAE-LSTM-Net whose structure is shown in Figure 6. The CAE-LSTM-Net comprises of two parts of neural network: one is channel-attention-Net serving as an encoder, and the other is LSTM unit serving as a decoder and equalizer. We introduce this network with the goal of improving the quality of representations and equalization produced by the feature encoder and equalizer. Inspired by channel attention [29], we take into consideration channel-wise feature recalibration in CNN-based descriptor design. As shown in the middle part of Figure 6, the descriptor of the sub-image from the output of the encoder at each time step is a one-dimensional vector generated by flattening the feature maps after channel-wise recalibration. Meanwhile, for the purpose of mitigating the ISI introduced by the exposure time overlap of the previous symbol shown below in Figure 6 in sub-image demodulation, and enhancing the accuracy of recognizing a specific data sequence (i.e., header of each packet) in the data packet, it occurs to us that we can take advantage of the correlations between previous information and current circumstance learned by the LSTM unit and build an equalizer to learn different nonlinear equalization mapping under different symbols arrangement. For instance, as shown in Figure 6, at the second time step, the sub-image is significantly distorted due to ISI brought by the previous symbol at the first time step, which may cause an error decoded bit. The proposed LSTM-based equalizer is capable of learning an appropriate pattern that avoids decoding the current sub-image into the previous one bit, using its short-term memory.  
Figure 4: Sketch map of the LSTM unit.  
Figure 5: The sequence classification model based on LSTM unit. \(x_{t}\) is the input at time step \(t\) and \(h_{t}\) is the output at time step \(t\).
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 2 Background
### Differences between TLS and DTLS
#### 2.4.2 Handshake Protocol Changes  
The unreliability of the transport layer had a major impact on the design of the DTLS handshake protocol. Unlike application data, the handshake protocol must exchange its messages reliably to derive a shared security context successfully. Therefore, the DTLS handshake protocol implements some of the mechanisms we can find in TCP. The handshake protocol uses a retransmission mechanism and message sequence numbers. Note that the handshake message sequence numbers are independent of the record layer sequence numbers.  
The retransmission operation works as follows. The DTLS handshake is divided into multiple flights, see Figure 2. Each time one of the endpoints sends an entire flight, it arms a timer. If the timer expires before the endpoint has received an answer, it resends the full previous flight. Since neither UDP nor DTLS uses acknowledgment packets, it not possible to inform the endpoint which individual DTLS messages inside the flight were received well and which ones got lost. Therefore, DTLS retransmits the entire flight. The default start timeout value is 1 s; it doubles each time the transmission fails, making out at 60 s. Handshake messages can grow larger than the Path Maximum Transmission Unit (PMTU) mostly due to long certificate chains. To prevent IP fragmentation, the DTLS handshake layer supports fragmentation. The handshake header has a fragment offset field and fragment length field to perform reassembly at the receiver side, see Figure 3.  
DTLS uses a connectionless transport protocol, which makes it vulnerable to two types of Denial-of-Service (DoS) attacks. The first attack is a resource consumption attack. A large number of malicious clients send _ClientHello_ packets to exhaust the resources of the server. The second attack is an amplification attack. Malicious clients spoof the IP address of a victim device and send _ClientHello_ messages to the server. The server then responds with the next DTLS flight (containing the _ServerHello_, _ServerCertificate_, _ServerKeyExchange_, and _ServerDone_ messages), potentially overwhelming the victim device. To mitigate these attacks, DTLS optionally uses a cookie exchange technique. At the start of the handshake protocol, the client must replay a cookie provided by the server to demonstrate that it can receive packets at its claimed IP address. The cookie exchange adds one full Round-Trip Time (RTT) to the DTLS handshake latency compared to TLS. Figure 2 depicts the technique in the first two messages. TLS does not suffer from the described attacks since the TCP handshake occurs before the client sends the first TLS message. It automatically detects address spoofing.  
Figure 3: Illustration of the DTLS headers for a handshake message. In the record layer header, DTLS adds the epoch and sequence number fields. In the handshake header, DTLS adds sequence numbers, a fragment offset, and fragment length.
## 3 Results and Discussion
### Analysis of the Microporous Body-Reaction Combustion Simulation
#### 3.1.2 Species Mass Fraction  
(1) Distribution of the reactant mass fraction  
Figure 4a shows the distribution of the methane mass fraction. It can be seen that the concentration is high at the inlet. After the reaction, the concentration gradually decreases along the hole depth direction, while the methane mass fraction at the same hole depth section near the orifice shows a uniform distribution. Figure 4b presents the oxygen mass fraction distribution map. According to the bulk reaction mechanism, methane and oxygen participate in the reaction simultaneously; therefore, the distribution of oxygen is similar to that of methane. Methane combustion requires more oxygen than its own volume, and the same gradient mass fraction of oxygen is lower than methane.  
(2) Distribution of product mass fraction  
After the reaction, the obtained products are mainly H\({}_{2}\)O and CO\({}_{2}\), as shown in Figure 5. Figure 5a,b present the H\({}_{2}\)O concentration distribution map and CO\({}_{2}\) concentra  
Figure 4: Distribution of reactant mass fraction: (**a**) methane mass fraction distribution; (**b**) oxygen mass fraction distribution.  
Figure 3: Microporous body reaction temperature field.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Inlet Velocity** & **Equivalence** & **CH\({}_{4}\) Mass** & **O\({}_{2}\) Mass** & **Pore Size** & **Wall** & **Outlet** \\
**(/s)** & **Ratio** & **Fraction (\%)** & **Fraction (\%)** & **(nm)** & **Shear** & **(atm)** \\ \hline (1) \(6\times 10^{-8}\) & (I) 0.3 & 0.016875 & 0.225 & (a) 60 & Adiabatic & 1 \\ \hline (2) \(1\times 10^{-7}\) & (II) 0.6 & 0.034 & 0.225 & (b) 150 & Adiabatic & 1 \\ \hline (3) \(13\times 10^{-8}\) & (III) 1 & 0.05625 & 0.225 & (c) 300 & Adiabatic & 1 \\ \hline \end{tabular}
\end{table}
Table 3: Boundary conditions setting.  
tion distribution map, respectively. The distribution of H\({}_{2}\)O and CO\({}_{2}\) increases with the depth of the hole and is evenly distributed on the same hole depth section.
## 4 Stochastic Equation  
Changing variables in a differential equation is often a powerful tool for finding solutions to it. We already know the solutions for the Omori Equation (2) and the logistic Equation (6). Nevertheless, we will still change the variable \(n(t)\) in order to linearize both of these equations. This will make it easier for us to search for a stochastic generalization of the equation for the evolution of aftershocks.  
The following replacement will help us transform nonlinear Equations (2) and (6) into linear ones [3]:  
\[n(t)\to g(t)=1/n(t). \tag{11}\]  
Omori Equation (2) takes on an extremely simple form:  
\[\dot{g}=\sigma. \tag{12}\]  
Here the dot above the symbol means time differentiation. Logistic Equation (6) becomes a first-order linear differential equation:  
\[\dot{g}+\gamma g=\sigma. \tag{13}\]  
Generally speaking, this circumstance is quite interesting in itself, but we use it precisely to make the stochastic generalization of the evolution equation the simplest possible way. Namely, let us imagine that the deactivation coefficient experiences small fluctuations. This assumption is formalized as follows: \(\sigma\rightarrow\sigma+\xi(t)\), where \(\xi(t)\) is a random function of time, and \(max|\xi|\ll\sigma\). As a result, we have  
\[\dot{g}+\gamma g=\sigma+\xi(t). \tag{14}\]  
Let us formally solve the Equation (14):  
\[g(t)=g_{\infty}+(g_{0}-g_{\infty})\exp(-\gamma t)+\int_{0}^{t}\xi(t_{1})\exp[ \gamma(t_{1}-t)]dt_{1}. \tag{15}\]  
Here \(g_{\infty}=\sigma/\gamma\), \(g_{0}=g(0)\).  
Our second assumption is that \(\xi(t)\) is the Langevin source, i.e., delta-correlated random function with zero mean  
\[\overline{\dot{g}(t)}=0,\qquad\overline{\dot{\xi}(t)}\overline{\dot{\xi}(t_{ 1})}=N\delta(t-t_{1}), \tag{16}\]where the line at the top means averaging.  
Now Equation (14) should be considered as the Langevin stochastic equation (e.g., see [13], where the Langevin equation is studied in detail). The phenomenological parameter N is determined by the intensity of noise affecting our dynamic system.
## 3 Stochastic MPC Approach for Coordinated Scheduling Optimization of MES
### Model of MPC Based Optimal Scheduling
Usually, the battery manufacturers report the relationship between capacity degradation and the number charge/discharge cycle with the fixed operating strategy employed, i.e., the charge/discharge is complete or half cycle. When EVs' batteries are scheduled in practical application, the real charging/discharging process is complicated, representing local cycle, non-complete or complete charging, and so on. Consequently, when considering the degradation cost of EVs' batteries in the optimization objective, the batteries' life loss cost, approximated in the last item on the right side of Equation (20), is included. Usually for an EV's battery, the total cumulative charging/discharging Ah throughput during the whole life cycle can be obtained by manufacturers, denoted by \(A_{\rm total}\). The actual cumulative Ah throughput in a certain period of time is employed to measure the life loss cost of batteries, and then it can be expressed as [32; 33; 34; 35; 36; 37]  
\[L^{s}_{\rm loss}(t+i|t)\ =\ \frac{\left(X^{s}_{\rm ch}(t+i|t)P_{\rm EV- max}+X^{s}_{\rm disch}(t+i|t)P_{\rm EV-max}\right)\cdot\Delta\ t}{A_{\rm total}}. \tag{21}\]  
The initial investment cost \(C^{s}_{\rm AEVs}\) can be defined as  
\[C^{s}_{\rm AEVs}\ =\ C_{\rm p}DAN^{s}(t+i)P_{\rm EV-max}+C_{\rm c}DAN^{s}(t+i )Cap_{\rm EV}. \tag{22}\]  
In addition to the operation constraints of CHP units and EVs represented in Equations (7)-(19), the supply and demand balance of electricity and thermal energy are considered as follows:  
(1) Electricity power supply and demand balance  
The electricity supply and demand balance should be imposed, which is described as follows:  
\[P^{s}_{\rm EVs}(t+i|t)+P_{\rm CHP\_l}(t+i|t)+P_{\rm CHP\_l}(t+i|t)+P^{s}_{\rm Grid }(t+i|t)\ =\ P_{\rm load}(t+i|t),i\ =\ 1,\cdots,T, \tag{23}\]  
where \(P_{\rm CHP\_l}(t+i|t)\) and \(P_{\rm CHP\_l}(t+i|t)\) are \(i\)-step-ahead scheduled values of CHP electricity power generation, and \(P_{\rm load}(t+i|t)\) is the electricity demand. Power exchange between the MES and the external main grid is constrained by  
\[X_{\text{ch}}^{\text{s}}(t+i|t)\leq B_{\text{ch}}^{\text{s}}(t+i|t),i\ =\ 1, \cdots,T, \tag{15}\]0  
where \(P_{\text{Grid}}^{\text{max}}\) is the maximum power purchasing from external grid. Thermal power supply and demand balance  
Practically, it is not necessary that the thermal supply is absolutely balanced by its demand; on the contrary, maintaining the thermal supply and demand balance within an accepted range is desirable. Consequently, the thermal supply and demand balance should follow the following constraints:  
\[X_{\text{ch}}^{\text{s}}(t+i|t)\leq B_{\text{ch}}^{\text{s}}(t+i|t),i\ =\ 1, \cdots,T, \tag{15}\]1  
where \(\eta_{h1}\) and \(\eta_{h2}\) are the lower and upper allowed fluctuation coefficients.
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
###### Abstract  
Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular, massive multiple-input-multiple-output (MIMO) and its future evolution of large intelligent surfaces (LIS), which provide more reliable channel quality than previous technologies. At the same time, network slicing in 5G and beyond systems provides easier management of different categories of users and traffic, and a better basis for providing quality of service, especially for demanding use cases such as industrial control. In previous works, we have presented solutions for scheduling industrial control traffic in LIS and massive MIMO systems. We now consider the case of dynamic slicing in the radio access network, where we need to not only meet the stringent latency and reliability requirements of industrial control traffic, but also minimize the radio resources occupied by the network slice serving the control traffic, ensuring resources are available for lower-priority traffic slices. In this paper, we provide mixed-integer programming optimization formulations for radio resource usage minimization for dynamic network slicing. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within 1 s, making our approach feasible for use in real deployment scenarios.  
Keywords:industrial communications; scheduling; massive MIMO; large intelligent surfaces; LIS; network slicing +
Footnote †: _Future Internet_2024, 16, 71. [[https://doi.org/10.3390/fi16030071](https://doi.org/10.3390/fi16030071)]([https://doi.org/10.3390/fi16030071](https://doi.org/10.3390/fi16030071))
# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 6 Numerical Results
### Simulation Results
#### 6.3.1 Convergence Performance
Figure 9: Comparison of ratios of incomplete tasks for two learning algorithms with different priorities: (**a**) performance comparison; (**b**) performance improvement. Figure 7 shows the comparison of all five algorithms with different computation capacities of VEC \(f_{VEC}\). We set the range of average computation capacity of all available VECs as [0.9, 1.2, 1.5, 1.8, 2.1] GHz. The curves of OT-RSU and LE-VP show that although the computational capacity of VECs is time-varying, the performances of these two algorithms cannot be affected because the average cumulative rewards of them are independent of the computational capacity of V2V. The performances of COPP-DDPG and BOP-DDPG are better than those of other algorithms due to their outstanding learning ability. Besides the uplink and downlink transmission rate of the V2I communication between ICV-I and RSU-\(j\), \(r_{ij}^{UL}\) influences the performances of the five algorithms; see Figure 8. The transmission rates between ICV and VEC (V2V communication) are also significant for all algorithms. Figure 8 shows the performances of the five algorithms with the different transmission rates of V2V. Given the curves of OT-RSU and LE-VP, the communication between V2V does not affect the performances of the two algorithms. However, with the increase in the transmission rate of V2V, the average cumulative rewards of COPP-DDPG, BOP-DDPG, and OT-VEC increased gradually, because the higher the transmission rate is, the lower the delay is, and the average cumulative reward increases. Furthermore, we will verify the performances of the algorithms considering the privacy preservation of ICVs. As mentioned in Section 3.5, pseudonym changing is an effective means for ICV privacy preservation. Figure 9 displays the comparison of the ratios of incomplete tasks for two learning algorithms with different priorities \(Q_{i}\). As shown in Figure 9, we set the change in the ICV's pseudonym to follow the Bernoulli distribution with parameter 0.5--that is, the probability of the ICV changing pseudonyms is 0.5; \(P(N_{i(t)})=0.5\). In terms of average cumulative rewards, COPP-DDPG's performance is better than that of BOP-DDPG. In addition, the completion rate of tasks with high priority is significantly higher than that of tasks with low priority. This is because the setting of the reward function \(r(t)\), which is related to the cost function \(U_{i}\), can guide the training of the two learning algorithms (COPP-DDPG, BOP-DDPG). As shown in Figure 9, the probability of ICV changing pseudonyms was set as \(P(N_{i(t)})=0.6\). With the incrementing of pseudonym change probability, the higher the privacy-preserving level of ICV, which can be used as VEC, the higher its corresponding privacy cost, resulting in higher cost and smaller average cumulative return.
## 5 Results of the TMC Uncertainties Assessments
### TMC Assessment of TBR Uncertainties Using Lithium and Beryllium Random Files
#### 5.1.3 TBR Uncertainty for Lithium and Beryllium Nuclear Data  
Using the generated with BEKED 300 random files for \({}^{6}\)Li and \({}^{9}\)Be from JEFF-3.2 library, the TMC calculations were performed for the TBR assessment in the HCPB and WCLL DEMOs involving both data sets together. The n+\({}^{6}\)Li included the modified covariance data as discussed previously. The rest of the nuclear data involved in the MCNP calculations were taken from the JEFF-3.2 library. The TMC simulations were performed with 10,000 repetitions. The results of the computations fitted with Gaussian distribution are presented in Figure 8. The position of the mean TBR for the HCPB DEMO was found to be 1.181, the standard deviation being \(\sigma\) = 0.030 (2.63%). For the WCLL DEMO, the mean TBR = 1.147 and the standard deviation was calculated to be \(\sigma\) = 0.032 (\(\sim\)2.8%).  
Figure 8: TBR statistical distributions with variated nuclear data for n+\({}^{6}\)Li and n+\({}^{9}\)Be.  
Figure 7: TBR statistical distribution with variated original n+\({}^{6}\)Li data (**a**) and TBR statistical distribution with variated modified n+\({}^{6}\)Li data (**b**).