esa-sceva/satcom-chunk-collection huggingface dataset:
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 4 Discussion and Outlook  
### Meta-Analysis of Publications  
To review research studies related to railway infrastructure monitoring using EO data, literature from multiple databases such as Web of Science, Scopus, Google Scholar,and IEEE was searched. The search was conducted using some specific keywords and their combinations, such as \"railway infrastructure\", \"monitoring\", \"Earth Observation\", \"satellite imagery\", \"remote sensing\", \"SAR\". Likewise, through a detailed reference screening of the reviewed studies, additional research studies were identified and included in the review. In total, thirty-four English language publications were identified relevant for the present review, out of which thirty-one were published in international journals and three at international conferences in the time period from 2005 to 2025. For the purpose of review from each research study, some relevant information was extracted, such as authors, date of publication, authors' country, specific application of Earth Observation data, type of satellite data used, satellite used, performed evaluation/testing/validation tests, and test site.  
Figure 3 illustrates the chronological sequence of the reviewed publications. Most publications appeared recently, from 2019 onward. 2022 and 2024 both accounted for 14.7% of the publications.  
The set of 34 reviewed publications were distributed across 27 different international journals and three international conference proceedings (Figure 4). _IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing_, _Natural Hazards_, _Remote Sensing_, and _Sensors_ contained most of the reviewed publications, with two appearing in each. The remaining international journals and international conference proceedings were jointly labeled \"Others\".  
Figure 5 shows the country for the authors of the reviewed publications, based on their affiliations. China is significantly ahead of other countries with sixty authors, followed by Italy with thirty-one, The Netherlands with eight, Spain with eight, and Canada with seven. Authors from remaining 13 countries represented 24% of the total authorship. China and Italy alone accounted for 60.67% of the authorship, with China dominating at 40%.  
Figure 6 shows the number of publications by year of publication split by the use cases. In some of the reviewed publications, the authors examined two or three use cases. Monitoring of ground deformation (GD) was the most-examined use case with 40% share, followed by monitoring of rail track deformations (TD) with 20%. Monitoring of railway bridges (B) and water level around the rail track (WL) were the least-examined use cases with 10% and 5% share, respectively.  
Figure 3: The distribution of publications by year of publication.  
Figure 4: Distribution of publications by international journals and international conference proceedings.  
Figure 5: Distribution of publications based on author country.  
Figure 6: Distribution of publications by year of publication split by the use cases.
## 3 Performance Evaluation
### Experimental Study: Masorry Arch Bridge Model
To locate the damaged region around the arch, localization index DI was formulated using only the sensors on the front face of the arch, specifically T\({}_{12}\) and T\({}_{23}\). With this arrangement, the spatial resolution for damage. Localization was limited to two regions between three sensors: between sensors 1 and 2 (A\({}_{1}\)-A\({}_{2}\)) or sensors 2 and 3 (A\({}_{2}\)-A\({}_{3}\)), as shown previously in Figure 9. The localization results obtained with DI are displayed in Figure 12b. As shown in this figure, damage localized between sensors 2 and 3 progresses into the region between sensors 1 and 2 as the load level increases from 30 kN to 60 kN.
## 3 Maximum Power Point Tracking Algorithms
### Particle Swarm Optimization (PSO)
\begin{table}
\begin{tabular}{c p{227.6 pt}} \hline \hline
1 & Initialize the population of particles within the search space, each with a position and velocity; i.e., define PSO coefficients \(\omega\), \(c_{1}\), and \(c_{2}\) for the global peak search, sequentially send duty ratios, and ascertain the corresponding power. \\ \hline
2 & Evaluate the fitness of each particle using the objective function representing the PV system’s power output under varying environmental conditions. \\ \hline
3 & Update the best solution for each particle and the global best solution based on the fitness evaluation. \\ \hline
4 & Update the velocity and position of each particle using the following: \\  & For particle i: \\  & \(\mathrm{v}_{\mathrm{i}}^{\mathrm{k+1}}=\omega\mathrm{v}_{\mathrm{i}}^{\mathrm{ k}}+c_{1}\mathrm{r}_{1}\left(\mathrm{P}_{\mathrm{best},i}-\mathrm{D}_{1}^{ \mathrm{k}}\right)+c_{2}\mathrm{r}_{2}\left(\mathrm{G}_{\mathrm{best}}- \mathrm{D}_{1}^{\mathrm{k}}\right)\) \\  & \(\mathrm{D}_{\mathrm{i}}^{\mathrm{k+1}}=\mathrm{D}_{\mathrm{i}}^{\mathrm{k}}+ \mathrm{v}_{\mathrm{i}}^{\mathrm{k+1}}\) \\  & where \(\omega\) is the inertia weight, \(c_{1}\) and \(c_{2}\) are acceleration factors, \(r_{1}\) and \(r_{2}\) are random values, \(\mathrm{P}_{\mathrm{best},i}\) is the particle i best position, and \(\mathrm{G}_{\mathrm{best}}\) is the global best position. \\ \hline
5 & Check for the termination criteria: \\  & If the stopping criteria are met, stop the algorithm and output the GPP. \\  & Otherwise, go to step 3. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pseudo code of PSO for tracking the global peak in photovoltaic systems. Figure 4: The PSO flowchart based on MPPT.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 3 Quantization Design Based On PVQ
### Quantization Design of the Reduced-Dimensional Matrix  
In the reduced-dimensional matrix, the first column is the first principal component, the second column is the second principal component, etc. According to the property of PCA, the first principal component has the most important information of the original signal, and information carried by other principal components becomes less and less important. In fact, more than 95% of the original signal energy, which can be also called information, is restored only by the first principal component. That means if the quantization error of the first principal component is large, compared with the original signal, the restored signal also has a large error. Therefore, the first principal component needs to be allocated more bits, and the bits for other principal components should be sequentially reduced. For some kinds of audio, 4 principal components are enough to obtain acceptable quality, while for other kinds of audio 5 principal components may be needed. We choose 6 principal components, because they can satisfy almost all kinds of audio. In fact, the fifth and sixth principal components  
Figure 8: Multi-frame in decoder. (iPCA, inverse Principle Component Analysis; Rd, reduced-dimensional).  
play a small role in the restored spectral; therefore, little quantization accuracy is needed for the last two principal components.  
Based on the above conclusion, the reduced dimensional matrix can be divided into certain regions, as is shown in Figure 9. Different regions have different bit allocations: the darker color means more bits needed.  
A PVQ quantizer was used to quantify the distribution of different bits in each principal component of the reduced-dimensional matrix. Several subjective listening tests have been carried out, and the bits assignments policy is determined according to the quality of the restored audio under different bit assignments. Finally, the bits that need to be allocated for each principal component are determined. Table 2 gives the number of bits required for each principal component of non-zero reduced-dimensional matrix under the PVQ quantizer.
## 2 Materials and Methods
### Serialiser and Deserialiser (SerDes)  
The SALT receives TFC commands and transmits data packets to the DAQ at 320 Mbps via a number of differential e-links, based on the Scalable Low-Voltage Signaling (SLVS) interface [26]. The rate is obtained by fourfold multiplication of the main clock, performed by a dedicated PLL and Double Data Rate (DDR) transmission. It is required that all active output e-links are continuously transmitting data packets [14]. Therefore, when the memory is empty, the chip transmits dedicated Idle packets (constant 12-bit word 0000_11_0000) to fill out the serialiser bandwidth.  
Since the synchronisation of the data stream is performed at the receiver side, the serialiser is relatively simple and a data synchroniser is implemented only at the deserialiser input. Because of DDR, there are two independent synchronisation circuits, one for odd and another for even bits. In each one, data are captured with a single input flip-flop connected to a clock with an adjustable phase generated by the PLL. A block diagram of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Header**} & \multicolumn{2}{c}{**Data**} \\ \hline
**Packet** & **BXID** & **Parity** & \multicolumn{2}{c}{**Type + Length**} & \multicolumn{2}{c}{**Comment**} \\ \hline  & **4-bit** & **1-bit** & **1-bit** & **6-bit** & **12-k-bit** & \\ \hline BxVeto & _bxid[3:0]_ & * & 1 & 010001 & — & BxVeto in TFC \\ HeaderOnly & _bxid[3:0]_ & * & 1 & 010010 & — & Header in TFC \\ BusyEvent & _bxid[3:0]_ & * & 1 & 010011 & — & _nHits_\(>63\) \\ BufferFull & _bxid[3:0]_ & * & 1 & 010100 & — & no space in mem. \\ BufferFullIN & _bxid[3:0]_ & * & 1 & 010101 & — & no space in mem. \\ NZS & _bxid[3:0]_ & * & 1 & 000110 & Values & NZS in TFC \\ Normal & _bxid[3:0]_ & * & 0 & _nHits_ & Hits & Normal event \\ Sync & & _bxid[11:0]_ & & & pattern & Synch in TFC \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data packet format. Both parts Header and Data are aligned to 12-bit word; parity bit ”*” is calculated as the exclusive or of all other bits in the header; \(k=0,1,2,\ldots,66\).  
the PLL is shown in Figure 6. Its basic architecture is rather typical [27]; however, it is highly customised and achieves an ultra-low power of 0.95 mW @ 160 MHz.  
The Voltage Controlled Oscillator (VCO) generates 16 clock phases in which the first one, phase 0 (_pll_clk[0]_), clocks most of the SerDes circuitry. The other two, selected independently by two internal multiplexers (similarly as in the DLL), are connected merely to the two aforementioned input flip-flops in the deserialiser. Thereby, the deserialiser is quite flexible and can handle data streams with time shifts between even and odd bits.  
The PLL operating conditions may be monitored by observation of VCO control voltage \(V_{PLL}\). This voltage can be tuned by a 7-bit DAC, which controls the VCO centre frequency, allowing for compensation in PVT variations.
## 2 The Proposed Method
### Data Preprocessing and Implementation Details
#### 2.5.2 Implementation Details  
The model proposed in this paper consists of four components: an encoder, a teacher generator, a student generator, and a discriminator. Each network architecture within the model is constructed using ordinary convolutional layers, batch normalization layers, and ReLU activation layers. An input image with a resolution of 128 \(\times\) 128 is first divided into 2 \(\times\) 2 non-overlapping image patches. Subsequently, the encoder extracts the features of these image patches. The specific architecture of the encoder can be found in Table 1.  
The teacher generator and the student generator have an identical structure. They utilize the features extracted by the encoder to reconstruct the image. During the training process, they are coupled using knowledge distillation, and the architectural details are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Channels** & **Resolution** \\ \hline Input & 1 & (2 \(\times\) 2) \(\times\) (64 \(\times\) 64) \\
1 & 32 & (2 \(\times\) 2) \(\times\) (32 \(\times\) 32) \\
2 & 64 & (2 \(\times\) 2) \(\times\) (16 \(\times\) 16) \\
3 & 128 & (2 \(\times\) 2) \(\times\) (8 \(\times\) 8) \\
4 & 256 & (2 \(\times\) 2) \(\times\) (4 \(\times\) 4) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Encoder structure in proposed method.  
Figure 6: Railroad track fastener image labeling chart.  
The discriminator participates in the adversarial training and competes with the generators to achieve optimization. When the discriminator discriminates an image, it adopts the full resolution of the image, and its architectural details are shown in Table 3.  
The optimizer chosen for parameter updating was Adam. To ensure that the learning rate could be adaptively adjusted according to the learning progress of the network, the initial learning rates for both the teacher and student networks were set to \(1\times 10^{-4}\). During the training process, the learning rate was gradually reduced to \(2\times 10^{-5}\) after 1000 cycles according to the cosine annealing strategy. The training batch size was set to 16, with a weight decay coefficient of \(1\times 10^{-5}\).
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Topology Aware Routing Protocols
#### 4.1.4 Topology Change Awaring Based Routing Choosing Scheme (TARCS)  
[PERSON] et al. proposed a routing mechanism (TARCS) based on topology change awareness [41]. This protocol mainly includes two parts: periodic topological change perception (PTCP) and adaptive routing choosing scheme (ARCS). The purpose of PTCP is to periodically sense the topology changes of each node and its neighbors within the one-hop transmission range of the network. As the movement of nodes causes the relative position between neighboring nodes to change, and the number of neighboring nodes will also change. Therefore, the network topology will be changed dynamically. Based on the analysis of the factors that affect the topological changes among the network nodes, a topological change degree (TCD) was proposed to represented the topological changes of the sharply dynamic networks. In the next step, the author proposes an adaptive routing choosing method (ARCS). Based on the recognition of the node movement pattern, the appropriate route can be determined. After the routing protocol is selected, the nodes will communicate through the newly setting routing protocol until the next topology change is perceived.  
**Advantages:** (1) Real-time monitoring of topology changes around nodes. (2) The node will react to the perceived results in real time and make adjustments to choose the applicable routing protocol. (3) Make the routing protocol meet the characteristics of node movement and topology change.  
**Disadvantages:** The authors focus too much on topological changes and the limited energy of FANET nodes is not considered in this paper.
## 2 Analysis of Ranging Consistency for ISL Payload
### Temperature Characteristic of the ISL Payload  
Because of the existence of nonlinear phase units in the RF channel and APAA, such as the frequency converter, amplifier, LNA, and filter, the temperature changes generate a group delay and phase distortion when the spread-spectrum signals pass through these non-ideal transmission channels. In addition, the field-programmable gate array (FPGA), ADC, DAC, buffer, and other digital components in the DBD generate time-delay changes with the change in temperature. These changes with the temperature fluctuations are included in the pseudo-code ranging measurements of the distance. The delay characteristics of these equipment components affected by the temperature are inconsistent. Some increase with temperature in which the delay becomes smaller, such as the antenna feed network [22], whereas others increase with temperature in which the delay becomes larger, such as the frequency up-converter [23]. Because almost no information is available from manufacturers on the sensitivity of the group delay to environmental parameters of the component used in a two-way satellite time and frequency transfer (TWSTFT) device, Ref. [24] measured the values (or range of values) and uncertainties of the temperature coefficients of the group delay observed from a variety of modules used in typical TWSTFT systems, including the LNA, HPA, filters, mixers, isolators, up converter, and down-converter, which are listed in Table 1.  
Meanwhile, Ref. [24] measured the temperature coefficients of the delays of the whole transmission and reception channels of the TWSTFT. The temperature coefficient reaches up to 230 ps/\({}^{\circ}\)C. Ref. [25] showed similar statistics for the delay equipment. The delay in the ISL payload component also demonstrated such temperature characteristics. Moreover, at different temperature points, the degree of delay variation is also inconsistent. The ISL antenna is mounted in an extravehicular manner on a satellite payload, and the RF channel and DBD are installed in the cabin. Obviously, the temperature of the whole ISL payload is unevenly distributed. The consistency delay error of the equipment introduced by temperature variation is a large error source in a two-way radio ranging system. If it is not controlled, the measurement error can reach up to tens of nanoseconds, which directly affects the key performance index of the ISL-ranging accuracy.
# Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model
## 4 Simulation and Results
### Results and Analysis  
#### 4.3.1 Normal Passenger Flow  
For the normal passenger flow forecast results, although the \(WMAPE\) algorithm used in the model can well measure the forecast error, we cannot see the intuitive performance of the model from it. Therefore, after the training, we use the mean percentage error function to evaluate the performance of the model site by site.  
Consider the following questions:  
1. The purpose of this paper is to predict in advance the passenger flow congestion problem that may occur when there is an emergency passenger flow;
2. The samples with small passenger flow are often in the starting station or the early morning when the subway is running. For these samples, most of the input passenger flow is 0, so accurate results cannot be obtained.  
Based on the above two points, we investigate the performance of the model in different situations, and the results are shown in Table 3.  
For all the samples with passenger flows of more than 500, there are 585,289 samples, accounting for 97.31% of the total, and the average error is 12.95%.  
Although for the sample with less than 1000 passengers (7.36% of the total sample), the average error of the model is larger, reaching 147.83%, which is nearly double the error, but for the sample with more than 5000 passengers (54.18% of the total sample), the average error is only 4.91%. For the situation we focus on, this model has high accuracy. Here we divide into different sections to observe the accuracy of the prediction results.  
Section 1 shown in Figure 6 is from Nanjing East Road station to people's Square Station, which is a representative large passenger flow interval of line 2. A total of 14 days are randomly selected, and the prediction results are shown in the figure below. It can be seen that when the passenger flow is large, the prediction of normal passenger flow has high accuracy.  
Section 2 shown in Figure 7 is the downward start section from Pudong International Airport Station to Haitian 3 rd road station. Similarly, 14-day data are selected. The prediction results are as follows. It can be seen that for the low passenger flow range, the prediction error is large, especially in the first hour and the last hour of the operation period, but on the whole, except for individual stations, the error remains within a certain acceptable range, and the model performance is still acceptable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Sample Size** & **Error Rate** \\ \hline \(>\)500 & 585,289 & 12.95\% \\ \(<\)1000 & 44,254 & 147.82 \\
1000–5000 & 230,557 & 19.75\% \\ \(>\)5000 & 325,854 & 4.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction effect under different passenger flow ranges.
# Secure D2D Communication for 5G IoT Network Based on Lightweight Cryptography
## 1 Introduction
AEAD ciphers can provide not only data confidentiality but also data integrity and authentication using a message authentication code (MAC) with associated data during the encryption process. These lightweight cryptographic algorithms help to make D2D communication secure and able to process communication efficiently. In this paper, we propose a secure D2D communication system for a 5G IoT network based on lightweight cryptography ECC and the AEAD cipher. First, we analyze typical security threats and present security considerations for D2D communication in a 5G IoT network. Moreover, we survey the existing research on secure D2D communication schemes and make a taxonomy of these results based on our security considerations. Finally, we propose a lightweight cryptography-based secure D2D communication system that can provide anonymity, user authentication, data confidentiality/integrity and efficiency. Because of its lightweight construction, the proposed D2D communication system can be applied efficiently on the 5G IoT network. Our main contributions can be summarized below:  
* We analyze existing typical security threats and secure D2D communication. Then we present security considerations for secure D2D communication for a 5G IoT network. * We survey and analyze existing research based on our security considerations, including authentication, data confidentiality/integrity, anonymity and efficiency. * We propose a lightweight and secure D2D communication system. The proposed D2D communication system is designed based on lightweight cryptography. It can be implemented simply and can efficiently process resource-constrained 5G IoT devices. The remainder of this paper is organized as follows. Section 2 introduces related works where we surveyed D2D communication and analyzed security considerations for secure D2D communication. In Section 3, we propose a secure D2D communication system for a 5G IoT network. In Section 4, we show the simulation results of our proposed D2D communication system. In Section 5, we analyze our proposed D2D communication system based on our security considerations and finally conclude in Section 6.
## 5 Transmission Model of Multipath QKD Network-Based Zero-Trust Relays
### Behavior Inspection Module
#### 5.3.1 Penalty Assignment  
\[W_{j}=\begin{cases}W_{j}^{(0)}+\Delta W,&\text{if }L_{j}\leq d_{\text{max}}\\ W_{j}^{(0)},&\text{O.W}\end{cases} \tag{8}\]  
where \(W_{j}^{(0)}\) is the initial penalty and \(\Delta W\) is an increment factor. The system dynamically penalizes suspected nodes and reallocates keys, ensuring the security fraction \(P_{T}(A,B)\) remains above \(P_{\text{min}}\).