esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Semantic Segmentation Models Based on Feature Fusion Module  
Deep neural networks compute features layer by layer, which leads to semantic gaps in features between different layers. Further, it makes it difficult for the detector to obtain specific-enough semantic information. Feature fusion methods have become the new focus, which can reduce the semantic gaps and fully exploit multi-scale features [32].  
[PERSON] et al. proposed (AF)2-S3 Net [33] for 3D LiDAR semantic segmentation. It is a CNN network with an end-to-end encoder-decoder. In the encoder, a novel multi-branch attentive feature fusion module is presented. In the decoder, a unique feature selection module is presented. Combining voxel-based learning and point-based learning, the proposed method demonstrates its advantage in the large 3D scene scenario. The proposed method ranked 1 st on the large-scale SemantiKITTI benchmarks.  
[PERSON] et al. proposed an ultrasound breast lesion image segmentation method based on a dilated semantic segmentation network (Di-CNN) [34]. Combined with morphological erosion operations, the proposed method includes a 24-layer CNN for feature extraction. It uses a parallel fusion method to fuse the feature map. The method outperformed other algorithms on the mask-based ultrasound imaging dataset and achieves an accuracy of 98.9%.  
[PERSON] et al. proposed an end-to-end multi-scale adaptive feature fusion network (MANET) [35] that addresses the challenge of the semantic segmentation of high-resolution remote sensing images. It involves a multi-scale context extraction module (MCM) and an adaptive fusion module (AFM). To fuse semantic features, it integrates a channel attention mechanism. It also generates global features by concatenating high-level semantic information and low-level semantic information. With experiments conducted on the Potsdam and Vaihingen datasets, the proposed MANET outperformed other existing networks, achieving an accuracy of 89.4% and 88.2%, and an average F1 of 90.4% and 86.7%, respectively.  
[PERSON] et al. proposed a self-attention feature fusion network (SA-FFNet) [36] for semantic segmentation. A vertical and horizontal compression attention module (VH-CAM) is adopted into the network, as well as an unequal channel pyramid pooling module (UC-PPM). The feature map is compressed vertically and horizontally to achieve richer information in each pixel. It also improves the special feature map. Experiments are conducted on two datasets: PASCAL VOC2012 and Cityscapes; the proposed method outperformed other models with an mIoU of 76.42% and 73.13%, respectively.
# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 5 Experimental Results and Discussions
### Simulation Experiment
The mean cross-track curve is also a commonly used image quality evaluation method, through which the overall trend of the image can be clearly observed. Next, we conducted a qualitative evaluation of various results through the mean cross-track curve. It can be seen from Figure 11 that the curve of Figure 11b,d is significantly different from that of Figure 11a, and there is a significant curve fluctuation caused by incomplete stripe removal. This conclusion is fully consistent with the subjective feeling of Figure 9. The curve of Figure 11e has obvious deviations in some areas compared to the original curve. After comparing Figure 10a, it was found that the CFPN in these areas has a certain width. It can be inferred that the VSNR denoising method will produce a certain error when processing wide stripes. The overall situation of the Figure 11c curve is better than that of Figure 11b,d,e, but there is excessive smoothness, which means that the details of the image are lost. Figure 11a,g are very similar in appearance. Figure 11: _Cont._  
Figure 10: Stripes extracted by various methods. (**a**) Added CFPN; (**b**) WAFT; (**c**) UTV; (**d**) ASSTV; (**e**) VSNR; (**f**) SILR; (**g**) \(\ell_{0}\) sparse; (**h**) LRSUTV. When you observe carefully, you will find that the curve of Figure 11g has an overall upward shift, which means that the denoised image has an overall shift in brightness compared to the original image. The curves of Figure 11f,h are very similar to Figure 11a in terms of strength and shape, but Figure 11f has a certain degree of excessive smoothing, while Figure 11h retains more details.
## 7 Conclusions  
Based on a multiple antenna arrays transmission model at the transmitter side for DM, where all signals from multiple antenna arrays are mixed at the receiver side, two secure and precise AN-aided DM schemes with single carrier and multiple carriers were proposed, respectively. Then, the SER, secrecy rate, and the robustness performance of the proposed schemes were analyzed and simulated. Simulation results demonstrate the advantages of precise transmission and neighbor security for the proposed schemes over the traditional AN-aided DM scheme using a single antenna array. Meanwhile, it is of considerable interest to extend to a multi-user and multi-eavesdropper DM system over multi-path fading channels. Moreover, the robust DM designs for the proposed schemes are also well worth considering. One future work is to investigate and validate the security performance of the proposed DM schemes in practical LOS communication scenarios.  
All authors contributed extensively to the study presented in this manuscript. [PERSON] designed the main idea, methods and experiments, interpreted the results and wrote the paper. [PERSON] and [PERSON] supervised the main idea, edited the manuscript and provided many valuable suggestions to this study. [PERSON] and [PERSON] carried out the experiments.  
This work was supported by Natural Science Basic Research Program of Shaanxi under Grant No. 2019 JM-591, and by National Key R&D Program of China under Grant Nos. 2017 YFB1402103 and 2017 YFB10203104. It was also funded by Open Research Fund of CAS Key Laboratory of Spectral Imaging Technology, Program for Changjiang Scholars and Innovative Research Team in University under Grant No. IRT17R87.  
The authors would like to thank [PERSON] from Northwestern Polytechnical University for his many constructive suggestions and comments that helped to improve the quality of the paper. The authors would also like to thank the anonymous reviewers and editors for their efforts in reviewing the manuscript and providing many insightful comments and suggestions, which helped greatly in improving the final manuscript.  
The authors declare no conflict of interest.
# TSCH Multiflow Scheduling with QoS Guarantees:
## 3 Results
### Radio Duty Cycle  
The energy consumption in TSCH is mainly due to radio use and can be estimated as the fraction of time that each node remains in the transmit and receive states. Therefore, the use of different scheduling processes causes a large variation in power consumption over the same scenario. In Figure 5a the different schedulers are observed with a packet period of 70 ms. MSF has the lowest consumption, which is due to the use of a shared slot and the dynamic allocation of the dedicated slots. Orchestra has a slightly higher consumption with a higher use of the radio in transmission due to the fact that in addition to the shared slot, it has a dedicated transmission slot in each node. For SDN WISE-TSCH and AMUS, the same amount of transmit and receive states are used. The difference in consumption is due to the amount of control traffic sent by each. SDN WISE-TSCH uses a proprietary protocol to exchange messages with the controller with less impact on bandwidth. AMUS uses the RPL protocol in addition to the communication with the PCE.  
Increasing the packet period to 140 ms slightly increases the radio usage in all protocols Figure 5b, however the behaviour is similar to the previous packet period. When using a packet period of 500 ms, Figure 5c, the radio usage in reception increases, because sending with a longer period will make the total number of reception slots much higher than needed. Therefore, the Orchestra consumption is similar to that of SDN WISE-TSCH. This is because with SDN it is possible to adapt the slotframe size according to the packet period, so that the total number of cells in reception in SDN WISE-TSCH coincides with the flow with the highest deadline. In this case, for 500 ms the slotframe would have 49 timeslots. In all cases AMUS is the scheduler with the highest power consumption.
# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 5 Simulation Results  
This section presents the simulation outcome and performance analysis of the suggested deep Q-network (DQN)-based migration scheme for virtual BBUs in a cloud radio access network (C-RAN). The section aims to demonstrate the excellence of the suggested solution in terms of resource utilization, energy efficiency, and migration performance compared to traditional methods.
### Retrospective Photogrammetry: Examples
Problems that had to be addressed when developing a 3D model from video data were as follows: the impossibility of processing a single image and the poor results when only two photographs or images with few overlapping areas are available, lack of camera calibration information and the measurements of specific control points, and consequently, the control of metric accuracy [22]. The most commonly cited problems associated with retrospective photogrammetry are as follows [23]:  
* Older photographs are more difficult to process due to archival quality, digitization issues, technical equipment, and film media;
* The use of analog photographs presents its own set of obstacles but allows the modeler to use much older data to achieve digital reconstructions of cultural heritage that people no longer have access to;
* The characteristics of scanned analog images with mostly unknown camera geometry;
* Missing or minimal object information and low radiometric and geometric resolution;
* The photographs are not created specifically for documentation purposes, so they often do not focus on the object being assessed. Nevertheless, the above examples of the use of photographs to reconstruct archaeological, paleontological, and cultural heritage sites are evidence that historical photogrammetry is becoming a powerful and common tool in the future, particularly as advances are made in photogrammetric techniques. Furthermore, modern software is able to compensate for the difficulties inherent in using old photographs. The application of the Structure from Motion method and implementation of algorithms for homology point detection, such as SIFT and RANSAC, enable the 3D reconstruction of objects at a high level of detail based on archival imagery [24].
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 4 Proposed M-LBS Scheme
### Cell Exit Protocol  
When a node leaves its current cell, it must execute a cell exit protocol to preserve data availability while minimizing redundancy. For example, when a user moves from cell 19 to cell 14 (Figure 2), the protocol ensures the efficient distribution of its cached data to the remaining nodes. Before describing the protocols, we establish their common elements:  
**Common Protocol Elements:**  
* _Goal:_ Maintain data availability while minimizing redundancy;
* _Participants:_ Departing node and remaining nodes (referred to as node \(N\));
* _Prerequisites_ List of current cell nodes from cell master;
* _Termination:_ The protocol ends when either all the data are distributed or all the nodes are contacted.  
We propose two protocols that prioritize different optimization goals:  
**Exit Protocol 1: Redundancy-Optimized**  
This protocol prioritizes minimizing data redundancy by checking for duplicate data before considering cache availability.  
1. _Data Inventory:_ Departing node sends its data list to node \(N\);
2. _Redundancy Check:_ Node \(N\) reports which data it already has;
3. _Duplicate_ Removal: Departing node removes redundant data from transfer list;
4. _Availability Check:_ Node \(N\) reports its available cache space;
5. _Data Transfer:_ Departing node sends non-redundant data within availability limits;
6. _Confirmation:_ Node \(N\) confirms successful data receipt.  
**Exit Protocol 2: Communication-Optimized**  
This protocol prioritizes minimizing message exchanges by checking cache availability before considering data redundancy.  
1. _Availability Check:_ Departing node requests node \(N\)'s cache space
2. _Space Verification:_ Node \(N\) reports available cache space
3. _Data Inventory:_ If space available, departing node sends its data list
4. _Redundancy Check:_ Node \(N\) reports which data it already has
5. _Data Transfer:_ Departing node sends non-redundant data within availability limits
6. _Confirmation:_ Node \(N\) confirms successful data receipt  
Protocols differ primarily in their optimization priorities. Protocol 1 ensures minimal redundancy but may require more messages when cache space is limited. Protocol 2 minimizes communication overhead but may result in greater redundancy when cache space is scarce.  
Before executing any of these cell exit protocols, the departing node must verify whether it is the current _cell master_ of the cell it leaves. If this is the case, the departing node sends a message to all nodes in the cell indicating that they must elect a new _cell master_. Each node interested in becoming a new _cell master_ waits a time proportional to its distance from the center of the cell. The first node that exhausts its time informs the others that it will be the new _cell master_ and receives all the information collected about cell statistics and the historical query frequency table for all cells.
## 4 Contribution
### Construction of Low-Altitude SULA Model of Urban Environment  
In the low-altitude environment, the distribution density of complex scatterers decreases as the flying height of the UAV increases. The influence of shadow fading on the channel becomes smaller. At this time, the Doppler effect caused by the relative movement of the UAV and the ground base station plays a major role in the signal fading. The rate of change of time-varying signal envelope is proportional to Doppler spread spectrum. Large Doppler shift causes rapid changes in the time-varying signal envelope and distorts the signal. In the small-scale flat fading channel described by the Clarke channel model, the received signals of mobile stations have the statistical characteristics of the field intensity based on the scattering mode. This is in line with the characteristics of low-altitude urban mobile communication environment where there is no direct path. The Jakes' model fits well with the statistical characteristics of the Clarke model, so it is optimized based on the Jakes' model and the beamforming technology is adopted to achieve the compensation of the Doppler frequency shift to improve the communication reliability.
# FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication
## 3 Preliminaries
### Physical Unclonable Functions  
A PUF refers to a digital circuit that maps an input, referred to as a challenge, to an output (a response) in an implementation-dependent manner [44; 45]. The fundamental basis behind the PUF design lies in the existence of minor discrepancies in microelectronic circuits that arise due to manufacturing imperfections [10]. These imperfections are deemed insignificant and do not impact the operation and characteristics of integrated circuits. PUF circuits are constructed to leverage these variations to produce a unique hardware-driven fingerprint, thus creating a distinct mapping from an input bit-string challenge to an output bit PUF response [10].  
These features have made the use of PUFs a viable means for generating a unique signature that provides an additional layer of security and authenticity in various applications.  
To illustrate, Figure 1 shows the design of an arbiter-PUF, a notable PUF design that leverages the variation in propagation delays. Due to the non-uniformity in delays among integrated circuits, the latched value for the same challenge bits will differ, even with the same circuit implementation. This variation reflects a random effect during device manufacturing, making physical cloning of a PUF infeasible, even with knowledge of all implementation parameters [46]. In fact, the CRPs of a PUF cannot be predicted or controlled by the manufacturer. An invasive attack on a device would result in changes in the signal delays, rendering PUFs tamper-resistant and one-way mapped. In summary, the responses received from different ICs for the same PUF are unique, serving as a fingerprint for individual ICs. A PUF can be defined mathematically as follows [47]:  
\[f:C\to R. \tag{1}\] \[f(C):r(c\in C,r\in R). \tag{2}\]  
where the output or response generated by a PUF is denoted by \(R\), while the PUF challenge is represented by \(C\).
# Analysis of Small-Loop Electromagnetic Signals to Detect Subsurface Anomaly Zones
## 4 Detection of Anomaly Zones by Visual Observation
### Areas of Cavities  
Figures 6a and 7a show the 2D ER contour maps of Sections 3 and 4, where the polystyrene hemispheres were installed at 4 and 1 m, respectively, with each buried at a depth of 0.45 m. As shown in the maps, the anomaly zones that exhibited a significantly large ER revealed the locations of the polystyrene hemispheres and could be detected on the contour maps by visual observation. Figures 6b and 7b show the graphs of the ER in each section measured according to the depth of each position. The tops of the cavities were also identified via the increase in the ER at 4 and 1 m and at a depth of 0.26 m for Sections 3 and 4, where the tops of the polystyrene hemispheres were located.  
Figure 6: Test result for cavity (Section No. 3). (**a**) Distribution of the ER. (**b**) ER according to the position at each depth.  
[PERSON] and [PERSON] [19] reported that the receiving rate of the transmitted EM wave could decrease in cases where the scale of the nonconductive object was large in the direction of the wave propagation. Therefore, it was estimated that the receiving rate of the transmitted EM wave would decrease when it penetrated the 0.5 m radius of the polystyrene hemispheres, as they were relatively large compared to the capacity of the equipment used in this study. It was also estimated that the higher ER that appeared only at the top of the object was caused by the current channeled at top of the polystyrene hemisphere. Thus, the ER in the lower part of the polystyrene hemispheres was lower than at the top of the polystyrene hemispheres and appeared to be similar to that of the surrounding ground.  
Figure 7: Test result for cavity (Section No. 4). (**a**) Distribution of the ER. (**b**) ER according to the position at each depth.
## 2 System Model and Assumptions
### Channel Model  
In the environment of indoor VLC systems, line-of-sight (LOS) links constitute a significant portion of the signal power [25]. For simplicity's sake, this paper primarily focuses on discussing channel conditions within LOS links. We use \(\mathbf{g}_{k}\in\mathcal{R}^{N\times 1}\), \(\forall k\) to denote the channel vector between the LED arrays and \(\text{User}_{k}\), denoted as \(\mathbf{g}_{k}=[g_{1,k},\cdots,g_{N,k}]^{T},\forall k\). The channel gain \(g_{i,k}\) from the \(i\)-th LED to the \(k\)-th user can be obtained using the Lambertian model [26], defined as follows  
\[g_{i,k}=\left\{\begin{array}{cl}\frac{(\kappa+1)A_{\rm r}}{2\pi I_{\rm r}^{ 2}}T(\psi_{i,k})G(\psi_{i,k})\cos^{\kappa}(\varphi_{i,k})\cos(\psi_{i,k}),& \text{if }\big{|}\psi_{i,k}\big{|}\leq\psi_{\rm FoV};\\ 0,&\text{if }\big{|}\psi_{i,k}\big{|}>\psi_{\rm FoV},\end{array}\right. \tag{3}\]  
where \(A_{\rm r}\) represents the receiving area of the PD; \(I_{i,k}\) is the straight-line distance from the \(i\)-th LED to \(\text{User}_{k}\); \(\varphi_{i,k}\), \(\psi_{i,k}\), and \(\psi_{\rm FoV}\) denote the emission angle starting from the transmitter axis, the reception angle starting from the receiver axis, and the field-of-view (FOV) angle of the PD, respectively; \(T(\psi_{i,k})\) refers to the gain of the optical filter; and \(\kappa=-\frac{\ln 2}{\ln(\cos(\varphi_{1/2}))}\) denotes the Lambertian radiation order, which is determined by the half-irradiance semi-angle \(\varphi_{1/2}\). \(G(\psi_{i,k})\) represents the light concentrator's gain and is denoted by:  
\[G(\psi_{i,k})=\left\{\begin{array}{cl}\frac{o^{2}}{\sin^{2}(\varphi_{\rm FoV })},&\text{if}\quad\big{|}\psi_{i,k}\big{|}\leq\psi_{\rm FoV};\\ 0,&\text{if}\quad\big{|}\psi_{i,k}\big{|}>\psi_{\rm FoV},\end{array}\right. \tag{4}\]  
where \(o\) is the reflective index.  
In this paper, we assume that the transmitter has perfect channel state information (CSI) for all users. This assumption is deemed reasonable in VLC systems, as users' positions and channel parameters can be reliably obtained. [PERSON] et al. [27] proposed a receiver localization algorithm to acquire receiver position information. In scenarios where environmental factors such as temperature, atmospheric turbulence, lighting, reflections, shadowing, and other factors impact the communication channel and the background radiation, relying solely on user location information may introduce biases in the CSI. However, both the intensity of background radiation and the channel parameters can be determined through pilot-based measurements or other effective channel estimations. These techniques can help mitigate the effects of environmental factors and provide accurate CSI and the intensity of background radiation. Regarding channel parameter estimation,various algorithms, such as least squares [28], statistical Bayesian MMSE [29], and neural networks [30], can be utilized to estimate channel interference and provide feedback to the transmitter.