esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Empirical Research on New Heterogeneous Traffic Control Mechanisms
### Exploration of Control Mechanisms for Future Traffic Environments
#### 6.3.2 Regional- and Network-Level Collaborative Control  
Beyond single intersection control, regional- and network-level collaborative control is an important development direction for future transportation systems. The research by [PERSON] et al. not only considered trajectory optimization at single intersections but also extended the scope to overall traffic management within dedicated autonomous vehicle zones [72]. By defining different fairness constraints in the TTMSE model, the research achieved a balance between two different operational objectives: system optimum and user equilibrium, providing a theoretical framework for regional-level collaborative control.  
The large-scale testing and simulation conducted by [PERSON] et al. extended the assessment of network-level benefits [71]. The research showed that when CACC technology and intelligent intersection technology are applied to provincial-level road networks, they can bring a 12% reduction in total delay, validating the cumulative benefits of vehicle-infrastructure collaboration in network-level applications. This cross-intersection, cross-regional collaboration is an important feature of future traffic control systems, integrating single-point optimization into overall network optimization.  
Synthesizing these exploratory studies oriented toward future traffic environments, we can summarize key trends: (1) expansion from single-point control to regional and network control; (2) evolution from single-objective optimization to multi-objective balance; (3) transformation from centralized control to distributed, self-organizing control; (4) development from passively adapting to traffic demand to actively guiding traffic behavior. These trends indicate that traffic control systems will undergo fundamental changes with the popularization of CAV technology, forming more intelligent, efficient, and sustainable future transportation systems.
# Data-Driven Redundant Transform Based on Parseval Frames
## 4 Image Denoising  
We introduce a novel problem formulation for signal denoising by applying the data-driven redundant transform DRTPF. Image denoising aims to reconstruct a high-quality image \(\mathcal{I}\) from its noise corrupted version \(\mathcal{L}\), which is formulated as \(\mathcal{L}=\mathcal{I}+\mathbf{n}\) where \(\mathbf{n}\) is a noisy signal. For a signal satisfying the DRTPF, the denoising model based on DRTPF is formulated as \[\{\hat{\mathcal{I}},\hat{\mathbf{Y}},\lambda\}=\min_{\mathcal{I},\{\mathbf{y}_{i} \}_{i=1}^{N},\lambda}\|\mathcal{L}-\mathcal{I}\|_{F}^{2}+\gamma\sum_{i}\| \mathbf{R}_{i}\mathcal{I}-\mathbf{\Phi}\mathbf{y}_{i}\|_{F}^{2}+\gamma_{1}\sum _{i}\|\mathbf{y}_{i}-\mathcal{S}_{\lambda}(\mathbf{\Psi}^{T}\mathbf{R}_{i} \mathcal{I})\|_{F}^{2}+\gamma_{2}\sum_{i}\|\mathbf{y}_{i}\|_{0}, \tag{26}\]  
where \(\mathbf{R}_{i}\) is an operator that extracts the \(i\)th patch of the image \(\mathcal{I}\), \(\mathbf{y}_{i}\) is the \(i\)th column of \(\mathbf{Y}\), and \(\lambda\) denotes a vector \([\lambda_{1},\lambda_{2},\cdots,\lambda_{M}]\) with \(\lambda_{j}\) operating on the \(j\)th element of \(\mathbf{\Psi}^{T}\mathbf{R}_{i}\mathcal{I}\). On the right side of Equation (26), the first term is the global force, which demands proximity between the degraded image \(\mathcal{L}\) and its high-quality version \(\mathcal{I}\). The other terms are the local constraints, which ensure that every patch at location \(i\) satisfies the DRTPF. This formulation assumes that the noise image \(\mathcal{L}\) can be approximated by a noiseless image \(\hat{\mathcal{I}}\) whose patch extracted by \(\mathbf{R}_{i}\) can be sparsely represented by the given transforms \(\mathbf{\Phi}\) and \(\mathbf{\Psi}\).  
To solve Problem (26), we apply Algorithm 1 to obtain the sparse coefficients \(\mathbf{Y}\) and the threshold values \(\lambda\). We mainly state the iterative method to obtain \(\mathcal{I}\).  
Denote \(\mathbf{d}^{k}=\mathbf{\Psi}^{T}\mathbf{R}_{i}\mathcal{I}^{k-1}\). We set \(\mathbf{O}^{k}\) as an index set that satisfies \(|\mathbf{d}_{l}^{k}|\leq\lambda_{l},l\in\mathbf{O}^{k}\). Set \(\mathbf{u}^{k}\in\mathcal{R}^{M}\) as a vector with elements \(\mathbf{u}_{l}^{k}=\begin{cases}1&l\in\mathbf{O}^{k},\\ 0&\textit{otherwise}.\end{cases}\) Then, the non-convex and non-smooth thresholds can be removed, with the substitution \(\mathbf{y}_{i}-\mathcal{S}_{\lambda}(\mathbf{\Psi}^{T}\mathbf{R}_{i} \mathcal{I}^{k})\approx\mathbf{y}_{i}-\mathbf{\Psi}^{T}\mathbf{R}_{i} \mathcal{I}^{k}\odot\mathbf{u}^{k}\) Thus, in the \(k\)th step, the problem that needs to be solved can be expressed as  
\[\{\hat{\mathcal{I}}^{k}\}=\min_{\mathcal{I}^{k-1}
## 3 Results and Discussion
### Al-STi-B-C-Ce Phase Analysis  
#### 3.3.1 XRD Analysis  
Figure 10 exhibits the X-ray diffraction pattern of Al-5 Ti-B-C-Ce alloy. It indicated that the Al-Ti-B-C-Ce alloy included Al, Al\({}_{3}\)Ti, TiB\({}_{2}\), and TiC phases. No Ce-containing phase was detected in the X-ray diffraction of Al-5 Ti-B-C-Ce, due to the relatively low content of element Ce, which was only confirmed by energy spectrum analysis.
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
## 2 Background
### Related Literature
#### 2.2.1 Error-Correcting Codes and Fuzzy Extractor  
Error-correcting Codes (ECC) and Fuzzy Extractors have been proposed to correct errors in SRAM PUF [11; 12; 13; 14; 15]. Typically, these techniques create a helper data that is made public. The helper data is used to recover a key from the original data while minimizing the probability of error. This setup is shown in Figure 2. To derive a stable key of a certain length, one must start with a string that is longer in length, representing an overhead. In a fuzzy extractor, this overhead, along with processing overhead, increases rapidly with rising intrinsic bit error rate of SRAM PUFs. An SRAM PUF array with inherently low reliability requires more area and computation for error reduction using the above approaches. Hence, a reduction in error rate of the raw PUF cells would benefit in total area reduction. Apart from this overhead concern, such schemes should also ensure minimal information leakage from the helper data. These concerns have spurred an alternative line of research, which is described next.  
Figure 1: Traditional 6T SRAM Cell.
## 4 Conclusions  
In this study, the crude protein content, oil content, erucic acid content, and glucosinolate content of rapeseed were determined using NIR-HSI. The regional growth algorithm was used for selecting the ROI. Furthermore, the best processing method was MSC combined with the first derivative for crude protein content, oil content, erucic acid content, and glucosinolate content. KPLS was used for the model establishment. The results indicated that RMSEPs were 0.5514%, 0.5680%, 2.8113%, and 10.3209 umol/g and R\({}^{2}\)p were 0.9461, 0.9503, 0.9572, and 0.9335 for the crude protein, oil content, erucic acid content and glucosinolate content in rapeseed, respectively, which revealed great potential for detecting four quality parameters of rapeseed using NIR-HSI. Therefore, this method can provide technical support for guaranteeing rapeseed quality and safety.  
D.W.: investigation, methodology, writing--original draft. X.L.: investigation, validation. L.Y., F.M., W.Z. and J.J.: formal analysis. L.Z.: conceptualization, writing--review and editing, project administration, funding acquisition. P.L.: supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Project of China (2021 YFD1600101), the earmarked fund for the China Agriculture Research System (CARS-12), the Major Project of Hubei Hongshan Laboratory (2022 hszd002), Key Research and Development Projects of Hubei Province (2021 BBA227), and the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2021- OCRI).  
Not applicable.  
The data that support the findings of this study are available on request to the corresponding author.  
The authors declare no conflict of interests.
### Carbon Balance  
Figure 17 shows the carbon yields for different gasifier temperatures. On the left y-axis, the carbon yield in tars, in bed ash and in fly ash is shown. On the right y-axis, the carbon yield in the syngas main components (CO, CO\({}_{2}\), CH\({}_{4}\), C\({}_{x}\)H\({}_{y}\)) as well as the total sum of all measured carbon yields is depicted.  
The carbon yield in tars was calculated as sum of carbon in gravimetric tars as measured from gravimetric tar concentration (Figure 7) and its elemental analysis (Figure 10) as well as the carbon in benzene, toluene and xylene (ECN3 in Figure 7) taken from the GC analysis. This approach adds the light tars to the gravimetric tars to represent a total tar amount. The carbon yield in tars decreased with temperature, since the tars are thermally cracked at higher temperatures.  
The carbon yield in bed ash and fly ash decreased with increasing temperature since more char was gasified, e.g., by the water-gas reaction at higher temperatures due to faster reaction rates.  
Figure 17: Carbon balance: Yield of carbon in syngas, tar, bed ash, fly ash and total sum for different gasifier temperatures.  
The carbon yield in the syngas main components increased with temperature, since at higher temperature more char and tar is converted to gases such as CO, CO\({}_{2}\), and CH\({}_{4}\).  
The total carbon yield sum for all shown carbon points are close to 1 kg kg\({}^{-1}\), which means that almost the same amount of carbon was found in the products than introduced with the sewage sludge.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 7 Industry Implementations  
Major technology companies are increasingly adopting PIC technology to meet the performance, scalability, and energy-efficiency demands of Fog and Cloud computing infrastructures. Intel and Cisco have emerged as industry leaders in deploying silicon photonic transceivers. Intel's optical I/O chiplet integrates lasers, modulators, and photodetectors on a single silicon photonics platform, enabling up to 4 Tbps of low-latency, high-bandwidth communication between CPUs, accelerators, and memory nodes--which is essential for hyperscale Cloud environments [3]. Cisco, in partnership with Accelink, has introduced 1.6 Tbps OSFP-XD silicon photonic transceivers based on dense wavelength division multiplexing (DWDM), which significantly increase fiber capacity while maintaining compact, energy-efficient form factors [55]. These solutions are particularly valuable for AI-driven data center operations and latency-sensitive Fog computing scenarios. Intel is also pursuing co-packaged optics (CPO) to eliminate traditional electrical I/O bottlenecks, integrating PICs directly into switch ASICs to reduce energy per bit and improve signal integrity compared to short-reach high-density connections. Cisco's roadmap includes the expanded adoption of coherent PICs within metro and long-haul transport platforms, leveraging integrated DSPs and tunable lasers to increase link adaptability and spectral utilization [87]. Nokia and Fujitsu are also contributing to the commercialization of PIC technologies. Nokia's latest optical transport innovations include 800G coherent pluggable optics and
## 4 Discussion  
In this study, we first optimized the parameters of the revised EC-LUE model using Landsat NDVI, spatially matched to the flux tower footprints at 30 m spatial resolution, and MODIS NDVI with a fixed footprint of 3 km \(\times\) 3 km at 500 m spatial resolution. We then evaluated the performance of the model with our optimized parameters at different spatial scales. We found that matching the NDVI to the flux tower footprint significantly improved the GPP estimates at the 78 FLUXNET sites.
## 3 Overview of Physical Unclonable Functions
### PUF Implementation  
#### 3.2.1 Optical PUF  
An optical PUF is a physical one-way function (POWF) comprising translucent matter doped with light-dispersing particles. As the laser beam falls on the matter, a unique and random speckle pattern is generated, and this process is shown in Figure 4. For the case of optical PUFs, the challenge is the light source, and the response is a speckle pattern. As the process of light falling on the material and then generating a speckle pattern is an uncontrolled process, replicating such a pattern is quite difficult.
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
###### Abstract  
Due to the limited payload and power of drones, the computational overhead, storage overhead and communication overhead that can be used for secure communication are restricted, making it difficult to apply some complex but fairly secure authentication protocols on drones. In this paper, we propose a low-complexity protocol for storing identity information in a resource-unconstrained device that does not require the UAV to store the information, thereby enhancing the UAV's resistance to capture. The protocol in this paper mainly consists of quasi-cyclic low-density parity-check (QC-LDPC) codes, physical unclonable functions (PUFs) based on random-access memory (RAM), \"XOR\" operations, and hash computation. The protocol in this paper is an authentication architecture in which the drone is guided by the ground station to read its identity information, and the drone does not store any identity information in advance. The protocol is divided into two phases: 1. fuzzy authentication of fingerprint PUF and 2. uniqueness authentication accomplished while guiding the recovery of identity PUF. Recovering identity PUF in this paper, QC-LDPC is used as the error control module, and the optimization of bit-flip decoding significantly reduces the probability of decoding failure. After the comparative security analysis and comparative overhead analysis of this paper's protocol, it can be concluded that this paper's protocol can withstand common attacks (including attacks attempting to pass authentication, attacks attempting to interfere with authentication, and physical capture attacks), and the storage and communication overhead is small in the case of large time overhead.  
authentication; PUF; RAM; QC-LDPC Article  
1
# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 5 Results  
### Ablation Experiments' Results  
The detection performance changes caused by modifications in the network structure were evaluated through ablation experiments, and the results are presented in Table 3.  
In contrast to the YOLOv5s model, our experiments showed that using the SIoU loss function could achieve better location results and accuracy, for which the Precision increased from 49.7% to 51.9%, AP50 increased from 46.4% to 47.9%, and AP50:95 increased from 23.0% to 24.4%. Additionally, using only our proposed network PartitionNet to replace the backbone of the model led to a 38.2% reduction in the quantity of parameters and a 53.4% reduction in the FLOPs, while increasing the Precision, AP50, and AP50:95. With the introduction of the CBAM attention module and our proposed VH block, we obtained a 1.6% AP50 improvement and a 0.5% AP50:95 improvement with slightly increased Params and FLOPs compared to the model of the backbone of PartitionNet. Furthermore, using the NewPAN feature fusion structure and CSPCrossStage resulted in a 38.1% reduction in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **AP50(\%)** & **AP50:95(\%)** & **Params (M)** & **FLOPs (G)** \\ \hline YOLOv5s & 46.4 & 23.0 & 7.02 & 15.9 \\ baseline model & \multirow{2}{*}{47.9 (+1.5)} & \multirow{2}{*}{24.4 (+1.4)} & \multirow{2}{*}{7.02} & \multirow{2}{*}{15.9} \\ YOLOv5s & & & & \\ baseline model + & & & & \\ SIoU loss & & & & \\ \hline LP-YOLO + & \multirow{2}{*}{46.8 (+0.4)} & \multirow{2}{*}{24.6 (+1.6)} & \multirow{2}{*}{4.51} & \multirow{2}{*}{6.9} \\ PartitionNet (backbone) & & & & \\ +VH block and & & & & \\ attention & 48.4 (+1.6) & 25.1 (+0.5) & 4.53 & 7.2 \\ mechanism & \multirow{2}{*}{49.0 (+0.6)} & \multirow{2}{*}{25.5 (+0.4)} & \multirow{2}{*}{4.34} & \multirow{2}{*}{7.4} \\ +NewPAN & \multirow{2}{*}{49.0 (+0.6)} & \multirow{2}{*}{25.5 (+0.4)} & \multirow{2}{*}{4.34} & \multirow{2}{*}{7.4} \\ (+CSProssStage) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of LP-YOLO on landslide dataset val.  
the quantity of parameters compared to the YOLOv5s baseline model and a 4.2% reduction compared to the proposed model that used the previous PAN structure.  
In addition, using NewPAN in the proposed model could achieve a 0.6% improvement for AP50 and a 0.4% improvement for AP50:95.  
With the simultaneous improvement of the backbone, neck, and feature fusion structure and the introduction of the attention module, the proposed model achieved a reduction of 38.1% and 53.4% in the number of parameters and FLOPs, respectively, when compared to the baseline model. Furthermore, the model's accuracy was significantly improved, achieving an AP50 and AP50:95 of 49.0% and 25.5%, respectively.
# A Biologically-Inspired Power Control Algorithm for Energy-Efficient Cellular Networks
## 1 Introduction
In addition, the conventional TPC algorithms in cellular networks mostly require a centralized controller that gathers the necessary channel information from other nodes, calculates the appropriate power level or the target SINR value and informs each node of the determined parameters [15; 16; 17; 18; 19; 20; 21; 22; 23]. This induces a large overhead for exchanging control information among network entities and requires a high complexity to calculate the operational parameters. To solve this TPC problem, in this paper, we refer to the biological phenomenon known as _flocking behavior_, which is exhibited when a group (or flock) of birds are in flight together [27]. Flocks behave with very simple rules, even in complex and dynamically-changing environments, showing emergent behavior to achieve a common goal, maximizing the minimum velocity of birds in a group in a robust and efficient manner. With the understanding of the similarities between flocking behavior and TPC in a cellular network, we make use of the underlying principles of flocking behavior in our TPC algorithm. Specifically, the proposed TPC algorithm adopts a local rate-averaging algorithm inspired by the flocking behavior in order to control the transmission power of BSs. Just as the flocking algorithm operates in a simple and distributed manner and shows convergent phenomena, our proposed bio-inspired TPC algorithm operates with a low level of complexity without a centralized controller and shows that the transmit powers of the BSs uniformly converge and that the rates of the MSs converge to the same value that maximizes the minimum rate. This eventually leads to a decrease in the energy consumption of the BS while lowering the outage probability of the MS in the wireless cellular network with a dynamically-changing interference level. The remainder of this paper is organized as follows. In Section 2, we present the system model for the considered problem. In Section 3, we introduce the flocking model and discuss its properties as they are currently understood. In Section 4, we explain the details of the proposed TPC algorithm, and in Section 5, we analyze the proposed TPC algorithm in mathematical terms. In Section 6, we discuss the simulation results, and finally, we offer some conclusions in Section 7.
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 5 Experimental Setup
### Separation Network Configurations  
For the single-channel separation task with training data under anechoic condition, the SuDoRM-RF++ architecture, as documented in [13], serves as the foundational model for ESC-MASD-Net. Hyperparameters for SuDoRM-RF++ and ESC-MASD-Net are configured to mirror those yielding optimal performance as reported in [13]. Within the encoder and decoder modules, a kernel size (\(K_{\mathcal{E}}\)) of 21 and a basis count (\(C_{\mathcal{E}}\)) of 512 are applied to input mixtures sampled at 8 kHz. Each U-ConvBlock configuration entails an input channel count (C) of 128, four successive resampling operations, and an expanded channel count of 512. Subsampling operations reduce the temporal dimension by a factor of 2, while depth-wise separable convolutions feature a kernel length of 5 and a stride of 2. The separation network incorporates 4 U-Conv Blocks (\(B=4\)), aligning with the minimal configuration across diverse SuDoRM-RF versions.  
Regarding the multi-view attention block, parameters include a channel count (CM) of 512, four attention heads, attention dimensions of 256, and an attention dropout rate of 0.1.
## 7 Risk Analysis
#### 7.1.2. Denial of Service (DoS)  
1. **Ports and Connections**  
_Security risk_: A DoS attack on the ECU would result in the complete shutdown of the entire automatic crash notification system. This is because it would become impossible to receive information from sensors that detect accidents and to communicate with the components responsible for triggering the alarm.  
_Security measure_: By implementing a firewall and an intrusion detection system (IDS), it is possible to establish an effective defense against DoS attacks directed at the ECU within connected vehicles. The firewall filters incoming and outgoing traffic, allowing only legitimate communications while blocking suspicious or harmful ones. The IDS constantly monitors system activity to identify any unusual traffic patterns associated with a DoS attack. Upon detecting signs of overload or malicious traffic, the firewall can react promptly by blocking connections from malicious sources and initiating mitigation measures.
## 5 Results and Discussion
### Results from Consideration Propensity Model
#### 5.1.2 Segment-Wise Variations in Key Factors Influencing Consideration
For example, train consideration increases for all three segments if a train station is within a walkable distance of <1.5 km (the coefficient is 0.34). Besides this effect, an increase in access time and egress distance to the nearest train station also reduces consideration propensity significantly. Note that consideration probabilities differ across segments, as follows: medium personal mobility has the highest influence, followed by low, and then by high personal mobility segments, and this difference is not captured by the unsegmented model. Figure 7 indicates that the effect of access time to train stations is non-linear, characterized by two discrete drops along the continuous line occurring at approximately 7.5 min and 17.5 min, which are the most common first- and last-mile travel times by walking (5-10 min range represented by the mean) and bicycling (15-20 min range). Furthermore, the slopes at these drop points differ across the three segments, suggesting different sensitivities to access times. Some trip context and socio-economic variables had an influential role in consideration propensity. Among work characteristics, the metro train is considered more for longer commute distances. Age, education, and gender are also significant. For instance, younger and highly educated respondents were more likely to consider the train in the choice set. Females were less likely to consider metro than their male counterparts. Thus, these potential groups can be targeted with suitable incentives or interventions to effectively enhance train patronage.