esa-sceva/satcom-chunk-collection huggingface dataset:
# MFCNet: Mining Features Context Network for RGB-IR Person Re-Identification
## 4 Results
### Experimental Settings  
The experimental data in this paper are from open-source datasets: SYSU-MM01 and RegDB. For both RegDB and SYSU-MM01, we use a standard cumulated matching characteristics (CMC) curve and mean average precision (mAP) to evaluate our algorithm. Specifically, we adopt the cumulative Matching Characteristics (CMC) at rank-1, rank-10, and rank-20. The rank-k calculates the percentage of testing samples that find the correct result in the top k search outcomes of the query sample. The metric mAP is an average of the maximum recalls for each class in multiple types of tests.  
**SYSU-MM01.** It is a large-scale benchmark data set [9] used to solve RGB-IR Re-ID problems. The dataset contains a total of 303,420 images of 491 pedestrians taken by six cameras (four visible light and two infrared). Among them, the visible-light cameras captured 287,628 pedestrian RGB images, and the infrared cameras took 15,792 pedestrian IR images. The images taken include both outdoor and indoor shots. Indoor photographs were taken by one infrared and two visible-light cameras, and outdoor photographs were taken by the remaining cameras. The training set includes 395 pedestrians in 22,258 RGB images and 11,909 IR images. The test set contains 96 pedestrians; 3803 IR images are used for querying, and 301 selected RGB images are selected as the gallery set.  
**RegDB.** It is a relatively small data set captured by a dual-camera system consisting of visible-light and infrared cameras. The dataset contains 8240 images of 412 pedestrians. Each pedestrian is included in 10 visible-light images and 10 far-infrared images.  
For fair comparison with other methods, the data set used in this paper was randomly divided into two halves: one half was used for training, and the other half was used for testing. In the test, the RGB images were used as the query set, and the IR images were used as the gallery set.
## 1 Introduction
Estimating the GLIM system's active LED indexes is crucial for detecting the transmitted signal; this step has usually been performed by the ZF, MMSE, and MAP detectors in previous studies [18; 19]. Although an ML-based detector is performance-optimal but impractical, a DNN-based solution is complexity-efficient and still useful for performance enhancement. Briefly, the key contributions of the proposed method are as follows:  
* An IE-based DNN (IE-DNN) is introduced into the GLIM (multiple-input multiple-output) MIMO-VLC demodulator;
* Only a single IE-DNN module without the need to change the transmitter and the signal demodulation part in the receiver structure;
* Three different structures of the IE-DNN are proposed and compared to demonstrate that the CNN-based estimator delivers the best performance;
* In comparison with conventional detectors, a remarkable active LED index estimation accuracy significantly improves the BER performance at acceptable complexity costs. This paper is organized as the following. In Section 2, we describe the system model of the GLIM with the mention of conventional detection techniques. In Section 3, we present the proposed DL-based index estimator include different network structures, the sample generation, and training specification stages. In Section 4, we provide the simulation results to demonstrate the advantage of the proposed index estimator in comparison with the previous ones. Finally, we provide the concluding remark in Section 5.
## 1 Introduction
In this paper, a state estimation problem is considered for a target tracking scheme in wireless communication environments. Firstly, an alternative FMS filtering and smoothing algorithm is derived for both cases with delay and without delay between the original state and the estimated state. This algorithm is thus called the unified algorithm of FMS filter and smoother. As shown in the terminology _unified_ which can also mean _united_, _combined_, _integrated_, both FMS filter and smoother are derived by solving one optimization problem directly with incorporation of the unbiasedness constraint using only finite measurements and inputs on the most recent window \([i-M,i]\). Thus, the proposed unified algorithm provides simultaneously state estimates at the current time \(i\) as well as at the lagged time \(i-d\), given finite measurements and inputs on the most recent window \([i-M,i]\) where \(i-M\) is the window initial time and \(i\) is the current time. The proposed unified algorithm of FMS filtering and smoothing shows that there are some unique properties such as unbiasedness, deadbeat, time-invariance and intrinsic robustness while the recursive IMS filter such as Kalman filter does not have these properties. The on-line computational complexity of the proposed unified algorithm is discussed and compared with the IMS filter. Secondly, a target tracking in wireless network environments is considered as an application of the proposed unified algorithm of FMS filtering and smoothing. Through extensive computer simulations for moving target's accelerations of various shapes, the proposed unified algorithm-based target tracking scheme is shown to provide estimates for position as well as acceleration of moving target in real time, while eliminating unwanted noise effects and maintaining desired moving positions. In particular, due to intrinsic robustness and deadbeat properties, the performance of the proposed unified algorithm-based scheme can be shown to be better than the existing IMS filtering-based scheme for suddenly changing acceleration. This paper has the following structure. In Section 2, a unified algorithm of FMS filtering and smoothing is proposed. In Section 3, a target tracking scheme using the proposed unified algorithm is considered via extensive computer simulations. Then, concluding remarks are given in Section 4.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 6 HWLNets in IoT Applications
### Summary  
Hybrid networks can provide better services in terms of positioning and network security than standalone networks. For instance, for slow moving users, LiFi can provide better positioning accuracy but if the users are moving faster, the channel degrades rapidly and WiFi should be used instead. Security concerns on the physical layer are mitigated while using VLC spectrum because of its natural properties but to a certain extent because light is susceptible to connectivity issues due to numerous activities. For a secure and stable network, one must employ both spectrums to solve current issues such as using quantum key distribution to encrypt data at low rates, then send it through WiFi for fast moving users or LiFi for slow ones. A same network could use radio frequency for locating users with access rights to a network, then if they are near LiFi AP, they could be precisely pinpointed. Possibilities for these applications in HWLNets are numerous and have not been fully researched.
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 1 Introduction
MEC can improve latency and bandwidth utilization while making users, developers, and content providers access network services more easily on the basis of its realization technologies including SDN and network slicing [15; 16; 17]. However, there is an arising issue that the cloud resources at the MEC on-site (access point) and edge site are restricted and insufficient primarily because of the operation and management constraints (e.g., limited space and capacity) in the case of on-demand dynamic computing and storage resource deployment in cloud-assisted mobile and multi-access edge computing by end-user requests in particular [18; 19; 20]. Resource provisioning in the MEC environment generally embraces hypervisor-based and container-based virtualization technologies. Furthermore, as the edge or distributed cloud is increasingly prevailing, it is desired that lightweight and container-based service resources are converged with an automated and intelligent networking environment. Therefore, a resource integration and orchestration technology is required to select and allocate computing, storage, and network resources efficiently over the SDNized and containerized edge cloud infrastructure in order for dynamic resource management; thus, MEC services can be enhanced with the improved quality of experience and service in a more intelligent and secure way. In this paper, we propose a dynamic and flexible network virtualization and orchestration scheme for automated and selective MEC services over a wide-area SDN (KREONET-S). Section 2 introduces how KREONET-S operates as a wide-area SDN infrastructure that meets the MEC-specific dynamic resource provisioning requirements. Section 3 describes the development of VDN and VDNO applications that can be utilized for selective MEC services by using a location- and load-aware approach. Consequently, as a use case of network automation based on dynamic virtual network slicing over KREONET-S, an experiment using Globus Online [21] is explained in association with the virtual networking (VDN) and orchestration (VDNO) technologies in Section 4. Finally, we conclude this article in Section 6 after considering a multitier service architecture and selective MEC model for the scheme proposed in Section 5.
# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 2 Literature Review, Theoretical Analysis, and Research Hypotheses
### Studies Focused on Employment Quality and AI in China  
Recent research on AI and employment in China has primarily focused on the manufacturing and service sectors, highlighting both positive and negative effects. In the manufacturing sector, the integration of AI and industrial robotics has been shown to increase productivity and create high-skilled jobs, but it has also led to the displacement of low-skilled workers [15; 16]. In the service sector, AI adoption has been associated with job growth and income optimization, but it has also intensified competition for medium-skilled workers [17; 18].  
Despite these insights, there is limited empirical evidence on how AI adoption affects employment quality across different regions in China. This study seeks to fill this gap by examining the regional disparities in the impact of AI on employment quality, with a focus on the role of industrial structure, economic development, and policy interventions.  
High-quality employment includes factors such as workplace conditions, remuneration, social security, and organizational frameworks. Although a definitive consensus on its definition is lacking, it is frequently examined at two levels: the macro-level, which encompasses the equity of employment prospects and working conditions, and the micro-level, which emphasizes job security, remuneration, and the work environment [6; 19; 20].  
This study analyzes the influence of AI on employment quality across three dimensions: productivity improvement, alterations in job structure, and income redistribution. The implementation of AI enhances productivity by refining organizational frameworks, optimizing workforce allocation, and providing employees with resources for experiential learning. Nonetheless, technology also causes job displacement in certain areas while generating demand for highly skilled labor, leading to skill polarization and an excess of labor in conventional industries [20; 21; 22]. The balance between AI's job creation and substitution effects is contingent upon policy interventions and workforce adaptation.
## 2 Materials and Methods
### Long Short-Term Memory (LSTM) and Bidirectional LSTMs  
LSTM is a specific RNN architecture used to prevent gradient vanishing and exploding problems. LSTMs can acquire long-term dependencies by a gate mechanism that can control the flow of information. These gates can realize which data in the sequence are important to preserve or forget. There are three different gates in the LSTM cell; input, forget, and output (Figure 1).  
The following equations are the LSTM cell states and parameters' updating scheme [12; 20; 32]:  
\[f_{t}=\delta\Big{(}W_{f}[h_{t-1}*x_{t}]+b_{f}\Big{)}, \tag{10}\]  
\[i_{t}=\delta(W_{i}[h_{t-1}*x_{t}]+b_{i}), \tag{11}\]  
\[\dot{\zeta}_{t}=tanh\;(W_{c}[h_{t-1}*x_{t}]+b_{c}), \tag{12}\]  
\[C_{t}=i_{t}*\dot{\zeta}_{t}+f_{t}*C_{t-1}, \tag{13}\]  
\[O_{t}=\delta(W_{o}[h_{t-1}*x_{t}]+b_{o}, \tag{14}\]  
\[h_{t}=o_{t}*tanh(c_{t}), \tag{15}\]  
where \(i_{t},f_{t},\;\) and \(O_{t}\) represent the activation of input, forget, and output gate at time step \(t\), respectively, \(H_{t-1}\) is the output at time step \(t-1\), \(x_{t}\) is the input at the present moment, and \(C_{t-1}\) is the memory cell from the previous state. The forget gate (\(f_{i}\)) can input the information in \(h_{t-1}\) and \(x_{t}\), and outputs a vector, with values ranging from 0 to 1 for the cell state \(C_{t-1}\), 1 means completely reserved and 0 means completely discarded. The input gate \(i_{t}\;\) decides what new information is going to be stored in the cell state, and the first part is  
Figure 1: The architecture of LSTM cell. Numbered values correspond to the equation numbers below.  
the sigmoid layer called the \"input gate layer\" that decides which values will be updated. The second part is that the \(tanh\) layer creates a vector of a new candidate value, \(\hat{\mathbf{C}}_{t}\) which is added to the state. The current block memory \(C_{t}\) is created by the accumulation of the items from the previous block and input gate. Finally, the output gate (\(O_{t}\)) determines the cell state value, where \(W\) and \(b\) are weight and bias. The following equations are sigmoid and hyperbolic tangent functions  
\[\delta(x)=1/\left(1+e^{-x^{\prime}}\right), \tag{16}\]  
\[tanh(x)=\left(e^{x}-e^{-x}\right)/\left(e^{x}+e^{-x}\right). \tag{17}\]  
The idea of BiLSTM is to combine input information in the past and future time step in LSTM models. In BiLSTM, information can be preserved from both the past and future at any point in time [33].
# Proxy-Based Semi-Supervised Cross-Modal Hashing
## 4 Experiments
### Training and Encoding Time  
To investigate the efficiency of the PSSCH method, we compared the training time and encoding time with baseline methods on the MIRFlickr-25K dataset with 64 bits. For the training time, we analyzed the time complexity of the PSSCH algorithm. The time complexity of the feature proxy loss \(\mathcal{L}_{f}\) is \(O(N_{l}C)\), where \(C\) represents the number of categories, while the time complexity of the Adaptive Dual-Label Loss is \(O\big{(}NC+N_{l}^{2}\big{)}\), where \(N_{l}\) denotes the number of labeled samples. Thus, the overall time complexity of PSSCH is \(O\big{(}NC+N_{l}^{2}+N_{l}C\big{)}\), where \(N\) represents the total number of training samples. As shown in Figure 5a, our training time is slightly higher than other methods, but the training process is conducted offline, so the training time does not affect retrieval performance. In the field of cross-modal hashing retrieval, more attention is often paid to inference time, which mainly consists of encoding time and Hamming distance computation time. The encoding time, as shown in Figure 5b, is in the millisecond range for all methods, demonstrating that the PSSCH method has a reasonable encoding time.  
Figure 4: Sensitivity of parameters \(\alpha\) and \(\beta\) on MIRFLICKR-25K, NUS-WIDE, and MS COCO datasets. The code length is 64.
## 2 Materials and Methods  
### UAV-Based Laser Scanning System  
The UAV-based laser scanning system used for all experiments within this work is shown in flight and on the ground in Figure 2a,b. The platform is based on the DJI Matrice 600 Pro [38] and the lightweight RIEGL miniVUX-SYS designed for the use on UAVs [39]. This system consists of the IMU/GNSS combination Trimble APX-20 UAV [40] for trajectory estimation in post-processing and the full waveform 2D laser scanner RIEGL miniVUX-2 UAV for object acquisition.  
The scanning mechanism is based on a rotating mirror resulting in a field of view of up to 360\({}^{\circ}\) and a maximum laser pulse repetition rate of 200 kHz. Furthermore, the laser scanner's absolute accuracy at 50 m distance is given with 1.5 cm and the precision with 1.0 cm [41]. To ensure a precise trajectory estimation and the calculation of the 3D point cloud, the sensor calibration between IMU, GNSS antenna and laser scanner is accurately estimated by the manufacturer RIEGL. Referring to the manufacturer's specifications in Table 2, it is apparent that also for this UAV-based laser scanning system, the major influence on the accuracy of the estimated point cloud comes from the accuracy of the trajectory.  
Figure 2: DJI Matrice 600 platform with the RIEGL miniVUX-2 UAV laser scanner in flight (**a**) and on the ground (**b**).  
The stated accuracy values are typical values that can be expected for trajectory estimation with this platform given short baseline GNSS processing and a sufficient satellite constellation. The sensor and system specifications listed so far are given without trajectory improvements, which are typically provided by strip adjustment approaches or GCPs [30]. To assess the quality of the point cloud in the following, the assumed accuracy of the final product should be within the range of the specified trajectory accuracy.
# Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF
## 3 Results and Discussion
### Measurement and Prediction of Vertical Wind Profiles
Figure 9: Vertical profiles of horizontal velocity for wind blowing from the land and that from the sea (for May 2016). Horizontal dashed lines indicate the hub height level, and the dark profiles indicate the percentage error in the Weather Research and Forecasting (WRF) profiles compared to the LiDAR measurement. Figure 11 shows the comparison of simulated and measured hourly averaged boundary layer profiles, which are further averaged over the period of one month. The comparisons show that during the morning hours the difference between the simulations and the measurements is significant, with simulations underestimating the wind speed in the lower region of the boundary layer (below 300 m) and overestimating it in the higher region (above 300 m). The largest error of almost 20% can be observed at the height of 140 m. On the other hand, during the day time, the agreement of the simulations with the measurements is significantly high. Therefore, although the long-term average data from mesoscale simulations are reliable, short-term statistics--like hourly averaged data--should be used with caution.
# Laser Scanning versus Wide-Field--Choosing the Appropriate Microscope in Life Sciences
## 3 Laser Scanning Microscopy
A further advantage of multiphoton microscopy is the higher penetration depth of longer wavelengths. However, the longer wavelengths also cause larger diffraction-limited spots, and the technical equipment is more complex and needs more precise adjustment than conventional CLSM. Stimulated Emission Depletion (STED) microscopy is a laser scanning technique that overcomes the diffraction-limited resolution of confocal and multiphoton microscopes. The enhancement of resolution is due to suppression of fluorescence of the dye molecules in the outer regions of a diffraction-limited illumination spot by stimulated emission using a (second) donut-shaped laser beam. While a resolution of 30-70 nm can thus be achieved [33], the irradiance, however, exceeds the irradiance in a conventional fluorescence microscope by a factor 104-105. This problem was minimized with the introduction of MINFLUX nanoscopy, a technique based on the localization and tracking of single molecules in the intensity minimum of a donut-shaped laser beam. MINFLUX achieves nanometer resolution (isotropic: \(\geq\)2 nm) with moderate light exposure, which is comparable to confocal laser scanning microscopy [34].
## 1 Introduction
[15] proposed an interpolation-based scheme that selects different functions to compute interpolation values based on secret data. In scenarios where privacy and sensitive content must be protected, reversible data hiding for encrypted images (RDHEI) [16; 18; 19; 20; 21; 22; 23] securely embeds data within encrypted images, allowing secure storage and transmission. Unauthorized viewers cannot access hidden data without proper decryption, achieving two-layer protection through encryption and data hiding. [PERSON] et al. [16] used the Arnold matrix to encrypt cover images, embedding secret data into a new image created by an enhanced encrypted image interpolation algorithm. [PERSON] et al. [19] developed an RDHEI model with multiple data-hiders using secret sharing, where the original image is split into encrypted parts and shared among data-hiders; the original image can be restored by collecting enough marked images. Since finding extra hiding space in encrypted images is challenging due to maximum entropy, some methods [22; 23] use interpolation to determine whether a pixel is viable for embedding. In this paper, we propose a high-capacity reversible data-hiding method for encrypted images that enhances privacy protection by employing the Fisher-Yates shuffle algorithm [24] to randomly shuffle pixel sequences. A chaotic sequence diffuses pixels to encrypt the cover image, and interpolation creates space for embedding. In our separate-key strategy, only a receiver with both an encryption key and a data-hiding key can extract secret data and recover the cover image, while a receiver with only the encryption key can only recover the cover image. The method aims to maximize hiding capacity while preserving image integrity and confidentiality. To make this paper self-contained, Section 2 elaborates on the proposed scheme in detail. Section 3 presents experimental validation, including encryption analysis and performance comparisons with existing reversible data hiding methods. Finally, Section 4 concludes the paper.
## 2 Optimization Methods
### Global Optimization Method
Therefore, the multi-objective genetic algorithm was used to search for the optimum solutions that do not dominate (i.e., a set of optimum solutions in both DCR and SR objective functions) each other. The two objectives of the joint inversion of DCR and SR data using the multi-objective global optimization method can be represented similar to Equation (1) as:  
\[\text{GA}(\mathbf{m}_{dc})=\|\mathbf{W}_{dc}(\mathbf{d}_{dc}-\mathbf{f}_{dc}(\mathbf{m}_{dc}))\|^{2} \tag{10}\]  
and  
\[\text{GA}(\mathbf{m}_{sr})=\|\mathbf{W}_{sr}(\mathbf{d}_{sr}-\mathbf{f}_{sr}(\mathbf{m}_{sr}))\|^{2} \tag{11}\]  
The objective functions are used to map the decision variables (model parameters) into an objective space where we delineate solutions that are not dominated or perform optimally in both objectives. These sets of solutions (Pareto sets) align in a pattern known as Pareto optimal solutions or the Pareto front [44]. Basically, the term \"Pareto optimality\" is used to describe these sets of solutions, implying that no further optimal solution in both objectives can be obtained. Details about the concept of Pareto optimality and non-domination is discussed by [12]. The NSGA II algorithm sorts the set of solutions as they arrive at the Pareto optimal front. This technique was proposed by [45] to overcome some of the limitations observed in some previous evolutionary multi-objective algorithms. These limitations include computational intricacy, elitism problems, and parameter sharing specifications. The NSGA II only utilizes standard parameters of the genetic algorithm needs, and no extra parameters are needed for its multi-objective base optimization (see the appendix section for the concept of NSGA II). Figure 1: Illustration of the non-dominated sorting algorithm 2 (NSGA II). \(Qt\) is the offspring population; \(Pt\) is the initial population; \(Rt\) is the total population; and \(F_{1}\), \(F_{2}\), and \(F_{3}\) are the levels of non-domination solutions.