esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis
### Key Space
#### 4.2.2 Key Size Issues  
The proposed key is a vector of \(N\times M\) integer values whose size increases as a function of the CGH resolution, which may result in keys of considerable size. One should consider the option of compressing this information using arithmetic encoding techniques [49] that guarantee to avoid loss of information in order to have a shorter key. Other options to consider involve the use of chaotic functions [18; 21] to rearrange the pixels of the CGH.
## 5 Digital Image Frame Relay Transmission (DIFRT)
### System Architecture  
The system can be conceptualized as the following major functional blocks as illustrated in Figure 3c. The whole system mainly consists of three components: the transmitter, the relay nodes, and the final receiver.  
* _Transmitter_: The MATLAB platform can compress and quantize the original image captured by the camera, and also convert the image data into the special data frame. The data frame is transmitted to the embedded system (STM32F407 ZGT) for processing. Through pulse width modulation (PWM) and data frame synthesis, this embedded system can modulate the data onto the LED (XHP-70) driver module in the form of binary bit data. Finally, the encoded binary bit information is transmitted in the form of visible light [32; 33; 34].
* _Relay node_: The relay nodes can receive and identify the data frame. After signal amplifying, the data frame is finally retransmitted to the next relay node.
* _Final receiver_: The photodetector (PDA10A2) firstly detects the square waves of visible light. After signal attenuation by the attenuator (KPAT2.5-90/IS-2N), the wave information is imported to the oscilloscope. Once the oscilloscope receives and saves a complete frame of image data, the data is transmitted to the MATLAB platform for offline demodulation, decompression, and reconstruction of the image.  
Figure 3: (**a**–**c**) Flow chart of digital image frame relay transmission (DIFRT), (**d**) setting up of DIFRT experiment, and (**e**,**f**) inner water tube.  
Figure 3a,b also illustrate the flow chart of data processing before transmitting and after receiving.  
Before the image transmission in the pipe, the image data should be processed by MATLAB and embedded system. Firstly, the image file captured by the camera is imported to MATLAB for image standardization. After this operation, the image can be converted to the standard grayscale image with a size of \(256\times 256\) pixels and a bit depth of 8 bits. Then this image is encoded and simplified by the DCT and quantization approach. Through data frame synthesis and modulation by the embedded system, the signal is sent out to the pipe channel by the transmitter. The processing after receiving can be roughly considered as the inverse method of the processing before transmitting. It includes demodulation, image data extraction, inverse quantization, and IDCT decompression. Figure 3d demonstrates the setting up of the experiment environment. This experiment will be described further in Section 6.3 and 6.4.
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Reference-Free Approach  
Anchor-free localization procedure does not require an anchor or only requires selecting some anchor nodes during the localization process [258]. In a mobile network over time, the estimation of node position is difficult without prior knowledge of node or pre-surveyed reference node (anchor node). The problem was addressed in [259] by using odometry data and UWB range measurements in a multidimensional scaling (MDS) framework. The MDS paradigm was exploited for both measurement data in this anchor-free indoor tracking system. The proposed system can estimate the node's path jointly with all others. This approach only needs a small number of assumptions and keeps the reference frame through time steps. Therefore, the authors extended this work to make available a real-time tracking system [260]. In this system, the results show that MDS-based approaches are better than the EKF method. The advantage of these approachesis the ability to evaluate the positions without pre-surveyed reference nodes. In [261], an anchor-free positioning system was presented based on single UWB measurements. Using a factorization-based method and nonlinear least squares (NLS) optimization estimated the position of the nodes. In addition, a semi-automatic method was used to obtain an initial estimation. Another reference-free localization is the footstep-tracker approach, which used accelerometers and gyroscopes in [262].
## 3 Results
### Input Data Intercomparison  
The input data used as forcing for UTOPIA simulations in experiments EXP1-3 come from the observations carried out in the three experimental vineyards mentioned above, and from the two databases, GLDAS2.0 and GLDAS2.1. While the observations refer to a specific environment and have been measured within the vineyards, with the sensors located above the vegetation, the data of GLDAS belong to a database of gridded data in which each grid point, at the latitudes of the three vineyards, refer to an area of about 225 km\({}^{2}\), in which the conformation of the territory is smoothed and thus the site characteristics are different. In addition, the two GLDAS databases have been created using different \"data\" sources, with a massive use of model outputs to accomplish for the irregular distribution of available observations. Differences in the data are thus expected. An inspection on Tables S1-S18, in the Supplementary Data section, confirm the expectations and allows for quantifying these differences. Moreover, differences are similar in the three sites.  
Regarding solar global radiation, for instance, we can see that GLDAS2.1 data are closer than GLDAS2.0 data to the observations, particularly concerning the range of the data distribution and the correlation. Differences of mean values between GLDAS2.1 and observations are about 5-15 W m\({}^{-2}\), and standard deviations are similar, while for GLDAS2.0 differences are 0-40 W m\({}^{-2}\), and standard deviations are much lower. Minima and maxima are often not well captured, thus, range is much lower for GLDAS2.0.  
Regarding precipitation, the two GLDAS databases show similar distributions and tend to exceed the observations, in terms of mean values, maxima, standard deviations, and ranges, however, they are well correlated with observations.  
Finally, air temperature shows some differences generally larger for GLDAS2.0 than GLDAS2.1 for each statistical parameter: mean values of GLDAS2.1 are lower than observations (0.5-2.5 \({}^{\circ}\)C) while those of GLDAS2.0 are higher (0.2-1.2 \({}^{\circ}\)C), ranges and standard deviations are slightly (quite) smaller than those of observations for GLDAS2.1 (GLDAS2.0), with minima higher for GLDAS2.0, and correlation coefficients are closer to 1 for GLDAS2.1.  
UTOPIA (University of TOrino land surface Process Interaction model in Atmosphere) Simulations Compared with Observations  
The experiments considered in this subsection are EXP1a, EXP2a, and EXP3a, compared with the observations (OBS) in the vineyards, if any. In order to consider the effect of having a different vegetation type, also experiments EXP1b, EXP2b, and EXP3b were considered. The comparison was carried out only in the days in which observations were sufficient, in number, to evaluate a daily mean, the number of data, thus, changes for each variable and is indicated in the Tables.
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 3 Uncertainty Estimation in Neural Networks -- Mathematics and Algorithm
### Mathematical Theory on Modeling Uncertainty
#### 3.1.1 Distinction of Model, Data, and Distributional Uncertainty  
Model uncertainty is inherent to the model and is caused by problems with the model structure or training process, such as insufficient model complexity, training duration, or dataset composition. Data uncertainty is inherent to the data and is caused by information loss in the measuring process, such as limited image resolution, motion blur, or labeling errors. Distributional uncertainty is intended to cover the concept of out-of-distribution (OOD) inputs.  
Even when a model is perfectly tuned for its task and the data is flawless (low model and data uncertainty), the predictions may still be unreliable if the model is applied to inputs it was not designed to handle (high distributional uncertainty). For example, if a person ReID model is provided with images of dogs, bikes, or text, unexpected results may be produced. Figure 1 illustrates the role of _distributional uncertainty_ in contrast to the other uncertainty types.  
In the case of high data uncertainty and low distributional uncertainty (Figure 1b), we know that we do not know which class is presented, whereas in the case of high distributional uncertainty (Figure 1c), we do not know whether we know and thus should not trust any seemingly certain prediction of category or level of data uncertainty we might  
Figure 1: Example for a Dirichlet distribution with three classes in three scenarios. Points on the simplex correspond to a parameter configuration \((p_{1},p_{2},p_{3})\) of the categorical distribution (\(p(i)=p_{i}\)). Yellow (blue) corresponds to a high (low) probability for the configuration. (**a**) The configuration \(p_{1}\approx 1,\ p_{2},p_{3}\approx 0\) is most likely. (**b**) The configuration \(p_{1}\approx p_{2}\approx p_{3}\) is most likely. (**c**) All configurations are equally likely. Image source: [PERSON] and [PERSON] [26], similar visualization used by [PERSON] et al. [7].  
get. One could say that distributional uncertainty arises due to the interaction between data and model, specifically due to a mismatch between them. In addition to model uncertainty and data uncertainty, we will also consider distributional uncertainty in this work.
## 4 Limitations of the Study  
While the presented methodology offers a comprehensive framework to evaluate the integration of thermal storage and curtailed electricity in CSP plants, several limitations inherent to the modeling assumptions and data availability must be acknowledged. These constraints affect the generalizability and applicability of the results and are detailed below.  
Firstly, the simulation results for DPS rely exclusively on experimental data and literature models, as there are currently no commercial-scale facilities in operation providing validated performance data. Consequently, all efficiencies for receivers, heat exchangers, and storage components in the DPS configuration are theoretical and may differ from actual long-term behavior under industrial conditions.  
Secondly, component efficiencies were treated as fixed values across all simulations, regardless of variations in temperature, part-load operation, or system aging. While this approach simplifies the modeling, it limits the ability to capture performance degradation, dynamic behavior, or off-design scenarios.  
Economic estimations--including CAPEX and O&M--were derived from literature sources and adjusted to current price levels using consumer price indices. However, given the novelty of some components, especially in Scenario IV, the associated costs remain indicative rather than definitive.  
Regarding operational strategy, the control algorithm prioritizes thermal optimization over cost minimization or grid-oriented dispatch. This idealized dispatch mode does not incorporate constraints such as maintenance downtime, partial load inefficiencies, or real-time grid interactions. Additionally, parasitic consumptions (e.g., for fluid transport, heliostat actuation, or anti-freezing systems) were only partially included in the energy balance, and other losses were assumed negligible.  
Finally, the modeling of curtailed electricity availability is based on historical REE data from 2016 to 2021, without considering regulatory evolution, market mechanisms, or dynamic pricing schemes that could affect future surplus patterns.  
Despite these limitations, the comparative approach remains valid for understanding the relative advantages of different storage media and integration strategies. However, future work should incorporate real operational data, dynamic component modeling, and techno-economic optimization to enhance the accuracy and applicability of the conclusions.
## 1 Introduction
In order to collect only important information, various data volumes, such as car camera images and driver movement, are immediately analyzed; risk is calculated, and avoidance behavior is supported or automated [5]. Software-defined network (SDN) technology refers to network structures and devices that allow distributed network equipment to be easily managed by software. This technology enables network operators to manage network resources in an active and automated manner. It is possible to reduce the management costs of the network and respond quickly to sudden situations. This technology flexibly controls networks through programs such as network virtualization and network function virtualization. For this reason, SDN separates the data plane and control plane of the network, providing a standardized interface between them, allowing network operators to program the control plane for different situations. Therefore, it is possible to control the communication function performed on the data side in various ways. The term SDN has spread throughout the world and is being researched by many institutions and companies. Under the SDN concept, various network control structures and implementations have been proposed. Although the number of cases where SDN is introduced into actual network systems is increasing, the environment in which SDN is applied is limited, and various types of SDNs have not become widespread. Among the security vulnerabilities of SDN technology, the privileged elevation attack causes a big problem in the control function by allowing the authority to be arbitrarily not authenticated. The information collected needs to be shared only by legitimate users, and the authority to access data needs to be controlled, depending on the situation. A login/password method is required to satisfy the demand for secure sharing and utilization by service users. Based on the attributes of these target service users, the authority to decrypt data is delegated to legitimate users, and a pull-in encryption method is required. In this paper, as a technical countermeasure for these problems, we study how to encrypt data so that only users with legitimate attributes can access them. Property delegation in a fog computing environment and Ciphertext Policy Attribute-Based Threshold Decryption (CP-ABTD), a retractable encryption method, can be applied to provide a monitoring service for vehicles. The structure of this paper is as follows: Section 2 is a study of the underlying technologies and threats for the proposed system. Section 3 explains the detailed operational processes of the system and the scenarios in which attribute-based delegation of authority and the retractable encryption method are applied. In Section 4, the proposed system is shown to be tamper-proof and publicly offered, demonstrating its security compared to existing attribute-based encryption-only or delegation-of-authority encryption algorithms. Finally, Section 5 offers conclusions [6; 7].
## 1 Introduction
[27] studied the group anchors' seismic performance using expansion anchor bolts and post-cut anchor bolts under flexural and shear conditions through experiments. The load-displacement curves of the specimens were basically the same under static and dynamic loads. The ultimate load of planting bars did not decrease significantly, but the ultimate displacement had a large increase. [PERSON] et al. [28] conducted high-cycle fatigue tests on 16-25 mm single-bar planting specimens, and found that the fatigue load weakened the bearing capacity of planting bars. The bond stress of planting bars showed a logarithmic development trend with an increase in loading times. Under the pull-out test, the peak bond stress gradually shifted to the ends of the planting bars with the increase in load. From the perspective of the actual engineering application of rebar planting technology, the existing research is mostly for medium- and large-diameter steel bars, which have a wider range of applications. However, in the face of concrete floor reinforcement, masonry structures, brick wall reinforcement and other practical projects, small-diameter planting bars also have a non-negligible use demand, and a further expansion of the research on the bonding and anchoring performances of small-diameter planting bars has important practical significance. In this paper, the basic mechanical performance parameters of 6 mm and 8 mm diameter single-bar rebar planting specimens under medium and low cycle fatigue loads were obtained through experiments. The load-slip curves of the rebar planting specimens were compared and analyzed, and we proposed the bond-slip constitutive relationship of the adhesive-rebar interface under medium and low cycle fatigue loads. The expression of the stiffness of the adhesive-rebar interface spring element based on ABAQUS was further derived. Through a large number of numerical simulation results, the elastic ultimate load calculation formulas of 6 mm and 8 mm single-bar rebar planting specimens with respect to the anchor depth and fatigue loading times as independent variables were established by fitting. The roadmap of the research is shown in Figure 1.
## 5 Building Resilience in Software-Defined IIoT-Edge Networks
### Resilience in Software-Defined IIoT-Edge Networks
These _intents_ are translated into flow rules that the SDN controller can implement in the network. Additionally, QoS policies specify resource allocation, for example, bandwidth allocation, latency requirements, packet loss tolerance, etc., for different types of traffic or applications. QoS-related policies are configured in the SDN controller to enforce policies by applying the appropriate QoS treatment for each QoS class [150]. [PERSON] et al. [151] developed a reinforcement learning (RL) traffic engineering method that trains a traffic-splitting RL agent to address the dynamically changing traffic and achieve link load balancing. Further, [PERSON] et al. [152] highlighted the implementation of QoS features in different open-source SDN controllers such as ONOS (Java-based _SetQueueInstruction_ functionality), ODL-Lithium (Southbound plugin for the DOCSIS infrastructure), and the Floodlight Northbound interface (which runs the module _QueuePusher_), which generates messages for queue configurations to Create, Read, Update, and Delete flows in the openflow-enabled switches flow tables. **Network isolation and segmentation:** This highlights crucial techniques adopted to divide the network into multiple segments or zones defining security and traffic engineering policies for each zone [153]. It reduces the attack surface because attackers may gain access to one zone and not the other zones, making it significantly harder to attack the entire network. This contains the security breach and prevents it from spreading to other areas of the communication network. Additionally, the network performance is improved by limiting broadcast traffic to specific segments and enhancing fault tolerance by isolating failures [154]. The SDN controller defines the network segments using virtual local area networks (VLANs) to segregate the network traffic. Using RESTAPI-based applications running in the application plane, the SDN controller can implement access control lists (ACLs) and flow rules that enforce the isolation between segments.
# Decorrelation-Based Deep Learning for Bias Mitigation
## 4 Results
### Age Biased German Dataset
#### 4.2.1 Hyperparamter \(\lambda\) Analysis  
The hyperparameter \(\lambda>0\) defines the strength or relative importance of the decorrelation function in relation to the loss function, and hence, it plays a crucial role in deciding the importance of the decorrelation task for bias mitigation. The higher value of \(\lambda\) means features learned by the network are highly decorrelated with bias which might impact the ability of the network to do certain tasks such as classification. A lower value of \(\lambda\) would mean less bias reduction relative to a higher value. The better performance is achieved by trying different values of \(\lambda\) depending upon the requirement of applications.  
Figure 6 plots fairness scores and accuracies for different values of \(\lambda\) for the age-biased German dataset. We can infer from the plot Figure 6a that for the German dataset, SPD, EOD, and AOD values somewhat decrease as the value of \(\lambda\) increases. The main reason behind this is the decorrelation between features learned by the model and age bias in the German dataset increases as the value of \(\lambda\) increases. In Figure 6b, we observe that DI increases as the value of \(\lambda\) increases whereas there is a slight drop in balanced accuracy as the value of \(\lambda\) increases. In order to achieve maximum fairness or bias reduction, we select \(\lambda=0.9\) since the lowest values of SPD and EOD and the highest value of DI are observed for the same. The values for \(\lambda\) may vary for different tasks depending upon network architecture and the complexity of the task.  
Figure 4: tSNE plots of learned features for different methods: (a) For Baseline CNN model [7] (b) For DcCNN model.  
Figure 5: Distance correlation between learned features and bias \(m_{2}\) for the simulated dataset.