esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
## 4 Simulation Verification
### Segmented Mirror System Development  
A thin mirror with a diameter of \(\phi\)500 mm, a radius of curvature of 6 m, an edge thickness of 25 mm, and a material of Zerodur was used as the segments. A segment support system was developed using an optimized design with 18-point Whiffletree supports in the segment axial direction, 12 correction forces per support system to achieve WH technical correction, and 3-point tangential rod-type supports in the lateral direction. According to Figure 1, the splicing distance is 5 mm.  
The room temperature was set to 20 \({}^{\circ}\)C along with three load conditions. T condition had a temperature increase of 10 \({}^{\circ}\)C. The Y and Z conditions were gravity loads along the segmented mirror optical axis pointing horizontally and zenith. Analyses were done for these three conditions, and the mirror surface shape error was calculated. The residual deformation of the segmented mirror surface is shown in Figure 4. The data units in the figure are in nm.  
It can be seen that the segmented mirror deformation is larger under the gravity load, and the mirror deformation generated by the temperature is small. Therefore, the active optical control model is used for the Y and Z conditions to achieve co-phase maintenance. The WH technique is used for the T condition to correct the segmented mirror aberration.
## 3 Results and Discussion  
The capability of the four ILs to remove phenolic compounds from water, using the liquid-liquid extraction technique, was studied. [1P1 Mpip][NTf\({}_{2}\)], [1P3 Myp][NTf\({}_{2}\)], [1P1 Mypr][NTf\({}_{2}\)], and [1P1 Mypr] [Nf\({}_{2}\)] were used to remove phenol, o-cresol, and 2-chlorophenol from water at 293.15 K and atmospheric pressure. The final concentrations of phenolic compounds in water (Table S2), as well as the extraction efficiencies, were calculated (Table S3). High extraction efficiencies were obtained for all the studied systems. According to the results, the legal limit (5 mg\(\cdot\)L\({}^{-1}\) if wastewaters are to be discharged into public sewage system) is reached in only one extraction cycle for initial concentrations below 50 mg\(\cdot\)L\({}^{-1}\) in all the studied systems, except in the case of [1P1 Mpip][NTf\({}_{2}\)] ionic liquid with o-cresol, for which the legal limit in only one step is reached for initial concentrations below 5 mg\(\cdot\)L\({}^{-1}\). For initial concentrations ranging between 50 and 2000 mg\(\cdot\)L\({}^{-1}\), two extraction cycles would be necessary for all systems, and three extraction cycles would be required for initial concentrations between 5000 and 15,000 mg\(\cdot\)L\({}^{-1}\).  
ILs are considered green solvents because they have negligible vapor pressures; nevertheless, it is important to know their solubility and toxicity in water when they are used as extraction agents from aqueous solutions. It is also important to know the solubility of water in the ILs, since their physical properties, such as density or viscosity, are affected by the amount of water in the IL phase. The toxicity of these extraction solvents was not analyzed by this group; however, [PERSON] et al. [17] published a work about the hydrophobicity study for nine ILs with different cations and anions with the aim of knowing the mutual solubility of ILs-water systems. The experimental results showed that the hydrophobicity trend for anions is as follows: [C(CN)\({}_{3}\)] \(<\) [PF\({}_{6}\)] \(<\) [NTf\({}_{2}\)], while the influence of the cation in the hydrophobicity of the studied ILs is [C\({}_{n}\)mim] \(<\) [C\({}_{n}\)mpy] \(\leq\) [C\({}_{n}\)mpyr] \(<\) [C\({}_{n}\)mpip]. Theirstudies also established that the solubility of water in the IL-rich phase is higher than the solubility of ILs in water, being the mole fraction in range from 10\({}^{-3}\) to 10\({}^{-4}\).
## 3 Theoretical Analysis and Proof
### Experiments with Different Datasets
#### 4.3.2 Hy-1c  
In the second experiment, the Arctic sea ice image in Figure 4a is based on the observed image obtained from HY-1C with a spatial resolution of 50 m and a pixel size of 376 \(\times\) 795, and Figure 4b is its ground-truth image. Both the results in Figure 4c-f and metrics in Table 2 indicate that all four models can identify sea ice images well and with high accuracy in the noise-free environment. In detail, the AA value of ETZNN model (29) is 0.993523, which is greater than that of the other three models, which shows that this method does not misidentify. In the random noise \(\psi(t)\in 5\times[-1,1]\) environment, Figure 4g shows that EAEND model (10) still performs well, and the corresponding metrics demonstrate the stability of the model, with Kappa = 0.970668. MNI model (27) exhibits a decline in all of its indicators, which is slightly lower than that of NRI model (28). The top right-hand portion of Figure 4h-j displays misidentification, indicating that the other three models are affected by random noise, resulting in falsely identifying a portion of the ocean as sea ice, and the Kappa coefficients for the models are 0.918992, 0.923335, and 0.787680, respectively, meaning they are unable to accurately identify the Arctic sea ice.
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 4 CubeSat Scheduling Model
### Restrictions
#### 4.3.2 Energy Availability Constraints  
During any period of CubeSat operation, the power balance constraints, battery energy constraints, and battery charging and discharging power constraints are satisfied.  
\[\sum_{j=1}^{J}q_{j}x_{j,t}=v_{t}+P_{dis,t}-P_{ch,t} \tag{15}\]  
Equation (15) is used to construct the power balance constraint. Among the variables, \(P_{ch,t}\) and \(P_{dis,t}\) represent the charging power and the discharge power of the battery, respectively.  
\[SOC_{min}\leq SOC_{t}\leq SOC_{max} \tag{16}\]  
\[SOC_{1}\times(1-0.01)\leq SOC_{T}\leq SOC_{1}\times(1+0.01) \tag{17}\]  
Equation (16) serves to avoid overcharging and over-discharging of the battery [34], where \(SOC_{t}\) is the state of charge of the battery, and \(SOC_{min}\) and \(SOC_{max}\) are the lower limit and the upper limit of the battery state of charge, respectively.  
Considering the continuity of the orbital period of the CubeSat, for its long-term stable operation, Equation (17) ensures that the fluctuation of the state of charge of the battery should not exceed 1% at the beginning and end of each orbital scheduling period.  
\[0\leq P_{ch,t}\leq P_{ch,max}I_{ch,t} \tag{18}\]  
\[0\leq P_{dis,t}\leq P_{dis,max}I_{dis,t} \tag{19}\]  
\[0\leq I_{ch,t}+I_{dis,t}\leq 1 \tag{20}\]  
Equations (18) to (20) are used to construct the charge and discharge power constraints of lithium batteries. Among the variables, \(P_{ch,max}\) is the upper limit of battery charging power and \(P_{dis,max}\) is the upper limit of battery discharge power.
## 2 Methodology
### Multi-Cluster Long Short-Term Memory (MC-LSTM)  
The architecture of the proposed MC-LSTM model is depicted in Figure 2. Unlike a standard LSTM, MC-LSTM incorporates a clustering mechanism to enhance predictive accuracy. Each LSTM model is independently trained on data categorized using k-means clustering, where the optimal number of clusters \(k\) is determined using the elbow method based on WCSS. This adaptive clustering approach ensures the optimal segmentation of power consumption data for different building types, such as residential homes, business offices, and industrial facilities, rather than relying on a predefined \(k\) value.  
By segmenting the dataset based on seasonal and operational conditions, the clustering-based approach allows each LSTM model to specialize in learning the unique temporal and seasonal characteristics of its assigned cluster. This method significantly improves prediction accuracy and reliability, as each model is trained on data with similar underlying consumption patterns. The integration of optimized clustering with deep learning enables MC-LSTM to capture both short-term fluctuations and long-term dependencies in energy consumption effectively. Several key hyperparameters that influence the model's performance are as follows:  
* Input Features: The model processes 14 input features at each time step, capturing various aspects of power consumption.
* Number of Layers: The network is composed of 3 stacked LSTM layers, allowing it to model complex temporal dependencies.
* Hidden State Dimension: Each LSTM cell has a hidden state dimension of 6, balancing the model's capacity and computational complexity.
* Sequence Length: The model considers 10 time steps for each prediction, providing sufficient context while maintaining efficiency.
* Output Features: The model is designed to predict a single value per time step.  
Furthermore, the system incorporates a real-time feedback loop that continuously refines the MC-LSTM model. The loss, computed as the mean squared error between the actual power consumption values and the model's predictions, is used to adjust the model parameters dynamically. This ongoing calibration ensures that the model remains robust and accurate under varying operational conditions.  
This high level of predictive accuracy is particularly valuable for downstream energy management applications, including peak shaving and demand response. Precise power forecasting enables the real-time optimization of distributed energy resources, allowing for more efficient and adaptive energy usage across diverse operational environments. By leveraging multiple LSTM models tailored to distinct clusters, MC-LSTM offers a scalable, data-driven solution for intelligent energy management.
# Total Ionizing Dose Effects on a Delay-Based Physical Unclonable Function Implemented in FPGAs
###### Abstract  
Physical Unclonable Functions (PUFs) are hardware security primitives that are increasingly being used for authentication and key generation in ICs and FPGAs. For space systems, they are a promising approach to meet the needs for secure communications at low cost. To this purpose, it is essential to determine if they are reliable in the space radiation environment. In this work we evaluate the Total Ionizing Dose effects on a delay-based PUF implemented in SRAM-FPGA, namely a Ring Oscillator PUF. Several major quality metrics have been used to analyze the evolution of the PUF response with the total ionizing dose. Experimental results demonstrate that total ionizing dose has a perceptible effect on the quality of the PUF response, but it could still be used for space applications by making some appropriate corrections.  
physical unclonable function; FPGA; total ionizing dose; Co-60 gamma radiation; ring-oscillator Article
# A Smart Decision System for Digital Farming
## 3 Rule-Based Preparation Work
### Data Preparation  
The user starts with a dataset for training the classification model. Typically, the dataset is divided into a training and a testing part. The design of data management and processing procedures, associated with a long-term wireless monitoring system, can join all these farming systems and data acquisition elements (see backend structure in Figure 2). The data from WSN is stored in a MySQL database via middleware through a management process and a communication socket. The user can, for instance, upload a profile of crops and fields that will be stored in the MySQL database.  
The weather forecast updates from a weather server can then be added, including field coordinates and responses with forecasts in XML, which will also be stored in MySQL as the weather profile.  
Data from aerial images (drone or satellite) may be manually uploaded for users via web view (Image and NDVI index file).  
The general structure of the system is shown in Figure 3, where the server layers are described: database, middleware, Server Web, BPM, and responsive web view.  
The system supports several types of pre-processing. Middleware prepares information to store, and the user can do pre-processing using intervals of determinate values. The user can then merge values at different intervals.
## 4 Evaluation
### Throughput
#### 4.2.1 Iops  
Figure 4 shows the IOPS of three file systems with a different number of concurrent tasks being executed. With a small number of concurrent jobs, the local file system XFS always performs the best, since it only requires one disk and thus its main performance bottleneck is the local disk IOPS. The sequential reading IOPS of XFS drops dramatically with the number of clients increasing to 2, most likely due to the built-in DRAM buffer of SSD and its prefetch cache technique. If the firmware receives a sequential read workload request, it will prefetch the subsequent data blocks into its DRAM as cache. This evaluation result is also reported by other work [29].  
In general, for sequential read/write, they are scalable until they reach their limit due to the I/O bottleneck, but CephFS' bottleneck is its software, as it cannot saturate the disk I/O bandwidth. F3 BFS has some advantages in this regard, particularly in cases where there is a high concurrent workload on the file system. For random read/write, both distributed file systems have certain disadvantages compared to the local file system XFS.  
Figure 4: IOPS evaluation for sequential and random IO operations, respectively.  
This is due to the fact that the data is chunked and stored on multiple devices, requiring more time for the chunk addressing and network communications. Nevertheless, we can still observe that F3 BFS performs slightly better than Ceph in terms of random read speeds.  
For the requirement of increasing data read performance, F3 BFS is intentionally designed for having poor random write performance, as shown in Figure 4d. However, this issue is not real a major flaw, because for biomedical big data analyses, most kinds of raw data are collected from biology experiment and are generally not modified after being written, thus often making the reading speed of the data more important than its writing speed. For this reason, we believe it is worthwhile to sacrifice a small amount of random write performance in exchange for a significant increase in read performance.
# Attention-Aware Adversarial Network for Person Re-Identification
## 2 Related Work  
### Person Re-ID  
Two main technical components of person re-ID methods are feature extraction and metric learning. Existing methods focus on extracting robust and representative features of person images, based on which intra-class images are matched in a learned metric space. The work in [8; 9; 15; 16] represented the input images with feature vectors, which are able to depict their global characteristics. However, these methods fail to capture local detailed information and lead to inferior matching and detection performance. In order to solve this problem, many work proposed to explore local features. The work [17] extracted the local features of key points. The work in [18] detected human pose in a local learning module for person re-ID. In [19], images were first divided into several patches. Then, all image patches were input into a a long short-term memory (LSTM) network in sequence to generate ensembled features, which consist of all local information.  
Except for exploring discriminative feature representing methods, it is also crucial to learn a robust metric space, in which the distance of intra-class person images is small, and the distance between inter-class person images is large. This is implemented by elaborately designed loss functions, such as the contrastive loss [20], the triplet loss [21; 22; 23], the quadruplet loss [24], etc.  
In recent years, convolutional neural network (CNN)-based architectures have demonstrated powerful ability for person re-ID. The work in [25] captured the relationship of person images in multi-views with a novel cross-input neighborhood layer. The work in [17] utilized human pose estimation methods to facilitate the person re-ID task. The work in [26] combined CNN and the dictionary learning technique for low-resolution person re-ID. The work in [27] integrated sparse reconstruction learning in a unified CNN framework to solve the partial person re-ID problem. The work in [22] proposed to detect discriminative regions in person images by training a comparative attention network (CAN), which is able to recognize which regions are determined to identify a person. Inspired by this work, our method employs an attention guidance module in the network to assign attentions on the spatial regions of feature maps, which are crucial to recognize a person.
## 2 Background and Literature Review
### IPv6 Routing Protocol for Low Power and Lossy Networks (RPL)
#### 2.1.2 RPL Security  
Below, we briefly present recent work in the field of RPL security in IoT networks.  
A detailed study about RPL security was presented by [PERSON] et al. [15]. Authors provided a comprehensive overview of RPL attacks and categorised them based on their targets, including resources, topology, and traffic. Moreover, they evaluated existing RPL security solutions for several attacks and compared their performance based on various metrics. The authors concluded that specific IDS and RPL protocol mitigation techniques are still in the early stages of RPL protocol. Thus, more research is needed to completely secure IoT networks.  
[PERSON] et al. [16] presented a comprehensive study of RPL attacks. They classified RPL attacks into those inherited from wireless sensor networks and those attacks specific to the RPL. The latest mitigation methods were also discussed and classified for RPL-based networks. The authors reported that, although there are some IoT-based IDSes, RPL-specific attacks, such as DIS attacks, have no appropriate mitigation method to date. Moreover, the majority of studies do not present complete implementations for the mitigation of the various RPL-specific attacks.  
Another extensive study of RPL security attacks and their impact on network performance was conducted by [PERSON] et al. [17]. The authors implemented well-known RPL routing attacks, including rank attack, in ContikiOS, and demonstrated that the protocol is vulnerable to these attacks. A heartbeat protocol was also developed as a security mechanism to protect the IoT network against selective-forwarding attack. However, their solutions worked well only if IPSec was used in the network because an attacker may choose to not filter ICMPv6 packets and thus, avoid being detected.  
[PERSON] et al. [18] studied blackhole and greyhole attacks in an RPL network. The authors analysed the impact of the attacks in Contiki-NG using metrics such as CPU, memory usage, and TX/RX rates. A novel UDP-based heartbeat detection technique was then implemented and evaluated using a Cooja simulator. Results showed high accuracy with low overhead in terms of CPU usage and battery consumption. However, traffic overhead caused by the proposed technique was not studied.  
[PERSON] et al. [14] discussed the latest work about RPL-based rank attack. The rank property of RPL protocol can be exploited and may cause poor network performance and a waste of energy. Mitigation methods as well as the damage caused on network parameters were explained in their work. In addition, the authors compared different attacks including rank attack using the Friedman test, and emphasised on the importance of rank attack.
# KGNER: Improving Chinese Named Entity Recognition by BERT Infused with the Knowledge Graph
## 2 Related Work  
### Named-Entity Recognition  
The named-entity recognition task was conventionally formulated as a sequence labeling problem, where entity boundaries and category labels are jointly predicted. Compared with English, Chinese does not have apparent word boundary features, but it is critical to utilize word boundaries and semantic information in Chinese NER. Usually, before proceeding with NER, word segmentation is performed. However, word segmentation can suffer from error propagation [16; 17]. It has been shown that character-based methods are superior to word-based methods for Chinese NER. However, one disadvantage of character-based methods is that they do not take full advantage of explicit word and word sequence information. Word information plays an essential role in Chinese NER. To overcome this limitation, many efforts have been devoted to incorporating word information by leveraging lexicon features. [PERSON] et al. [8] matches sentences with dictionaries to form a lattice structure that integrates potential word information into character-based LSTMs. It shows that fusing lexical information into the native LSTM may be helpful for NER.  
However, there are some problems with the above-mentioned method. The RNN structure used by Lattice LSTM cannot utilize the global information, which leads to possible lexical information conflicts. In addition, it is tough to migrate the Lattice-LSTM structure to other neural network architectures. This not only limits its inference speed but also makes it difficult to execute training and inference in parallel. [PERSON] and [PERSON] et al. [9] addressed this problem by modeling characters with potential words in parallel, as well as employing a rethinking mechanism. Hence, their method is more efficient and effective. [PERSON] et al. [18] proposed encoding the matched words obtained from the lexicon into the representations of characters. His model is also compatible with any suitable neural architectures without redesign. [PERSON] et al. [19] proposed a fresh lattice Transformer encoder with the help of a porous mechanism for Chinese NER, which is able to manipulate lattices in batch mode and catch dependencies between characters and matched lexical words. [PERSON] and [PERSON] et al. [10,20] resorted to a lexicon and character sequence to build a graph, transforming NER into a node classification work. However, due to NER's forceful alignment between label and input, their model is compelled to embrace an RNN module for encoding. [PERSON] et al. [21] utilized a Transformer that adopts a fully connected self-attentive mechanism to capture long-range dependencies, and also uses position encoding to fuse lattice structures. These methods, based on character-attached lexical information augmentation, have proven to be of great benefit in exploiting lexical information and avoiding word segmentation error propagation, achieving decent results.
## 2 System Model
### Transmitter  
Considering the ease of implementation and the widespread use of on-off keying (OOK) modulation in optical communications, we also adopted this modulation in our system. Let \(u_{k}\) represent the binary data symbol sent via the BS to the \(k\)-th user, and let the vector \(\mathbf{u}=\left[u_{1},\cdots,u_{K}\right]^{T}\) collect the symbols sent via the BS to all \(K\) users in a symbol duration. After the transmitted symbol vector \(\mathbf{u}\) is precoded using the precoding matrix and adding the direct current (DC) bias, the final transmitted signal \(\mathbf{x}\in\mathbb{R}^{M\times 1}\) is expressed as  
\[\mathbf{x}=\mathbf{W}\mathbf{u}+\mathbf{D}=\sum_{k=1}^{K}\mathbf{w}_{k}u_{k}+ \mathbf{D}, \tag{1}\]  
where \(\mathbf{W}\in\mathbb{R}^{M\times K}\) represents the precoding matrix, \(\mathbf{w}_{k}\) is the \(k\)-th column of \(\mathbf{W}\), and \(\mathbf{D}=\left[D_{1},D_{2},\cdots,\ D_{M}\right]^{T}\) denotes the DC bias vector. Note that \(x_{i}\) is the expected number of photons to be sent on the \(i\)-th LED, and its relationship with the transmitted optical power \(P_{t,i}\) satisfies \(x_{i}=\frac{P_{t,i}\tau}{\hbar\
u}\), where \(\tau,h,\
u\) are the symbol duration, the Planck's constant, and the optical frequency, respectively.  
Figure 1: IRS-assisted MU downlink MISO photon-counting communication system.