esa-sceva/satcom-chunk-collection huggingface dataset:
# Generalized Sketches for Streaming Sets
## 4 Preliminaries
### Optimal Densification  
Clearly, the OPH method generates sketches (i.e., vectors) that may have empty elements (i.e., elements equal \(\emptyset\)), which hinders its application for LSH-based similarity search with sub-linear time, and also results in a large error in the Jaccard similarity estimation. To solve this problem, refs. [20; 21; 22] proposed several densification techniques, which reassign empty elements using the values of non-empty elements. The state-of-the-art densification technique, _optimal densification_[22], is shown in Algorithm 2. Specifically, when the \(i^{\text{th}}\) element of OPH sketch \(\vec{x}_{u}\) is empty (i.e., \(x_{u,i}=\emptyset\)), optimal densification iteratively uses a hash function \(j=h(i,l):\{1,\ldots,k\}\times\{1,2,\ldots,\}\rightarrow\{1,\ldots,k\}\), to find a non-empty element \(x_{u,i}\) (i.e., \(x_{u,i}\
eq\emptyset\)). Function \(h(i,l)\) takes two arguments: (1) \(i\), the current empty element that needs to be reassigned; (2) \(l\), the number of failed attempts made so far to reach a non-empty element, which is initialized to \(1\). Let \(\vec{x}_{u}^{*}\) denote the optimal densification of OPH sketch \(\vec{x}_{u}\). For any sets \(N_{u}\) and \(N_{v}\), their Jaccard similarity \(J_{u,v}\) is estimated as  
\[\hat{J}_{u,v}^{*}=\frac{\sum_{i=1}^{k}\mathbf{1}(x_{u,i}^{*}=x_{v,i}^{*})}{k}.\]  
[PERSON] [22] proves that \(\hat{J}_{u,v}^{*}\) is an unbiased estimator of \(J_{u,v}\) and is more accurate than the original OPH method, as well as other densificiation techniques in [20; 21].  
```
input :\(\vec{x}_{u}=(x_{u,1},\ldots,x_{u,k})^{\mathsf{T}}\). output :\(\vec{x}_{u}^{*}=(x_{u,1}^{*},\ldots,x_{u,k}^{*})^{\mathsf{T}}\). foreach\(i=1,\ldots,k\)do if\(x_{u,i}\
eq\emptyset\)then \(x_{u,i}^{*}\gets x_{u,i}\);  end if else \(l\gets 1\); \(j\gets h(i,l)\); while\(x_{u,j}==\emptyset\)do \(l\gets l+1\); \(j\gets h(i,l)\);  end if \(x_{u,i}^{*}\gets x_{u,j}\);  end for  end foreach
```  
**Algorithm 2**Optimal densification [22].
###### Abstract  
The rapid proliferation of artificial intelligence (AI) across various industries presents both opportunities and challenges, particularly concerning personal data privacy. With the enforcement of regulations like Thailand's Personal Data Protection Act (PDPA), organizations face increasing pressure to protect sensitive information found in diverse data sources, including product and shipping labels. These labels, often processed by AI systems for logistics and inventory management, frequently contain Personally Identifiable Information (PII). This paper introduces a novel AI-driven system for automated PII redaction on label images, specifically designed to facilitate PDPA compliance. Our system employs a two-stage pipeline: (1) text extraction using a combination of EasyOCR and Tesseract OCR engines, maximizing recall for both Thai and English text; and (2) intelligent redaction using a pre-trained large language model (LLM), Qwen (Qwen.2.5-72B-Instruct-AWQ), prompted to identify and classify text segments as PII red non-PII red based on simplified PDPA guidelines. Identified PII is then automatically redacted via black masking. We evaluated our system on a dataset of 100 drug label images, achieving a redaction precision of 92.5%, a recall of 83.2%, and an F1-score of 87.6%, with an over-redaction rate of 3.1%. These results demonstrate the system's effectiveness in accurately redacting PII red while preserving the utility of non-sensitive label information. This research contributes a practical, scalable solution for automated PDPA compliance in AI-driven label processing, mitigating privacy risks and promoting responsible AI adoption.  
PDPA; data privacy; data protection; PII redaction; label redaction; image redaction
# Evaluating the Capture Efficiency of Microchannel Plates Through Photoelectron Detection
###### Abstract  
Capture efficiency (CE) is a critical performance parameter for microchannel plates (MCPs), yet its accurate measurement remains challenging. In this study, we propose an innovative method for evaluating the CE of newly fabricated MCPs based on the detection of a photoelectron beam generated by UV light irradiation of a zinc plate. When incident photoelectrons are detected by the MCPs, they produce a series of disordered pulse signals. We demonstrate that the average pulse interval (denoted as Ts) correlates with the number of electrons entering the microchannels, enabling the assessment of CE differences among various MCPs under identical experimental conditions. Additionally, by partially blocking the incident surface to modulate the active area of the MCP, we established a relationship between Ts and active area, providing a means to roughly quantify CE. This method offers a straightforward alternative for assessing MCP performance, with reduced platform requirements and operational complexity.  
capture efficiency; microchannel plates; UV light; pulse interval +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Discussion
### 3D Pose Estimation from 2D Joints  
We tested our proposed self-intersection penalty term (SPT) on UP-3D [24] whose sample images were labeled with ground truth 2D joints. To estimate 3D pose from 2D joints, we defined an objective function as:  
\[E(\mathbf{\theta})=E_{I}(\mathbf{\theta},\mathbf{\beta},K,I_{gt})+E_{SPT}(\mathcal{M}( \mathbf{\beta},\mathbf{\theta};\mathbf{\Phi})) \tag{13}\]  
where \(K\) are camera parameters and \(I_{gt}\) is the ground truth of 2D joints. \(E_{I}\) represents the error between projected joints of the SMPL model and the ground truth 2D joints.  
The shape parameters \(\mathbf{\beta}\) are fixed during optimization. Since the objective function defined in Equation (13) is differentiable, gradient descent can be directly applied to optimize the pose parameters \(\mathbf{\theta}\) by minimizing the objective function. To demonstrate that SPT can improve the accuracy of 3D pose estimation by excluding unreasonable predictions, we also performed the optimization of objective function without SPT which can be represented as:  
\[E^{\prime}(\mathbf{\theta})=E_{I}(\mathbf{\theta},\mathbf{\beta},K,I_{gt}) \tag{14}\]  
Figure 9 visually compares the results of two different objective functions on a few images from UP-3D dataset. It is obvious that minimizing the error between projected joints and the ground truth 2D joints directly without self-intersection penalty term tends to result in body meshes with self-intersection. The fitting results with SPT are more natural and more reasonable compared with the results without SPT. This experiment demonstrated that it is effective to add our proposed SPT into the objective function to avoid self-intersection of body meshes in optimization-based 3D pose estimation.  
Figure 9: Visualized results of 3D pose estimation. Images in row (**a**) are the input images, images in row (**b**) are fitting results without SPT, images in row (**c**) are fitting results with SPT.
## 5 Results and Discussion
### Discussion of Results
#### 5.3.2 Efficient AI Integration  
The measured data were saved continuously to disk in a plain text file. In this file, the trigger times of the measurements were saved together with the corresponding acquired values, so that the data were synchronized. In the processing center, the measured values could be visualized in real time in a graphical representation by means of a GUI programmed specifically for this purpose (Figures 6 and 7b). This application also included a series of data processing algorithms and alerts programmed to be triggered automatically when any of the estimated magnitudes exceeded previously established limit values, which could pose a risk to the correct execution of the test.  
The data processing algorithms used in this study are predictive ML models for time series, specifically ARIMA and LSTM (Section 4.2), since the data recorded in the test exhibit such characteristics. The integration of both prediction models is highly complementary and effective. ARIMA establishes a predictive foundation grounded in the statistical structure of historical data, while LSTM enhances this analysis by identifying nonlinear and subtle patterns. The accuracy achieved through the combination of these models in 5 s predictions resulted in an MSE below 8%, ensuring the effective monitoring of the evolution of the test with a sufficient lead time to implement corrective actions if necessary.  
AIoT architectures are designed to facilitate the integration and deployment of these types of models in each of the layers: edge, fog and cloud. In the use case, predictive models have been applied in the fog layer, capable of predicting future values and anticipating possible anomalies in the development of the test. The chosen models complement their detection capabilities in an efficient and accurate manner. However, there are other options available that could also produce high performance, such as convolutional models or transformers [56; 57]. The cloud layer has been used as a backup for raw data history, but it is common to apply more complex ML models to analyze large amounts of data, whose execution in other layers would not be feasible due to their high computational cost and latency.
## 3 Diffuse Reflectance Spectroscopy: Analyzing Results
### Tauc Plot  
For evaluation of diffuse reflectance spectra, the Kubelka-Munk (K-M) function \(F(R_{\infty})\) is frequently used [103]:  
\[F(R_{\infty})=\frac{K}{S}=\frac{(1-R_{\infty})^{2}}{2R_{\infty}}\;\;, \tag{3}\]  
which connects the diffuse reflectance \(R_{\infty}\) to an (abstract) absorption quantity \(K\) and scattering quantity \(S\). Even though the K-M function is foremostly designed for calculating the ratio \(K/S\), additional insight on the absorption characteristics can be obtained. Assuming that \(S\) changes negligibly within the wavelength region of electronic absorption, the K-M function becomes solely dependent on \(K\). This way, information regarding the absorption \(\alpha\) can be extracted which is connected to \(K\) through a functional relation involving the incident photon energy \(E=I\
u\) and an electronic transition-dependent exponent \(n\) (cf. Table 3) [103]:  
\[(F(R_{\infty})\cdot I\
u)^{n}\sim(\alpha\cdot I\
u)^{n}\;\;. \tag{4}\]Similarly, the onset of the electronic band gap described through the absorption coefficient \(\alpha\) may be linearized as follows [103; 110]:  
\[\alpha\cdot hv=A_{1}(h\
u-E_{\mathrm{g}})^{n} \tag{5}\]  
with an arbitrary proportionality constant \(A_{1}\). Combining Equations (4) and (5) connects the band gap energy \(E_{\mathrm{g}}\) to the K-M function [110]:  
\[(F(R_{\infty})\cdot h\
u)^{(1/n)}=A_{2}(h\
u-E_{\mathrm{g}})\ . \tag{6}\]  
By plotting the thus modified diffuse reflectance (mod. DR) against the photon energy \(h\
u\), the band gap energy \(E_{\mathrm{g}}\) can be extracted by a linear fit at its rise as shown in Figure 12. The crossing point between fit and abscissa will then immediately yield \(E_{\mathrm{g}}\)[41; 111; 112; 113]. This procedure is also known as _Tauc plot_ and is a powerful tool in the field of quantitative determination of band gaps and absorption centers.  
With rutile-phase TiO\({}_{2}\) possessing a direct forbidden transition [114; 115; 116] the determined band gap amounts to \(E_{\mathrm{g}}\) = 3.03 \(\pm\) 0.01 eV \(\equiv\) 409 \(\pm\) 1 nm which is in excellent agreement with published results and underlines the validity of this method [117].
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 5 Key Challenges and Future Research Directions
### Storage and Power Optimization Issues  
As discussed above, with big data and large heterogeneity among sensors, there is a need for larger memory devices for storage of the acquired raw grid parameter data. Different storage systems are required at multiple levels such as sensing levels in the input layer, communication level, and application level for data processing. Furthermore, storage systems are required to be secured from cyber-attacks. They are also required to be expandable or scalable in case new sensor integration occurs later in the future. Energy storage [156] is also an issue since the integration of microcontrollers, and communication and computation modules, along with multiple sensors call for more power utilization. Furthermore, most devices are deployed in remote environments and regular battery charging and re-charging can be demanding and expensive. Power optimization [18] is being conducted through energy harvesting, power converters, power latency tradeoffs, etc. Optimized embedded systems are under research and are a promising area. Devising energy efficient protocols and mechanisms [72] that enable devices or sensors to self-generate, self-harvest, recycle, and store energy is another research direction to address the issue of storage and power requirements.
# Engineering Resource-Efficient Data Management for Smart Cities with Apache Kafka +
## 3 DMP Software Architecture Design  
The main components of the DMP are introduced briefly in Section 1 (see also Figure 1). Below, we provide an overview, followed by component details in dedicated sub-sections.  
**DatAna** is responsible for collecting the inference results from all AI components from all layers through its instances residing at each layer, transforming them into SDM-compliant counterparts and then transferring them to higher layers in the E2F2C continuum. The DatAna cloud layer thus aggregates all the transformed inference results and relays them to the DFB, which also resides in the cloud. **DFB** persistently stores all the SDM-compliant inference results it receives, but also ensures they are available in real time to SmartViz and Data Corpus. DFB also exposes a REST API to SmartViz to allow it to access all the archived inference results in its ElasticSearch ([[https://www.elastic.co/](https://www.elastic.co/)]([https://www.elastic.co/](https://www.elastic.co/)), accessed on 16 January 2023) database. In addition, the DFB receives user-generated inference result verification messages from SmartViz and use this information to update the corresponding inference results stored in its database. **HDD** interacts exclusively with the DFB to receive information on current Kafka topic partitioning and associated performance metrics and to send updated, optimised Kafka topic-partitioning recommendations. In parallel, **StreamHandler** receives information on active AV sources after requesting it from a component called _AV Registry_ via a REST call and it uses that information to connect to all the active AV sources and receive their AV data streams to segment them and store them persistently. StreamHandler also exposes a REST API that is accessed by SmartViz to request archived AV data from specific sources and points in time. The Data Corpus resides at the cloud and is subscribed to all the DFB Kafka topics where DatAna publishes SDM-compliant inference results to receive them in real time and archive them internally to ensure they are available for further AI training purposes along with the associated AV data it collects from StreamHandler. The Data Corpus is also subscribed to the DFB Kafka topic that is used by SmartViz to publish user-generated inference result verifications to receive them in real time and update the corresponding archived inference results accordingly. The Data Corpus is also connected to StreamHandler, from which it receives AV data as binary files that are a result of AV stream segmentation.
## 1 Introduction  
The main objective of researchers should be to develop methods that expand the industrial state-of-the-art in order to increase productivity, reduce costs or overall improve the performance measures. However, these methods must not only be theoretically possible based on models, but must also be robust, practical and easy to implement for practitioners. In many cases, however, researchers' models are oversimplified and do not reflect the actual conditions faced by practitioners. Other times, the proposed solutions are too difficult to implement in real-life situations. This paper examines one such example, the Resource-Constrained Project Scheduling Problem (RCPSP), which is found in practically every industry and has long been a focus of interest among the operations research community. However, despite the high level of interest, no standard framework has emerged to guide practitioners in creating a project schedule that allocates limited resources in such a way as to: (1) maximize outcomes; (2) minimize risks of delay or failure; (3) ensure adherence to budgets and schedules; (4) ensure that quality meets expectations; and (5) eliminate the scheduler's experience as an impact factor.  
Taking these factors into consideration, in a previous study, the authors proposed a methodology to develop robust, upper-bounded schedules for the Multi-mode RCPSP (MRCPSP), a more realistic variation of the RCPSP. In this paper, however, the methodology is extended to a considerably more complicated and true-to-life model, the MRCPSP with minimal and maximal time lags (MRCPSP/max). The most significant difference between these two models is that, due to its constraints, the MRCPSP/max presents loops between activities. This means that an activity \(i\) can be both a predecessor and a successor of an activity \(j\). Not only does this condition make the problem more difficult to solve, but it also resembles a real-life situation, which is not accounted for in the MRCPSP model. On the other hand, the methodology used only requires inputs that should be included in every project, i.e., estimations of activity durations and the set of precedencies that need to be satisfied. The proposed methodology results in execution time-frames, meaning a makespan range (minimum and maximum makespan) in which a project can be executed. These schedule execution time-frames usually include the benchmark's best-known solution, as the lower bound and the upper bounds are on average no larger than 11% of their own lower bound. The rest of this article is organized as follows: Section 2 presents a literature review for the characteristics of past models and the solutions proposed in other studies; Section 3 details the proposed methodology; Section 4 summarizes the results, and conclusions are presented in Section 5.
# Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network
###### Abstract
It is especially suitable for multiple classifications of defects in variable environments, which has strong versatility and robustness. Overall, most studies regarding copper strip surface defect detection mainly use traditional machine vision, which is susceptible to interference from production site environmental factors, such as light, fog, and vibration. This traditional method also has poor versatility and robustness, and only a few types of defects can be identified. Thus, practical application of this method is not ideal. Compared with traditional machine vision, deep learning has improved capability of non-linear learning perception and generalised anti-interference, which can strongly overcome the shortcomings of traditional machine vision. Therefore, researching a new type of intelligent identification method of copper strip surface defects suitable for multiple classifications has strong practical significance and is crucial for improving the surface quality of the copper strip and enhancing the level of intelligence. The remainder of this paper is organized as follows: Section 2 presents surface defects of copper strip from the literature. The characteristics and classifications of surface defects are explained, and the surface defect dataset is established. In Section 3, the overall process of surface defect detection is proposed, and then the surface defect discrimination model and recognition model are established, respectively. A model training strategy is formulated through a large number of experiments, and then a variety of methods are used to visually analyse and evaluate the model recognition mechanism in Section 4. Finally, Section 5 presents our conclusions and outlines possible directions for future research.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 4 Propulsion Systems in Nanosatellites  
The compact size and weight of nanosatellites are a limitation for propulsion systems, which play a crucial role in maneuvering, orbit changing, and mission longevity. This Section presents a taxonomy of micro-propulsion systems that are suitable for nanosatellites and are used or under development. The main difference between propulsion systems is the dependence on onboard power, which can be categorized into two main types: non-electric and electric. Electric propulsion systems require onboard power for operation. In contrast, non-electric power only needs onboard power to regulate the propulsion system, which means that onboard power is needed only to initiate and terminate the propulsion processing.
## 4 Machine Learning and LoRaWAN  
LoRaWAN technology may be enhanced in several ways with the use of ML, which enhances resource efficiency and optimization. Energy Consumption Optimization is one important field where machine learning can show immense benefits. ML algorithms can evaluate and optimize device usage in LoRaWAN by exploring complex data patterns. This optimization promotes resource-efficient and sustainable operations by extending the battery life of devices as well as reducing the need to replace them more often [29].  
In network management, ML techniques also play an important role. These algorithms can predict potential faults and optimize network settings through continuous performance analysis of the network. This approach reduces the potential challenges before they escalate. Proactive management improves productivity as well as making LoRaWAN robust and reliable [30]. Another field where ML has a significant influence is traffic prediction and load balancing. ML can predict traffic patterns on the LoRaWAN network by using predictive models [31]. This prediction enables effective load distribution across LoRaWAN gateways, ensures optimal utilization of network resources, and prevents bottlenecks. ML algorithms can identify problems in the LoRaWAN network through fault detection and localization. As a result, errors can be fixed, downtime is reduced, and network performance is enhanced [18].  
The current literature is comprised of various ML model techniques and datasets that are used to enhance resource and EE optimization in LoRaWAN. This section covers various ML and AI-based techniques, such as supervised and unsupervised learning techniques, along with DL (Deep Learning), Neural Network (NN), and Reinforcement Learning (RL).