esa-sceva/satcom-chunk-collection huggingface dataset:
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 6 Conclusions  
In this paper, we presented ChaMTeC, a novel time-series anomaly detection framework that combines inverted embedding, temporal encoding, and MSE-based feedback mechanisms with dynamic thresholding. Our experimental results demonstrate that ChaMTeC outperforms most of the existing state-of-the-art models across multiple benchmark datasets. In addition, when an ablation study was conducted to evaluate the contribution of each component of the ChaMTeC model to the performance, it was observed that the Inverted embedding component strengthened the model's ability to detect subtle anomalies in time-series data, and when it was removed, there was a significant decrease in the F1-CPA score. Temporal encoding plays a critical role in understanding time-dependent anomalies, and when it was removed, there was a decrease in the recall rate. Finally, the MSE-based feedback loop and dynamic thresholding increase the accuracy by continuously adjusting the model's detection thresholds; when this component was removed, there was a significant decrease in precision. These results reveal how each component contributes to the overall performance of ChaMTeC and the model's anomaly detection ability. The framework's effectiveness is particularly evident in its robust performance on complex industrial datasets such as SWAT and our newly introduced WaterLog dataset. Our results suggest that the combination of channel-mixing techniques and temporal convolutional networks, along with our dynamic thresholding approach, is particularly effective for detecting subtle anomalies in industrial control systems. Future work could explore the adaptation of our framework to other domains and the integration of additional context-aware features to further improve detection accuracy. However, we observe that the inclusion of attention mechanisms in our architecture introduces dataset-dependent performance variations. This suggests that standard attention mechanisms may not sufficiently capture anomaly-specific temporal patterns in certain contexts. While ChaMTeC demonstrates strong anomaly detection performance, certain types of anomalies remain challenging, particularly long-duration anomalies, abrupt pattern shifts, and cross-sensor dependencies. Long-duration anomalies spanning multiple sliding windows are harder to detect due to the model's focus on recent temporal patterns, while abrupt shifts in volatile signals can be mistaken for transient fluctuations. Additionally, complex dependencies between multiple sensor readings in datasets such as WaterLog pose difficulties, as standard feature extraction methods may overlook subtle correlations. Interestingly, while
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 5 Conclusions and Future Work  
In this work, an in-depth study is carried out in order to determine how a remote sensing multi-class semantic segmentation problem should be properly tackled. We have focused on high-resolution satellite imagery, since the limited spatial resolution reduces the separability of the classes. In this way, we set a challenging scenario for evaluating the usefulness of the different approaches included in this study.  
Specifically, the joint building footprint and road network detection in high-resolution satellite imagery is addressed. For this purpose, a dataset combining S1 and S2 high-resolution satellite imagery with OSM building and road labels is generated. The direct multi-class approach is compared to the OVO and OVA binary decomposition strategies as well as their combination with a multi-task learning hard parameter sharing scheme. Quantitative and qualitative results show that decomposing a multi-class problem into multiple binary ones using an OVA binary decomposition strategy leads to better results than the standard direct multi-class approach. Moreover, despite obtaining similar results overall, multi-task learning seems promising for pushing the performance of binary segmentation models. Therefore, more advanced auxiliary tasks may be taken into consideration for a clear pay-off.  
Furthermore, more experimentation is required to properly assess the effect that the number of classes has on the usefulness of the decomposition strategy. Moreover, the dataset could be extended to other cities different from the ones used for training and testing the models. This outlook will not only have a positive effect on the generation capability of the models, but it will also make the evaluation fairer, giving more weight to the conclusions extracted.  
Figure 10: Qualitative results for the benefits of using a multi-task learning scheme. Visual comparison of the results obtained with the OVA binary decomposition strategy and its multi-task variant, for a zone taken from Barcelona North city in the test set. TP are presented in green, FP in blue, FN in red and TN in white.  
**Author Contributions:** Formal analysis, C.A. ([PERSON]); Investigation, C.A. ([PERSON]); Supervision, C.A. ([PERSON]) and M.G.; Writing--original draft, C.A. ([PERSON]); Writing--review & editing, M.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** [PERSON] was partially supported by the Government of Navarra under the industrial PhD program 2020 reference 0011-1408-2020-00008. [PERSON] was partially supported by Tracasa Instrumental S.L. under projects OTRI 2018-901-073, OTRI 2019-901-091 and OTRI 2020-901-050, and by the Spanish MICIN (PID2019-108392 GB-I00/ AEI/10.13039/501100011033).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Performance Evaluation  
In this paper, we compare the proposed method with Geomesa in terms of ingestion and range query throughput through experiments. Geomesa is one of the well-known big spatiotemporal data management systems. It is currently maintained and professionally supported by CCRi. The most recent version of Geomesa is 2.3.1 released in July. 2019. We use the Geomesa 2.3.1 version in our experiments for the comparison. Table 2 shows the experimental environment of this paper. Nine nodes are used for Geomesa and the proposed method, and 8 nodes are used for clients that request queries and data insertion. Client HW(Hardware) specifications are higher than server HW specifications. The reason is to run multiple client processes on each client node to provide enough workload for Geomesa and the proposed method.  
We generate a couple of synthetic spatiotemporal data sets from the GPS coordinate area (37.2125, 128.1361111-36.7944444, 127.6611111), as shown in Figure 9. The first dataset is 100,000,000 spatiotemporal data with a uniform distribution. The second dataset is a 100,000,000 spatiotemporal dataset with a hot spot where 80% of the total data places 20% of the area. We also generate two query sets. Like the data set, the first query set has a uniform distribution of query ranges and the second query set has the same hot spot as that of the second data set. The average number of returned objects of the range queries is about 120. To compare the performance of the proposed method and Geomesa, we measure ingestion and query throughputs with varying the number of nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Server HW (9 Nodes)** & Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30 GHz, 4 cores, HDD 50 GB, RAM 16 GB \\
**Client HW (8 Nodes)** & Intel(R) Xeon(R) CPU E5-2676 v3 @ 2.40 GHz, 16 cores, HDD 20 GB, RAM 64 GB \\
**SW(Software)** & Ubuntu 16.04 LTS, Apache Kafka 2.11-2.2.0, Apache Hadoop 2.8.4, Apache \\  & Accumulo 1.9.1, Apache Zookeeper 3.4.10 \\
**Data Set 1** & Synthetic spatiotemporal data 100,000,000 with uniform distribution generated \\  & from the GPS area (37.2125, 128.1361111-36.7944444, 127.6611111) \\
**Data Set 2** & Synthetic spatiotemporal data 100,000,000 with hot spot generated from the GPS \\  & area (37.2125, 128.1361111-36.7944444, 127.6611111) \\
**Query Set 1** & Range queries 5,000,000 with uniform distribution \\
**Query Set 2** & Randomly generated range queries 5,000,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environments.  
Figure 9: GPS coordinate area used for data generation.
## 1 Introduction
The application of LPGP under various conditions including contact with air (LPGPA), nitrogen (LPGPN), carbon dioxide (LPGPC) and methane (LPGPM) has been found to enhance the yield of essential oils by 40%, 60%, 20% and 20%, respectively [20]. Nanowater was evaluated for its potential as a catalyst for high-quality brewing malt production. Soaking spring barley grains in NW led to significantly higher water uptake and germination capacities. The resulting malt showed improved moisture content and seed index (1000-grain mass) [21]. [PERSON] et al. [22] demonstrated that water exposed to low-temperature, low-pressure glow plasma of low frequency for 30 min stimulated the growth and pathogenicity of insecticidal fungi _Beuveria bassiana_ and _Isaria fumosorosea_, which are used as biopesticides. [PERSON] et al. [23] conducted tests on NW to investigate its effects on the growth of microorganisms commonly found in the human body. The tests aimed to establish whether NW could stimulate or inhibit the growth of these microorganisms. The selected species, such as _Escherichia coli_, _Saccharomyces cerevisiae_, _Aspergillus niger_, _Candida albicans_, and _Enterococcus faecalis_, were monitored by measuring the optical density of their cultures. In the instance of culture in water treated with LPGP, a growth stimulation of approximately 20% was observed between the 12 th and 24 th hour, specifically for _E. coli_ and _S. cerevisiae_ cells. The positive impact of NW in the aforementioned experiments, particularly in promoting microorganisms' growth, has motivated further research into the potential use of this method to enhance the effectiveness of gamma-decalactone biosynthesis by _Yarrowia lipolytica_ yeast cells. Gamma-decalactone (GDL), a cyclic ester of 4-hydroxydecanoic acid with a sweet peach fragrance, has the chemical formula C\({}_{10}\)H\({}_{18}\)O\({}_{2}\). The biotechnological synthesis of \(\beta\)-oxidized ribonucle acid through the biotransformation by yeast cells is a well-established process [24; 25]. Despite the long history of research on GDL biosynthesis (the discovery of _C. tropicalis_' ability to produce GDL dates back to 1963) [26], increasing the efficiency of the bioprocess remains a topic of ongoing scientific investigation [27; 28]. The present article reports on a study that analyzed the growth and catalytic activity of _Y. lipolytica_ yeast in the biotransformation of castor oil to GDL and other secondary metabolites using nanowater-based media. To the best of the authors' knowledge, it is the initial attempt to assess the impact of NW on the potential of _Y. lipolytica_ for producing GDL. In the experiments conducted, three types of treated water were used: water treated with LPGP in an atmosphere of nitrogen or air, and Nantes plasma water--a commercial product of NANTES Systemy Nanotechnologii Sp. z o.o. (Boleslawiec, Poland) company with a higher content of singlet oxygen.
## 2 Related Research
### Fiber-Optic
#### 2.2.2 Fiber-Bragg Grating (FBG)  
A diffraction grating formed by an ultra-violet laser in the optic fiber modulates a certain range of the spectrum of the reflected light [22]. The spectrum changes according to the applied deformation and/or temperature, and thus the FBG can be used as a force/temperature sensor. [PERSON] et al. presented a 3-D force sensing surgical instrument for eye surgery with a diameter of less than 0.8 mm. In this device, three FBG optic fibers are attached along the long axis of the surgical instrument [23; 24]. [PERSON] et al. also presented a surgical instrument for eye surgery, implemented using FBG sensors [25]. [PERSON] et al. presented an FBG embedded irrigated radiofrequency ablation catheter [26]. [PERSON] et al. presented surgical robotic forceps with multiple degrees of freedom with embedded FBG sensors. The robotic forceps were specially designed to have flexure in the structure [27]. [PERSON] et al. presented a backburn-shaped flexure design for force sensing using FBG [28].
# Soft Contrastive Cross-Modal Retrieval
## 4 Experiment
### Setting
#### 4.1.2 Baselines  
To confirm the significant performance of our proposed method, we compare SC-CMR with 12 state-of-the-art methods in our experiments, including 6 traditional models  
Figure 5: Sample image–text pairs of Wikipedia, NUS-WIDE, and Pascal Sentence.  
(CCA [13], SM [59], MCCA [14], MvDA [28], MvDA-VC [28] and JRL [29]) and 6 deep learning methods (Deep-SM [33], CMDN [34], DCCA [35], DCCAE [36], ACMR [32] and MG-HSF [31]). All of these baselines are introduced briefly as follows.  
* **CCA** is a multivariate statistical method used to analyze the correlation between two sets of variables, often employed in cross-modal retrieval to learn correlations between different data modalities such as images and text.
* **SM** uses machine learning technology to represent cross-modal data as semantic vectors, facilitating retrieval and matching based on semantic content.
* **MCCA** is a statistical approach designed to explore relationships between multiple datasets, with the goal of finding linear associations that reveal shared structures or patterns.
* **MvDA** is an analysis method focused on maximizing the variance between different views of the same object or event while minimizing the variance within views of different objects or events.
* **MvDA-VC** is an extension of MvDA, which aims to maintain consistency across different views in the low-dimensional embedding space, thereby more effectively capturing the correlation and structural information between views.
* **JRL** is a method that improves information sharing and complementarity between different modalities or perspectives, leading to enhanced generalization ability and model performance.
* **Deep-SM** employs deep learning techniques for semantic matching, embedding data from different modalities like text and images into a neural network that facilitates cross-modal semantic similarity calculations.
* **CMDN** uses two multi-modal neural networks to embed and represent data, performing matching in a low-dimensional semantic space for cross-modal semantic similarity assessment.
* **DCCAE** consists of two main encoders and decoders that work together to encode input data into a low-dimensional representation and then attempt to reconstruct the original data, learning a compact representation for each modality.
* **ACMR** employs adversarial generative network techniques (specifically, Generative Adversarial Networks or GANs) to learn feature representations between modalities, using adversarial training to refine these representations.
* **MG-HSF** combines hierarchical semantic fusion with cross-modal adversarial learning to capture both fine-grained and coarse-grained semantic knowledge, generating modality-invariant representations in a common subspace.
# Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer
## 4 Experimental Validation
### Experimental Environment  
The experiment utilizes the Mujoco [21] simulation environment within the Gym environment. This environment incorporates a comprehensive robot motion engine, providing detailed motion information for each component of the intelligent agent. It encompasses the state information of the agent, along with reward-related settings, making it suitable for training in reinforcement learning tasks. The environment offers a variety of task scenarios, and the experiment specifically involves conducting assessments in the Hopper, Walker2D, and HalfCheetah environments. The experimental scenario is illustrated in Figure 11.  
**Hopper:** This environment features a single-legged robot with four main body parts: torso, thighs, legs, and feet. The robot's motion involves the control of three joints to execute a jumping maneuver. The state observation encompasses angular velocity, joint angles, and coordinates. The action space is defined by the control values for the three joints, and rewards are provided based on the robot maintaining an upright posture and successfully propelling forward.  
**Walker:** In this environment, the robotic body features two complete legs and is composed of four main components: torso, thighs, legs, and feet. Unlike Hopper, the intelligent body in this scenario has two legs and two feet. The state observation space is similar to that of Hopper, while the action space expands in spatial dimension due to the increased number of joints in the intelligent body. Reward feedback is obtained when the intelligent agent maintains an upright position and successfully propels forward.  
**HalfCheetah:** This environment involves a two-legged robot that exhibits animal-like movements and endeavors to progress within the plane defined by its legs. The state observation space encompasses the angles, angular velocities, and coordinates of the joints. Similarly, the action space includes control information for the joints. Reward feedback is provided when the intelligent agent maintains an appropriate upright posture and moves in the correct direction.  
In the second segment of the experiment, we utilized the Maze2D game environment, focusing on a navigation task. The objective of the test is to discover the shortest path to the target point by integrating sub-optimal trajectories with the Offline RL algorithm. This environment comprises three game scenarios: Umaze, Medium, and Large, each featuring a maze structure illustrated in Figure 12. The primary evaluation of the model's performance revolves around its capacity to concatenate sub-optimal trajectories to efficiently find the shortest path to the target point.
## 5 Conclusions  
In summary, our results suggest that LA-1 accumulates rapidly in the ischemic brain lesions by targeting integrin \(\alpha\)v\(\beta\)3. Thus, it effectively alleviates cerebral ischemia-reperfusion injury in MCAO rats. The mechanisms of action of LA-1 are associated with suppressing inflammation and oxidative stress, as well as modulating the PI3K/Akt and Nrf signaling pathways. These findings highlight the potential of LA-1 as a therapeutic agent for cerebral ischemia-reperfusion injury, and the promising role of \(\alpha\)v\(\beta\)3 mediated cellular uptake for efficient drug brain delivery.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/antiox13111339/s1](https://www.mdpi.com/article/10.3390/antiox13111339/s1)]([https://www.mdpi.com/article/10.3390/antiox13111339/s1](https://www.mdpi.com/article/10.3390/antiox13111339/s1)). Scheme S1. Preparation of LA-1. (i) HBTU, NMM and DMF; (ii) H\({}_{2}\), Pd/C and CH\({}_{3}\)OH. Figure S1. 1 H NMR (300 MHz, DMSO-4\(\delta\)) spectra of LA-1. Figure S2. 13C NMR (75 MHz, DMSO-4\(\delta\)) spectra of LA-1. Figure S3. The mass spectrum of LA-1 (ES\({}^{+}\)). Figure S4. The purity of the LA-1. Figure S5. The ESR signal intensity of NO, -OH, and DPPH radicals. Figure S6. The MTT assay result of LA-1 and loganin. Figure S7. Assessment of the biosafety of drugs in vivo. Table S1. Details of the gradient.  
Conceptualization, Y.W.; formal analysis, A.T.; validation, A.T.; investigation, A.T., M.M.T., R.J., K.W., L.C. and Y.J.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON], [PERSON], [PERSON] and [PERSON] designed the study. [PERSON] performed the experiments and analyzed the data. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] participated in part of the experiments. [PERSON] provided experimental drugs and quality control. [PERSON] wrote the manuscript. [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from the Chinese Institutes for Medical Research, Beijing under the CIMR Organized Scientific Research Program (CX23 YZ06) and Beijing Hospitals Authority Clinical Medicine Development of special funding support (ZLKK202308). The animal study protocol was approved by the Institutional Animal Care and Use Committee of Capital Medical University (protocol code AEEI-2020-188, approved on 2020). Not applicable. Data Availability Statement: Data will be made available upon request.  
We would like to thank [PERSON] and [PERSON] from the School of Pharmaceutical Sciences of Capital Medical University for their guidance on some of the experimental details.  
The authors have no conflicts of interest to declare.
## 4 Discussion
### Challenges and Limitations
This lack of global frequency standardization limits RFID's effectiveness in international supply chains, creating barriers to its broader adoption in a unified EU market [28]. Privacy and security concerns also present substantial obstacles. Consumers are wary of the potential for RFID tags to track their movements and purchasing patterns automatically. Although the implementation of privacy features such as \"kill switches\"--which deactivate RFID tags at the point of sale--can address some privacy issues, ensuring the security of data throughout the supply chain remains a major challenge. Unauthorized access to RFID systems could compromise sensitive supply chain information, which is particularly concerning in the food industry, where consumer trust and product safety are paramount. Balancing RFID's efficiency and traceability benefits with robust privacy and security measures will be essential to garnering consumer acceptance and trust in this technology [29]. Despite these challenges, RFID technology holds significant potential to enhance supply chain efficiency, improve product traceability, and contribute to sustainability in the EU dairy industry. However, overcoming the technical, economic, regulatory, and social hurdles will be crucial for realizing its full benefits. Addressing these limitations requires continued investment in technology improvements, policy alignment, cost reduction strategies, and public engagement to build trust in RFID as a reliable and secure solution for the dairy supply chain.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Other Projects for Energy Management through ICT Tools
#### 4.4.6 The Houat and Hoedic Islands  
Energy control and monitoring has been the target of \"The Houat and Hoedic islands\" project, led by the ERDF French energy supplier. It mostly involved a field test during which an Energy-Box (EBox) was installed in the consumers' homes for energy control [97].
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods
### 3D Reconstruction
#### 2.5.4 Dense Matching  
This dense point cloud process was based on the MVS algorithm [7]. The software was set to high-quality mode for dense matching and the final results were calculated based on the initial tie points. This workflow was repeated for both the spherical and DSLR datasets. The final output was a.txt file with the 3D point cloud coordinates.