esa-sceva/satcom-chunk-collection huggingface dataset:
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 2 Related Work  
### Scale Change  
Objects with different scales can be more appropriately reflected in different levels of feature maps. Thus, for the detection of multi-scale objects, the fusion of high-level and low-level layer features is a common method. [PERSON] et al. [15] enhanced the feature map from the lowest-level layer with semantic information, and fused the enhanced feature map with the feature map from the higher-level layers, which improved the performance of small object detection without affecting the detection of large objects. [PERSON] al. [16] proposed a novel joint image cascade with enhanced semantic features and a feature pyramid network with multi-scale convolutional kernels, which can extract multi-scale semantic features and improve the detection results. [PERSON] et al. [17] introduced a pyramid local context network to learn the global and local contexts of objects, as well as the multi-scale features. [PERSON] et al. [18] designed a finer anchor sampling and a feature fusion network to improve the detection performance for small objects. [PERSON] et al. [19] upsampled high-level layers and combined them with the low-level layers to aggregate feature maps for richer features.  
Most of the above methods consider multi-layer and multi-scale features, but just directly aggregate the hierarchical feature maps of different layers for fusion. Actually, the receptive fields learned from different scale feature maps, in which the different semantic information is contained, are different. The direct fusion of two features with large semantic information gaps will weaken the expression of multi-scale features for the subsequent network [20]. From the perspective of refined features, introducing multi-scale receptive fields acting on the multi-layer features, to further enhance the features, can allow to obtain more discriminative and robust features, and make better use of the fused features.
## 4 Conclusions  
The work presented here has shown that PS-_co_-DVB polyHIPEs with a readily tailored pore morphology can easily be formed within the confines of 1.0 mm i.d. silcosteel tubing with secure covalent attachment to the walls. Our goal was not to equal or surpass the chromatographic performance of previously reported polymer monoliths in the literature for small molecules, but rather to explore the possibility of preparing a polyHIPE material in a larger diameter column housing. To this end, this report is the first instance of a polyHIPE being formed and covalently attached to a column housing larger than 100 \(\upmu\)m i.d. Monoliths with 90% porosity exhibiting the classical \"voids and windows\" bimodal pore structure could withstand pressures over 200 bar. The monolith exhibited predictable separation selectivity for alkylbenzenes, characteristic of a traditional polystyrene stationary phase. Despite the low column efficiency, it should be noted that this separation represents the first report of the isocratic separation of small molecules on a polyHIPE using pressure-driven flow. Future work will include efforts to decrease stationary phase mass-transfer resistance by optimisation of the nano-porosity of the polymer skeleton, either by the incorporation of an additional porogenic solvent in the continuous \"oil\" phase during polymerisation [46; 55] or through the use of hyper-crosslinking using methods reported by [PERSON] _et al._ [19.20].  
Funding for this research was provided by the Irish Research Council (IRC) for the EMBARK initiative (Grant number: RS/2012/24).  
[PERSON] and [PERSON] conceived, designed and performed thex experiments; [PERSON] and [PERSON] were also involved in experimental conception and design and subsequent data interpretation and wrote the paper.  
The authors declare no conflict of interest.
## 3 Methods
### Instrumentation  
#### 3.2.1 Stretch-Sensor Glove  
This study employed stretch sensors provided by Image Scientific Instruments, Inc., Staten Island, New York. as depicted in Figure 1. The sensor is designed as a flexible cylindrical cord with a diameter ranging from 0.06 to 0.07 cm. It features a distinctive polymer composition that alters its resistance in response to stretching. An unstretched sensor exhibits a resistance of 1000 ohms per linear inch. When stretched to 50% of its original length, the resistance gradually rises to approximately 2000 ohms per inch. As illustrated in Figure 2, at this 50% stretch, the resistance effectively doubles compared to its initial value. Furthermore, after being stretched, there may be a slight increase in resistance upon release before it eventually returns to its original resting state [38].  
Figure 1: Flexible stretch sensor with hook at the terminal [38].  
Figure 2: Characteristic of stretch sensor under 50% stretch from the initial length [38].  
A total of five stretch sensors were employed and individually affixed to the finger sections of the glove, as depicted in Figure 3. This configuration facilitated the monitoring of each finger's movement by tracking alterations in the resistance values. The stretch sensor's length was customized to span from the metacarpophalangeal (MCP) joint to the interphalangeal (IP) joint for the thumb. For the index, middle, ring, and little fingers, it extended from the MCP joint to the proximal interphalangeal (PIP) joint. The specific lengths of the stretch sensors for each finger are detailed as follows:  
\begin{tabular}{l l l}
1. & Thumb & : 5.5 cm; \\
2. & Index & : 6.5 cm; \\
3. & Middle & : 7.5 cm; \\
4. & Ring & : 6.5 cm; \\
5. & Small & : 6.0 cm. \\ \end{tabular}  
To determine the resistance value of the stretch sensor, the voltage divider method was utilized, as illustrated in Figure 4. A 5 V power supply and ground connection for all five stretch sensors were sourced from the Arduino Nano, with the output voltage from each sensor being individually linked to an analog input pin. This output voltage was subsequently converted to reflect the resistance value of the stretch sensor, allowing for real-time observation of changes in resistance. The Arduino Nano was calibrated to facilitate a sampling rate of approximately 100 Hz for the data collected from the stretch sensors.  
Figure 3: Proposed stretch-sensor glove.
## Materials and Methods  
### Learning from Interaction: \(Q\)-Learning  
Learning from interaction is the fundamental basis of the human nature of learning, and has been computationally explored in the Reinforcement Learning (RL) field. This practice involves a learner and decision-maker agent which takes actions (A) in specific states (S) within an environment and evaluates its behavior based on some reward (R). Additional elements in RL are the policy (\(\pi\)), a mapping that defines how the agent acts from a specific state, and a value function that determines the total amount of reward which can be accumulated over the future [16]. The mathematical formulation of this problem may be address by a Markov Decision Process (MDP) framing with finite state, action and reward sets \(\{S,A,R\}\) for different discrete time steps (\(t\), \(t\)+1...) (Figure 2). Finite MDPs formalize a sequential decision making process by defining these finite number of elements and well defined probability distributions (Equation (1)). In this equation, the definition of probability \(p\) represents the dynamics of an MDP and specifies the probability of state \(s^{\prime}\in S\) and reward \(r\in R\) occurring at time \(t\), given some preceding state \(s\in S\) and action \(a\in A(s)\) values.  
\[p\left(s^{\prime},r\mid s,a\right)\doteq Pr\left\{S_{t}=s^{\prime},R_{t}=r\mid S _{t-1}=s,A_{t-1}=a\right\} \tag{1}\]  
These probabilities characterize the environment's dynamics of an MPD framework able to abstract a goal-directed problem. Specifically targeting the HORUS problem, the selected algorithm was Q-Learning, a model-free RL method that finds an optimal policy by maximizing the expected value of discounted reward over a sequence of steps [17]. The value function is referred to as Q-function and allows to store subsequent Q-values, estimates of cumulative reward in the long run, in a Q-Table [16]. Q-Learning is a time-difference learning approach. This means it updates the value of a state based on the observed reward and the estimated value of the next state. It is a method based on the Bellman equation:  
\[Q(S_{t},A_{t})\leftarrow(1-\alpha)Q(S_{t},A_{t})+\alpha[R_{t+1}+\gamma\underset{ a}{max}Q(S_{t+1},a)] \tag{2}\]  
where \(Q\) refers to the updated Q-value for a specific state \(S_{t}\) and action \(A_{t}\) in a given time step \(t\), \(\alpha\) is the step-size or learning rate, \(R_{t+1}\) is the reward associated to the action performed, \(\gamma\) is the discount factor and \(\underset{a}{max}Q(S_{t+1},a)\) is the maximum expected future reward given the new state \(S_{t+1}\) and all the possible actions \(a\) at that new state.  
Figure 2: Block diagram of the Agent-Environment interaction in a Markov Decision Process [16].
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 3 System Design and Scheduling Problem Definition
### Problem Definition and Objectives  
The scheduling problem involves dynamically assigning jobs to machines while minimizing fault risks, meeting deadlines, and optimizing resource utilization. Each job consists of multiple tasks with predefined precedence constraints; in addition, machines operate under specific limitations, executing only one task at a time. Scheduling must be adaptive to real-time machine conditions to ensure system reliability and efficiency. The objective of this research is to develop an intelligent scheduling mechanism that proactively mitigates fault risks and dynamically reallocates jobs based on evolving machine states. The primary challenges in achieving an optimal scheduling solution include:  
* Fault-Aware Scheduling: Anticipate machine failures using real-time sensor data, such as vibration, temperature, and tool wear, and proactively reassign jobs to reduce the risk of disruption.
* Deadline-Driven Task Allocation: Ensure that all jobs are completed within their specified deadlines while adapting to machine conditions without introducing significant delays.
* Resource Utilization Optimization: Minimize machine idle time by effectively balancing workloads and preventing bottlenecks in task execution.
* Dynamic Machine Adaptation: Continuously adjust job assignments based on real-time machine conditions to maintain operational continuity and prevent excessive wear or downtime.  
By addressing these challenges, our approach enhances the adaptability and robustness of scheduling in dynamic production settings, ultimately improving overall system reliability and efficiency.
## 3 Numerical Results and Discussion
### Dual-Hop  
The first application refers to a regenerative dual-hop relay link, part of a wireless mesh network (WMN). The nodes are denoted \(A,B\) and \(C\) located at a distance \(d\) apart. The system is located in a densely populated indoor environment, as a small or an airport. The average speed of moving objects is considered to be that of pedestrians, as 1.5 m/s, whereas the shadowing decorrelation distance has been chosen 3 m, a value typical for indoor environments. The links are considered balanced, i.e., with the same fade margin.  
In Figure 3 we plot the theoretical alongside the simulated by use of the model outage probability for the case of the decode and forward dual-hop relay. The accuracy with which the model generates the stationary statistical characteristics of second order is remarkable. The efficiency of the model concerning the second order dynamical statistical characteristics is shown in Figure 4 where the theoretically calculated in Section 2.4 average outage duration is plotted along with the generated one for different values of fade margin. The simulated values are in a very good agreement with the theoretical ones. Slight differences are noted for high values of fade margin, which are due to the slow convergence of the simulated AOD for such high values of fade margin. Figure 4 serves also as a validation for the theoretical derivation of the AOD as in Section 2.4.  
In Figure 5, in order to show the impact of the shadowing correlation coefficient we plot the theoretical AOD for three different values of the correlation coefficient. From Figure 5 we see that the impact of the shadowing correlation coefficient is very small concerning the average outage duration.  
Figure 4: Theoretical and simulated average outage duration for a regenerative dual-hop link vs. fade margin.  
Figure 5: Average outage duration for a regenerative dual-hop link vs. fade margin for three different values of shadowing correlation coefficient.  
Figure 3: Theoretical and simulated outage duration for a dual-hop link vs. fade margin.
###### Abstract  
In this paper, the feasibility of an optical fiber-based photoacoustic imaging system for detecting caries lesions inside a tooth is examined. Models of hidden caries were prepared using a pigment with an absorption spectrum similar to that of real caries lesions, and the occlusal surface of the model teeth containing the pigment was irradiated with laser pulses with a wavelength of 532 nm. An examination of the frequency spectra of the emitted photoacoustic waves revealed that the spectra from simulated caries lesions included frequency components in the range of 0.5-1.2 MHz that were not seen in the spectra from healthy parts of the teeth. This indicates that hidden caries can be detected via a photoacoustic imaging technique. Accordingly, an imaging system for clinical applications was fabricated. It consists of a bundle of hollow-optical fibers for laser radiation and an acoustic probe that is attached to the tooth surface. Results of ex vivo imaging experiments using model teeth and an extracted tooth with hidden caries lesions show that relatively large caries lesions inside teeth that are not seen in visual inspections can be detected by focusing on the above frequency components of the photoacoustic waves.  
photoacoustic imaging; hidden caries; hollow-optical fiber +
Footnote †: journal: Applied Physics Letters
## 1 Introduction  
Processing tomato (_Solanum Lycopersicum_ L) is a globally important crop, with a worldwide production estimated at 39 million tons in 2021 [1]. Size, form, color, fruits' dry matter and soluble solids contents are important quality parameters for the processing industry [2]. Furthermore, tomatoes are rich in lycopene, carotenoids, flavonoids, vitamin C, K1, B2, B9, potassium, copper, iron, and phosphorous. For this reason, it has been considered a functional food, which means that its regular consumption may prevent and/or act on certain human diseases [3]. Tomato has a high input and water-intensive growing system, particularly for its high water-demanding: 400-600 mm of irrigation water, rising due to ongoing climate change [4]. However, in recent years, we have witnessed a gradual reduction of water reserves for irrigation in quantity and quality [5]. Among the many causes, it is possible to include ongoing climate change, increased demand for civil and industrial uses, and incorrect irrigation application with consequent waste. Therefore, improving irrigation water use efficiency (IWUE) is one of the major agricultural challenges that modern technologies are helping to resolve [6]. Furthermore, improved IWUE helps achieve sustainability in water use and contributes to increasing agricultural production's competitiveness [7]. In response to global water scarcity, blue water requirement (BWR) can be a useful index. BWR estimates the irrigation consumption per product unit and is particularly interesting with crops that require significant amounts of water that cannot be fulfilled by the scarce rainfall during the crop cycle, and for which, irrigation is crucial [8]. Given these premises, the identification of technological innovations that make it possible to rationalize and optimize irrigation water use appears of great interest. Irrigation scheduling represents a decision-making process that seeks to provide plants with appropriate quantities of water at appropriate times. Despite some on-farm system irrigation schedules are now available, mainly based on soil water balance, the common irrigation scheduling in use in tomato growing systems is still based on fixed intervals between irrigation supplies without considering the actual crop water demand [9], leading to over-irrigation and poor IWUE [10]. Furthermore, to the best of our knowledge, none of the existing irrigation scheduling systems currently incorporate monitoring of both plant water status and soil moisture level. Thus, to optimize the irrigation of processing tomatoes, the aims of this research were: (1) to implement a smart system based on monitoring of both plant water status and soil moisture level, based on the use of wireless sensors; and (2) to compare the smart system with an approach based on evapotranspiration model and an empiric approach managed by the farmer.
## 4 Experimental Results and Analysis
### Ice Thickness Comparison Experiment  
This section conducts a comparative experiment on the calculation of ice thickness on transmission lines and verifies the advantages and disadvantages of the thickness identification algorithm by comparing DABNet and EDPNet. The experimental results are shown in the Table 5.  
As can be seen from the table, the recognition error of EDPNet is below 5%, with the highest being 4.2%. The thickness recognition error of DABNet is higher than that of EDPNet. The experimental results prove that EDPNet can accurately identify the ice thickness on transmission lines, and its predicted thickness is not much different from the actual thickness.
# Predicting Human Location Using Correlated Movements
## 7 Evaluation Results and Discussion
### Performance Comparison of PCM Selection Methods
As a consequence, the BSB method achieves significantly higher prediction accuracy than the STC one with both datasets. However, the performance gap between BSB and STC in the Dartmouth traces is noticeably smaller than that in the MIT one. The gap difference may come from different characteristics of human  
Figure 5: Effects of temporal location features on the prediction model in case of (**a**) the MIT dataset and (**b**) Dartmouth dataset. mobility in two datasets. Recall that the STC scheme does not consider the dependence between human movement and the temporal information. Therefore, if human mobility extracted from a dataset (e.g., the Dartmouth) is not highly related to the temporal data, the STC can achieve better accuracy than the case of strong relationship between human movement and the temporal information. One should note that BSB, SC and STC approaches do not take into account the actual encounter or friendship between people. The experiment results in Figure 5 indicate that friendship-based methods (EFB and CISB) causes the lower performance than other approaches (BSB, SC, and STC) in which friendship is not considered. In addition, the proposed BSB approach achieves the most accurate prediction among the considered PCMs selection methods. For example, using location data of PCMs at time \((t-1)\) and \(t\), the PLP model predicts human locations at time \(t\) with top-1 accuracy of 81.64%, 82.57%, 85.92%, 82.07%, and 92.31% using EFB, SC, STC, CISB, and BSB methods, respectively, in case of the Dartmouth dataset. Also, from the observations in Figure 5, among three considered temporal features, pre-cur-loc and cur-loc lead to quite similar performance in both datasets. For example, in case of the MIT traces, the PLP model achieves 71.13% and 71.00% accuracy in predicting the location of person \(p\) given pre-cur-loc and cur-loc information of PCMs, respectively. As anticipated, using data of PCMs during time \(t\) (cur-loc) can result in a more accurate prediction than the use of \((t-1)\) (pre-loc).
## 3 Materials and Methods
### Thermodynamic Modelling  
Geochemical calculations were made using the computer code PHREQC 2 (USGS). PHREEQC is a computer program written in the C++ programming language that is designed to perform a wide variety of aqueous geochemical calculations. The thermodynamic database Ilnl and PSINA (PSI/Nagra Chemical Thermodynamic Database 12/07) were used for the calculations of uranium migration forms. We calculated the following species of U(IV-VI): U\({}^{4+}\), U(UH)\({}_{4}\), U(CO\({}_{3}\))\({}_{4}\)\({}^{-}\), UOH\({}^{3+}\), U(CO\({}_{3}\))\({}_{5}\)\({}^{6-}\), U(SO\({}_{4}\))\({}_{2}\), USO\({}_{4}\)\({}^{2+}\), UCl\({}^{3+}\), UNO\({}_{3}\)\({}^{3+}\), U(NONO\({}_{3}\))\({}^{2+}\), UO\({}_{2}\)\({}^{+}\), UO\({}_{2}\)(CO\({}_{3}\))\({}_{3}\)\({}^{5-}\)), UO\({}_{2}\)\({}^{2+}\), (UO\({}_{2}\)(CO\({}_{3}\))\({}_{2}\)\({}^{-}\), UO\({}_{2}\)(CO\({}_{3}\))\({}_{3}\)\({}^{4-}\), UO\({}_{2}\)CO\({}_{3}\), UO\({}_{2}\)(OH)\({}_{2}\), UO\({}_{2}\)OH\({}^{+}\), UO\({}_{2}\)(OH)\({}_{3}\), UO\({}_{2}\)SO\({}_{4}\), (UO\({}_{2}\))\({}_{3}\)(OH)\({}_{5}\)\({}^{+}\), (UO\({}_{2}\))\({}_{2}\)(OH)\({}_{2}\)\({}^{+}\), UO\({}_{2}\)CN\({}^{+}\), UO\({}_{2}\)NO\({}_{3}\), (UO\({}_{2}\))(OH)\({}_{7}\), (UO\({}_{2}\))\({}_{4}\)(OH)\({}_{7}\), (UO\({}_{2}\))(OH)\({}_{4}\)\({}^{+}\), (OH)\({}_{4}\)\({}^{+}\), UO\({}_{2}\)C\({}_{2}\), (UO\({}_{2}\))\({}_{3}\)(OH)\({}_{5}\)CO\({}_{2}\), (UO\({}_{2}\))\({}_{2}\)(OH)\({}_{2}\)(HCO)\({}^{+}\), UO\({}_{2}\)(OH)\({}_{4}\)\({}^{-}\), UO\({}_{2}\)SO\({}_{3}\), and (UO\({}_{2}\))\({}_{11}\)(CO\({}_{3}\))\({}_{6}\)(OH)\({}_{12}\)\({}^{-}\).  
Saturation index (SI = logIAP--logKs) values were also calculated. At SI > 0, solid phase formation is predicted, and at SI < 0, dissolution occurs; SI = \(\pm\) 0.3 indicates equilibrium, accounting for the calculation error. The system database allowed the presence of the following phases: urannite (logKs -4.84), UO\({}_{2(\mathrm{am})}\) (logKs 0.11) and other nonstoichiometric oxides and phosphates. All solubility constants were calculated for similar stoichiometry reactions. For the thermodynamic modelling of Samples 1, 2, and 3, a uranium concentration of 10 mg/L was used in the computations.