esa-sceva/satcom-chunk-collection huggingface dataset:
# Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks
## 4 Experimental Results
### Performance Evaluation of WCRNN against State-of-the-Art Neural Network Training Algorithms
#### 4.1.1 Bank Marketing Dataset  
The data were related to direct marketing campaigns (phone calls) of a Portuguese banking institution, and the classification goal was to predict if the client will subscribe to a term deposit. Each observation represented a customer and was described by 17 attributes, both categorical and continuous, corresponding to a total of 4119 contacts. During these phone campaigns, an attractive long-term deposit application, with good interest rates, was offered. For each contact, a large number of attributes was stored and if there was a success (the target variable). For the whole database considered, there were 451 successes (11% success rate) and 3668 failures (89% failure rate). The network architectures consisted of one hidden layer with 10 neurons and an output layer of two neurons, while the error goal was set to \(E_{G}\leq 0.05\) within the limit of 1000 epochs.  
Figure 1 presents the performance profile for the bank marketing classification problem, investigating the performance of each training algorithm. Firstly, we note that both versions of the proposed training algorithm illustrated the highest probability of being the optimal training algorithm in terms of classification accuracy, since their curves lied on the top. More analytically, WCRNN\({}_{1}\) and WCRNN\({}_{2}\) reported 26% and 42% of simulations with the best \(F_{1}\)-score, respectively; while the state-of-the-art training algorithms Rprop, LM, and SCG presented 10%, 18%, and 6%, respectively. Furthermore, WCRNN\({}_{1}\) and WCRNN\({}_{2}\) reported 34% and 42% of simulations with the best accuracy, respectively; while the state-of-the-art training algorithms Rprop, LM, and SCG presented 14%, 16%, and 10%, respectively. Summarizing, we conclude that the application of the bounds on the weights of the RNNs increased the overall classification accuracy, in most cases. However, it is worth noticing that in case the bounds were too tight, this will not substantially benefit the classification performance.
## 3 Results
### [PERSON] (aged 75) lived with her husband and son. She was our only participant to indicate full _Non Acceptance_\({}_{\varnothing}\) of the mobile telepresence robot. [PERSON] was familiar with 'gadgets' like Instacart (Instacart--online delivery service platform that allows users to have items delivered to their homes) and smart clocks (Smart Clock--A wireless clock that can connect to the internet for information). This influenced her opinions on robots as she believed robots are \"tools... that help and do stuff for you\". [PERSON] used the mobile telepresence robot with her interaction partner, [PERSON]\({}_{IP}\). [PERSON] was disappointed that the robot did not have more diverse applications, as she had expected it to assist her in her home with tasks like searching for recipes online while cooking. Throughout her experience with the robot, [PERSON] had trouble operating it on her own. This made her confused and agitated throughout the study. She explained this _Lack of Use_\({}_{\varnothing}\) with her belief that she was experiencing a study manipulation that limited her access to the robot and that she was in a different condition compared to [PERSON], a participant she personally knew. Despite the researchers' best efforts to show [PERSON] how to use the robot and explain that there were no study manipulations, she maintained her beliefs throughout the study. She only used the robot during monthly interview sessions when researchers helped her set up her interaction partner on the robot. She concluded that there were \"no benefits at all from this robot\" as no one could \"access\" it (_Non Acceptance_\({}_{\varnothing}\); Figure 6 & Table 8).
#### 3.6.1. House Tour Interview: Expectation\({}_{1}\) and Adoption\({}_{3}\)  
During the House Tour, [PERSON] had primary peaks of both the **Expectation\({}_{1}\)** (85.71%) and **Adoption\({}_{3}\)** (14.29%) phases.  
[PERSON] began this study viewing robots as \"tools\" that \"have amazing capabilities... [that] are not always positive...\" and that it is a matter \"of how they're programmed and human habit\" (_Attitude Formation\({}_{1}\)_). She was familiar with internet apps and robots from science fiction but knew that it was \"obviously fiction\". She based her expectations of the robot on her Shark robotic vacuum (Shark robotic vacuum--A robotic vacuum cleaner that uses sensors to detect obstacles. This vacuum uses sensors to map floor plans and clean surfaces) (_Association\({}_{1}\)_). Due to her limited knowledge of real-world robots, she expressed _Curiosity\({}_{3}\)_ with this robot as she did not understand \"what it does\" or \"how I would use it\" (_Curiosity\({}_{3}\)_).
# Wireless Power Transfer System Based on Strapping Resonators
###### Abstract  
In this study, a kind of strapped resonator is proposed to deal with high power wireless power transfer (WPT) in microwave regimes. In many specific applications, such as high power microwave wireless power transfer system (WPT), a coil resonator is not suitable due to the frequency limitations. The high cost of the high-permittivity dielectric resonators also limits their application. As a high \(Q\) resonator, the strapped resonator is often used in the anode structure of a magnetron. The field distribution of \(\pi\) and \(\pi+1\) modes allow the system to operate in dual-frequency mode. Numerical simulation and experimental validation show that with a certain distance, the system provides power transfer efficiency of more than 80% and 70% at 630 MHz and 970 MHz, respectively. Compared to the system based on dielectric resonators, the proposed system has higher power capacity. The leakage and radiation loss of the system is also discussed using numerical methods.  
wireless power transfer (WPT); strapped resonator (SR); \(\pi\) mode; power capacity +
Footnote †: journal: Applied Physics Letters B
# Blob Detection and Deep Learning for Leukemic Blood Image Analysis
## 1 Introduction  
Among medicine's branches, hematology studies the diagnosis and treatment of patients with blood or bone marrow disorders. Hematologists perform a wide range of laboratory tests to produce and interpret results assisting clinicians in the diagnosis and treatment of disease. They are related to the blood and the bone marrow to provide direct clinical care to patients. Indeed, in certain circumstances, predicting how the bone marrow may have contributed to a clinical condition may be more important than identifying the patient's hematologic condition. Hematologists generally receive samples of blood smear and examine them, looking for abnormalities; therefore, if they detect the possible presence of pathologies, they perform a bone marrow biopsy and give a diagnosis in a short time.  
Healthy individuals' bone marrow releases only mature and nearly mature cells into the bloodstream. However, diseases, disorders, or deficiencies can induce the bone marrow to release immature or abnormal cells into the bloodstream, affecting the number and type of blood cells produced, their functions, and their lifespan.  
Different tests exist to supervise blood cell proportions and numbers. For example, the Complete Blood Count (CBC) indicates the numbers and types of cells in the peripheral circulation. Pathologists compare cell percentages with the reference ranges to determine if they are present in a reasonable proportion, if a cell type is increased or decreased, or if there are any immature cells. Reference ranges for blood tests are the set of values in which 95% of the healthy population falls. Test results performed over a representative selection of the population help to define them.  
Automatic cell counters or flow cytometry are examples of quick methods to perform CBC automatically. Nevertheless, if they indicate the certain or possible presence of abnormal cells, hematologists realize a blood smear in order to analyze this manually in any case. Although the manual analysis of blood smears depends on the operator's skills, opinion, and tiredness, it is also particularly helpful to categorize and identify pathological conditions through the description of the cells' appearance, as well as any cell abnormalities. The drawbacks are several. Indeed, it is lengthy, repetitive, and error-prone, suffers from the absence of standard procedures, and is subjective because different operators can produce several interpretations of the same scene.  
In this work, we investigate and implement image processing techniques to automatize the blood cells counting procedure, particularly the white blood cells, and classify them as affected by leukemia or not.  
The rest of the paper is organized as follows. Section 2 illustrates the background regarding peripheral blood analysis. Section 3 presents some related works. The proposed method for cell segmentation, counting, and classification is explained in Section 4, while Section 5 discusses experimental results and gives detail about the datasets used. Finally, Section 6 offers conclusions and future aspects.
###### Abstract  
The residue of chestnut processing generates a large amount of waste material, a resource not adequately exploited. The antioxidant and antimuoral properties of cold and hot water extracts from discarded pericarp of four chestnut Sardinian accessions and one marron variety were studied. The antioxidant capacity of the extracts was determined by spectrophotometric and electrochemical tests. The 1,1-diphenyl-1-2-pic-crylhydraz (DPPH) and 2,2\({}^{\prime}\)-azinobis (3-ethylbenzothazoline-6-sulfonic acid) diammonium salt (ABTS) results were highly correlated with each other; likewise, a good correlation was found between Ferric Reducing Antioxidant Power (FRAP) and cyclic voltammetry (CV) values, both based on the direct transfer of electrons. The antiproliferative effect on normal cells (fibroblasts), and on colon (RKO and SW48) and breast (MCF7) cancer cells was evaluated. Additionally, this paper marks the first application of chestnut extracts to investigate their effects on melanoma (B16F10) cells. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide) test demonstrated that temperature and different extraction times significantly influenced the growth of cells, both normal and tumor. The fibroblast growth was significantly inhibited by moderate doses of cold extracts, while the GI\({}_{50}\) values calculated for hot extracts were high, regardless of the accession or cultivar. An even more marked inhibitory action of the cold extracts was observed both on the growth of RKO and SW48 cells and on B16F10 melanoma cells. Otherwise, an extract concentration, both cold and hot, of no less than 243 \(\mu\)g mL\({}^{-1}\) is required to achieve a 50% inhibition of MCF7 cell growth.  
chestnut biodiversity; pericarp; water extraction; antioxidant activity; cancer cells +
Footnote †: journal: Applied
## 2 Materials and Methods
### Feature Extractors
#### 2.5.1 Multi-Layer Perceptron  
The MLP is a fully connected neural network where data are propagated through multiple layers of neurons, each applying a linear transformation followed by a non-linear activation function. In an MLP, the input layer processes raw data (e.g., price, volume, or technical indicators), which are then passed through several hidden layers before reaching the output layer. Each neuron in a given layer is connected to every neuron in the next layer, and the transformation from one layer to the next follows the equation  
\[z^{(l)}=W^{(l)}a^{(l-1)}+b^{(l)} \tag{8}\]  
where  
* \(z^{(l)}\) is the pre-activation value at layer \(l\);
* \(W^{(l)}\) is the weight matrix for layer \(l\);
* \(a^{(l-1)}\) is the output (activation) from the previous layer;
* \(b^{(l)}\) is the bias vector.  
The output \(a^{(l)}\) is then passed through a non-linear activation function \(\sigma\), such as ReLU (Rectified Linear Unit) or sigmoid:  
\[a^{(l)}=\sigma(z^{(l)}) \tag{9}\]  
This layered structure enables the MLP to capture complex dependencies in the data.
## 3 Results and Analysis  
The LAI in the study area was inversed based on the proposed LAI method for ZY-3 MUX, GF-1 WFV, and HJ-1 CCD, and the accuracy of the LAI inversions was validated based on the LAI field measurements of maize crops. The degree of the influence of the spatial resolution on the LAI inversion was analyzed using an upscaling method to determine the LAI differences among different sensors with varied spatial resolutions. The reflectance in the red and NIR bands was compared between ZY-3 MUX, GF-1 WFV, HJ-1 CCD, and Landsat-8 OLI to illustrate the feasibility of using LAI inversion with three Chinese satellite sensors.
## 3 Results and Discussion
### Protein Networking Deciphers Functional Links among the DEPs  
We constructed a protein network of DEPs associated with growth, biofilm, and pathogenesis, and the resulting network revealed different nodes showing connections between proteins within as well as between the relevant pathways (Figure 4). The betweenness centrality analysis depicted dominating nodes and paths in the constructed protein network. Interestingly, most of the DEPs (HflC, Ihfa, ClpP, BtuE, GlnA, Adk, and AckA) identified through quantitative proteomics were principal effectors that modulate the functioning of respective bacterial signaling pathways. Among the growth regulating proteins, Adk, AckA, and GlnA were the main node, which exert a determining impact on the protein network (Figure 4). In bacteria, cellular stoichiometry between Adk, AckA, and GlnA is crucial for the maintenance of the energy budget by invoking alternative ATP-generating pathways, which is an important factor regulating bacterial growth [40]. Similarly, Ihfa, HflC, and MotA (flagellar motor) were the main nodes influencing the biofilm network (Figure 4). Ihfa and MotA are important regulators of the flagella-biofilm network, whereas HflC regulates the differentiation of matrix-producing cells, two of the important steps of bacterial biofilm formation. ClpP and BtuE are the main nodes influencing the bacterial pathogenicity network (Figure 4). [PERSON] et al. [75] demonstrated the regulatory role of ClpP protease in the oxidative stress response by controlling the antioxidative enzymes like catalase and peroxididases, respectively. In bacteria, as in other organisms, physiological processes do not occur in an isolated and exclusive manner. They are part of an interlinked and complex network driven by different metabolic pathways in parallel. Likewise, growth, biofilm formation, and pathogenesis are interrelated phenomena in bacteria; therefore, the machinery regulating these processes might be linked via some regulatory proteins.
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 3 Applications of Swarm Intelligence Algorithms in WSN
### SI Applications in UAV-Aided WSNs  
For WSN, the UAV can assist the communication between sensor nodes and assist the positioning of mobile network nodes. UAVs have been widely applied to the WSNs. To minimize the energy consumption and maximize the network life, it is necessary to determine the optimal flight path of UVAs, maximize the value of collected sensing information, and minimize the total cost of flight time, energy consumption and operational risks of UAV under given environmental conditions.  
Reference [42] proposed a multi-objective utility function to obtain the optimal UAV flight path and a dynamic VSI updating mechanism. This method combines the GA and ACO to select the optimal path from the possible flight path according to sense, energy, time, and risk-utility. The combination of GA and ACO algorithm divided the whole sensor field into cell regions, and first considered sensor information acquisition (SIG) units. The value of sensing information collected by this pathis maximized, and the total cost of flight time, energy consumption, and operation risk of UAV are minimized under given environmental conditions.  
Different from static WSNs, mobile WSNs often have more dynamic topology and lower power consumption ability. The most critical issue in mobile WSNs is the mobile aggregation scheduling problem. Reference [43] proposed a BFO-based cooperative mobile sensing scheme in mobile WSNs using UAV as a mobile sink. The process of _Escherichia coli_ moving in BFO was used to simulate the distributed self-organization and adaptive displacement of the mobile sink. The UAV decided to move in a specific direction according to the process of _Escherichia coli_ flooding. Simulation results showed that this method can reduce energy consumption, reduce delay, and improve the throughput and coverage of network.
# More on Pipelined Dynamic Scheduling of Big Data Streams
## 7 Average Latency  
To study the average latency, we conducted two sets of experiments. In the first set, we try to find how the migrations affect the total latency, so we computed the latency during the migration periods. Again, the overall migration process required from 10 to 100 s, depending on the number of tasks migrated and the corresponding data (tuples) that they processed. We repeated the migration experiment 20 times for clusters of 5-50 and 50-100 nodes (we gradually expanded the network size by adding subclusters) and we regularly computed (every 5 s) the average latency (see Figures 9 and 10).  
The small ups and downs that one can see indicate that, every time we expanded the network (added another sub-cluster) and redistributed the tasks, we got a latency drop for a short period of time, but as more data kept arriving, this small gain was eliminated and the latency increased. As an example, notice, in Figure 10, the large drop of the latency found in our scheme, when we expanded the network size from 85 to 100 nodes, at time \(\approx\) 45 s. This latency reduction lasted for a few seconds. As more and more data streams kept arriving, the latency gradually increased.  
For our strategy, one can notice that the latency increases in a rather smooth way, especially for smaller networks (see Figure 9). As the network size and the number of tasks further increase, so does the latency for all the schemes (see Figure 10). Particularly, [PERSON]'s and [PERSON]'s scheme had almost identical behavior for smaller and larger networks (see the shape of the 2 curves in Figures 9 and 10), but the latency experienced for larger networks was quite larger. This can be explained by the regular timeouts employed in this scheme. Although the MT scheduler optimizes the system performance, it incurs regular and large imbalances when task migrations occur and thus, under such conditions, it experiences the largest latencies.  
In the second set of experiments, we study the overall latency for a period of one hour. The results are shown in Figure 11a,b, for networks of 5-50 and 50-100 nodes. Again, our work outperforms its competitors for two main reasons: (1) the buffering required by our scheme is minimized because the communications are performed in such a manner that each node gets data from only one node at a time. On the long term, when there are large amounts of unprocessed data buffered, the latency increases, and (2) the overlapping of certain communication and processing procedures.  
Figure 8: Overall load balancing comparisons: (**a**) for smaller clusters, (**b**) for larger clusters.
## 2 Literature Review
### IoT Technologies in Health Monitoring
#### 2.1.2 Relevance to Connected Ecosystems  
IoT-enabled solutions facilitate smooth integration and real-time interaction between patients, healthcare providers, and medical devices across many healthcare stakeholders, laying the groundwork for connected health ecosystems [27].  
Critical health and sensitive patient data are continuously gathered by a variety of medical devices and sent via the Internet for further analysis and decision making in an interconnected environment made possible by IoT technologies [28; 29]. Real-time interactions are made possible through the seamless communication between these systems and devices inside the ecosystem, which also keeps patients, physicians, hospitals, and caregivers informed. Through the aggregation of data from several IoT devices, this integration promotes a linked healthcare network and offers comprehensive insights.  
Cloud-based platforms and data integration technologies are used by connected health ecosystems to facilitate the gathering, secure storage, and analysis of patient data from IoT devices [30], improving decision making and enhancing collaboration across healthcare institutions.  
Through the continuous analysis of patient data within the ecosystem and the provision of real-time health insights by means of wearables or applications, IoT-enabled monitoring enhances proactive and personalized care [31]. Furthermore, healthcare practitioners receive early warning alerts from AI-driven analytics, which helps avoid complications and decrease the number of hospitalizations.  
Regardless of geographical location, connected ecosystems in healthcare bring together different stakeholders--hospitals, clinics, patients, and devices--into a cohesive framework that improves collaboration and provides treatment continuity [32]. For enhanced overall efficiency, hospitals deploy IoT systems for equipment optimization, resource management, and patient tracking.  
These ecosystems also contribute to population health monitoring by detecting trends like disease outbreaks or high-risk patients [33], enabling targeted interventions through the utilization of data-driven insights [34].  
Seniors can live independently with personalized care plans and remote monitoring thanks to the seamless integration of smart home health systems and linked wearables into connected ecosystems. Smart home health systems further integrate wearable devices, allowing seniors to maintain independent living with continuous health support [35].
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 2 Processes
### Sorption
A diagram showing the relationship between the amount of sorbed compound per unit mass of solid and the concentration in the solution phase under equilibrium conditions is called an isotherm [188]. Sorption isotherms often exhibit non-linear behaviours (Figure 6). A comprehensive classification is provided in the works of Giles et al. [214; 215] and Voice and Weber [188]. Some models, such as Langmuir [216], BET (Brunauer-Emmett-Teller [217]), and Gibbs, may not be suitable for describing sorption in the water phase. Therefore, only Freundlich and linear models appear suitable for characterizing pollutant reabsorption in the aqueous environment [218; 219]. Given that the sorption process can also immobilize microorganisms (forming biofilms), isotherms can also be utilized to simulate the movement and attachment of microbes to soil particles. For simplicity, microorganism sorption is assumed to be linear. Detailed findings regarding microorganism sorption in porous media are available in the works of Mills [220], Aal et al. [221], and related references. Conversely, organic pollutants may be sorbed by various biological materials in a process termed biosorption [222; 23].