esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Bladder cancer is the most common malignancy of the urinary tract. Approximately 70% of bladder cancer cases are classified as non-muscle-invasive bladder cancer (NMIBC, tumor with T1 or a lower stage) [1]. Patients with low-grade NMIBC are usually managed conservatively with transureth resection of the bladder tumor (TURBT) with or without intravesical therapy, and they show favorable survival outcomes. However, patients with high-grade NMIBC and muscle-invasive bladder cancer (MIBC, tumor with T2 or a higher stage) require more intensive therapies, and according to the European Association of Urology guidelines [2], those with high-grade NMIBC are recommended to undergo re-TURBT to rule out possible MIBC or residual disease, followed by intravesical Bacillus Calmette-Guerin instillation because of the high risk of recurrence and progression. The standard of care for patients with non-metastatic MIBC is radical cystectomy with perioperative systemic chemotherapy [3]. Despite intensive therapies, these patients are at a high risk of recurrence or metastasis, with a 5-year overall survival rate of approximately 50% [4,5]. Thus, accurate assessment of the presence of MIBC and high-grade disease is essential for the appropriate management of patients with bladder cancer.  
In clinical practice, TURBT specimens are used for the evaluation of the histological grade and T stage. However, fragmentation of tumor tissue and the effects of thermal cauterization hamper accurate histopathological diagnosis. Indeed, an understaging rate of 48%was reported among patients with clinical T1 disease who underwent radical cystectomy [6]. Hence, there is an unmet need for diagnostic methods other than TURBT-based histopathological examination in which MIBC and high-grade disease are accurately detected.  
In current clinical practice, multiparametric magnetic resonance imaging (mpMRI) is the most accurate imaging modality for T staging of bladder cancer [7]. Recently, the Vesical Imaging-Reporting and Data System (VI-RADS) score utilizing mpMRI was introduced [8; 9]. Validation studies have demonstrated the high accuracy of the VI-RADS score in diagnosing MIBC [10; 11]. Among the imaging protocols that employ mpMRI, diffusion-weighted MRI (DW-MRI) is a functional imaging technique that allows quantification of the diffusion of tissue water molecules [12]. The apparent diffusion coefficient (ADC) quantitatively expresses the degree of diffusion. We previously reported that the ADC is a biomarker that reflects the histological grade and clinical T stage of bladder cancer [13; 14]. Several other groups have also evaluated the association of ADC values with the histological grade and T stage of bladder cancer [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26].  
In this study, we performed a systematic review of all published studies to assess the role of ADC values in differentiating MIBC and high-grade bladder cancer from NMIBC and low-grade bladder cancer, respectively.
## 5 Common Systems
### Data Acquisition
#### 5.3.3 Dispatching Layer  
The Dispatching Layer is a cluster of machines that take care of retrieving and providing data from all data sources except the primary experimental station 2D detectors. This layer shields all the data sources from other clients and acts as the single point of access to live data. Via this layer clients can subscribe to customized and synchronized data streams of channels from any part of the facility. Clients can request channels in various frequencies and combinations. This architecture allows clients to retrieve any combination of channels regardless of their origin as if they came from a single source, i.e., the data of a given machine pulse of all requested channels is in the same message alongside the pulse-id and other global metadata. The synchronization of data is close to real time and data emitted can be used for immediate online monitoring, data analysis and processing. Requests and calls to this layer are done via a central REST API. Data streams provided by the layer are based on ZeroMQ.
# Haar Wavelet-Based Classification Method for Visual Information Processing Systems
## 4 Discussion  
This article is a significant extension of [30] with further details regarding classification by training and determining a set of nested coefficient value ranges for separating surfaces with a reduced number of coefficient ranges using the Haar \(WT\) only. Moreover, by choosing the search parameters--error \(\delta_{1}=0.1\) and interval \(\gamma=0.7\)--one range is obtained for \(c_{0}\) and two ranges for \(c_{1}\). This reduced the time taken determining the coefficient ranges \(c_{0}\) and \(c_{1}\) as 0.08 s and 0.1 s, respectively.  
Thus, the proposed method enables us to increase the performance of determining sets of coefficient value intervals during the training of the classifier, which is verified by the results of experimental investigations.  
In addition, the authors proposed the use of the Shannon entropy estimates to increase the performance of the procedure for reducing the feature space for identification. Moreover, the evaluation of intermediate classification results is simplified by visualizing the dependence of informativeness and avoiding the estimates of systems efficiency in the multidimensional space [11; 12; 13].  
Summarizing, the main advantage of the proposed method over that proposed in [28] is the defining of the set of intervals where the value of coefficients is located, which are separating the classes of surfaces with the higher performance. At the same time, we note that the performance in the applied task depends on the size of the training sample and the location, which, along with the compactness of data clusters in the feature space, determine the form of the objective function.  
In addition, the number of intervals (and the performance, respectively) is related to the choice of error \(\delta_{1}\) and interval \(\gamma\). However, this kind of research is usually carried out when selecting classification methods and/or adapting them to the new application areas.  
The authors plan to expand the application area of the proposed method for the analysis and processing of non-stationary periodic biomedical signals, such as electrocardiogram signals [34], because the methods selected for classification in this application area have high requirements for both noise immunity and efficiency, as well as operational efficiency.
## 2 Problem Formulation
### Flexibility Potential Pricing Mechanism  
Three typical pricing mechanisms are generally used to encourage the DR of prosumer aggregators in the distribution system. While TOU pricing is generally obtained from markets, incentives can be calculated via market clearing or set as constant parameters.  
RTP, which is set as a time-varying parameter, is another pricing mechanism used to facilitate DR experimentation. Previous studies have shown that the above price-based DR and incentive-based DR are popular in peak shaving, since the man-made pricing mechanisms can directly execute the regulation measure of system operators. However, these pricing mechanisms have no considerations for the overall supply-demand level and prosumer-operator interactions. Even though the end-users can benefit from the pricing mechanisms financially, the overall system load profile cannot be flattened. Ideally, it would be possible or technically possible to repeatedly simulate each flexible resource in order to calculate the exact flexibility price. However, this is a machine-intensive process and cannot be put into use practically.  
To facilitate the prosumer-operator interactions, a flexibility potential pricing mechanism is alternatively developed. Once positive and negative flexibility are quantified in Section 2.1 from the aspects of time and power, these can be aggregated to form the overall flexibility potential of prosumer aggregators. While previous mechanisms use the man-made constant/time-varying parameters to price the resource flexibility, the flexibility potential pricing mechanisms intend to reflect the high degree of scarcity and the real-time supply-demand within the distribution system. Depending on the real-time supply-demand, the more resource flexibility there is, the more reward it will obtain. Basically, the flexibility potential prices are positively correlated with the flexibility \(e_{n,flex}\) of each aggregator. Inspired by the pricing function [16], an increasing and strictly convex quadratic function is adopted in order to calculate the flexibility potential pricing \(pr_{n,flex}\):  
\[e_{n,flex}=\sum_{t\in\{1,...,T\}}\sum_{j}P_{j,t,n,flex}\cdot\lambda_{j,t,n,flex} \tag{38}\]  
\[pr_{n,flex}=a\cdot{e_{n,flex}}^{2}+b\cdot{e_{n,flex}}+c \tag{39}\]
# Application of Blockchain Technology in Emergency Management Systems: A Bibliometric Analysis
## 5 Discussion
### Main Findings
This includes applications in participatory IoT for safety and the management of critical infrastructure. * Cluster #8 showcases blockchain's role in creating more resilient and secure IoT networks by eliminating central points of failure, particularly vital in healthcare IoT and sensor networks crucial for emergency monitoring and response. Despite the significant transformative potential, several challenges continue to hinder the widespread adoption of blockchain technology in emergency management:
* **Limited global collaboration and standardization**: As highlighted in Section 4, the research landscape still exhibits limited global collaboration at the institutional and author levels. This lack of unified effort can impede the development of standardized protocols and interoperable solutions crucial for effective cross-jurisdictional emergency response. * **Gap between theory and real-world implementation:** While theoretical frameworks and pilot projects demonstrate promise, there remains a significant gap in large-scale real-world deployments and comprehensive case studies that validate the effectiveness and scalability of blockchain-based solutions in diverse emergency scenarios. * **Scalability and interoperability issues:** As identified as a key future research direction, current blockchain technologies often face challenges in terms of scalability and interoperability between different platforms and legacy systems. Addressing these technical limitations is crucial for seamless data exchange and collaboration among various emergency management stakeholders. * **Regulatory and governance uncertainties:** The lack of clear regulatory frameworks and governance guidelines surrounding blockchain adoption in emergency management creates uncertainty and can impede investment and widespread implementation. Issues related to data privacy, security, liability, and compliance need to be addressed. * **Social and ethical considerations:** Integrating blockchain into emergency management systems raises important social and ethical considerations, including digital inclusion, data bias, algorithmic transparency, and potential unintended consequences, which require careful consideration and mitigation. Building upon the current state and acknowledging existing challenges, several frontiers for future research emerge as critical to fully realizing blockchain's transformative potential in this field: interdisciplinary integration and technological convergence; development of scalable and interoperable solutions; establishing clear regulatory and governance frameworks; prioritizing real-world pilot implementations and case studies; addressing social and ethical implications; and fostering global collaboration and knowledge sharing, all of which are discussed in detail in Section 5.2.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
###### Abstract  
In mobile edge computing, a set of edge servers is geographically deployed near the mobile users such that accessible computing capacities and services can be provided to users with low latency. Due to user's mobility, one fundamental and critical problem in mobile edge computing is how to select edge servers for many mobile users so that the total waiting time is minimized. In this paper, we propose a multi-user waiting time computation model about composite services and show the resource contention of the edge server among mobile users. Then, we introduce a novel and optimal Multi-user Edge server Selection method based on Particle swarm optimization (MESP) in mobile edge computing, which selects edge servers for mobile uses in advance within polynomial time. Extensive simulations on a real-world data-trace show that the MESP algorithm can effectively reduce the total waiting time compared with traditional approaches.  
mobile edge computing; multi-user edge server selection; resource contention; total waiting time +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 184; doi:10.3390/611090184
# Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols +
## 5 Results
### Gini Coefficients  
As introduced in Section 4.1, we compute the Gini coefficient for both the distribution of block rewards and the hashing power distribution. If a selfish mining attack is successful, the block reward concentration should exceed that of the hashing power distribution. In a scenario in which selfish mining is more profitable, the selfish miner will earn block rewards in excess of her fair share, implying that block rewards get relatively more concentrated in the selfish miner. Thus, with profitable selfish mining, the Gini coefficient of block rewards should exceed the Gini coefficient of hashing power.  
From Figure 2a we know that for \(\alpha=0.4\) selfish mining is always profitable, which is reflected in Figure 8a by the fact that block rewards are more unfairly distributed than the hashing power, and vice versa for \(\alpha=0.2\). As expected, the wealth inequality level remains relatively constant when there is no selfish mining activity (\(\alpha=0\)). The reason why the Gini coefficients (for non-zero \(\alpha\) values) are not \"stabilised\" yet for small \(\lambda_{nd}\) is due to a lack of variation in \(w_{i}\) as defined in Equation (6). For very slow block propagation rates (small \(\lambda_{nd}\)) the blockchain consists of a high share of orphan blocks, as we have seen in Section 5.2. Thus, the share of miners that have found a block finalised in the main chain is very low.  
Similarly, Figure 8b shows that for reasonable block propagation rates (i.e., block diffusion time is less than the expected block creation time), the concentration of block rewards is growing more quickly than that of hashing power. The thresholds for when wealth concentration surpasses mining power concentration mimic the fair share threshold levels found in Figure 2b.  
In summary, when selfish mining is relatively more profitable, the block reward concentration increases, and vice versa.
# Chemical Compositional Changes in Over-Oxidized Fish Oils
## 3 Results
### Fatty Acids  
The relative abundances of a number of saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA) were determined to gain a better understanding of the relative susceptibility of both oils to oxidation. To that purpose, several fatty acids were measured using an official US pharmacopeia method (expressed in area %) in both hoki liver oil and anchovy oil over the 30-day period (Table S1). This method did not include several LCPUFA, including arachidonic acid (AA), EPA, and docosapentaenoic acid (DPA) n-3, and hence, it under-reports total PUFA content. Based on the fatty acids that were measured, hoki liver oil had relative proportions of \(31.4\pm 0.5\%\) SFA, \(48.4\pm 0.7\%\) MUFA, and \(17.5\pm 0.2\%\) PUFA, whereas anchovy oil contained \(53.9\pm 0.2\%\) SFA, \(23.2\pm 0.3\%\) MUFA, and \(22.9\pm 0.1\%\) PUFA (mean \(\pm\) S.D.; \(n=3\)) (Figure 3e). Hoki liver oil had a higher content of monounsaturated fat compared to anchovy oil, whereas the opposite was seen for saturated fat. The relative levels of the major fatty acid classes did not change over the course of the incubations (Figure 3f).  
The fatty acid with the highest abundance in hoki liver oil was oleic acid (C18:1n9) (\(\approx\)23%) (Table S1), which matches a previous published study [59]. C16:0 had the second highest abundance (\(\approx\)16%), which was followed by C22:6n3 (\(\approx\)12%). The fatty acid with the highest abundance in anchovy oil was palmitic acid (C16:0) (\(\approx\)18%), which matches the level reported [60]. C22:6n3 had the second highest abundance (\(\approx\)15%) followed by C22:0 (\(\approx\)12%) and oleic acid (11%). The levels of the PUFA linoleic acid (\(\approx\)1% in hoki liver oil and 1.7% in anchovy oil), C18:3 n-6 (\(\approx\)0.4% in hoki liver oil and 1.0% in anchovy oil), and \(\alpha\)-linolenic acid (ALA) (\(\approx\)0.1% in hoki liver oil and 0.4% in anchovy oil) remained constant throughout the 30 days under both oxidation conditions (Table S1).
# Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles
## 3 Our Method
### Law Article Distinction Extractor
#### 3.3.2 Law Article Groups' Distinct Learning Operation  
The graph constructed in the previous step could distinguish the different law article groups, but distinguishing similar law articles in one group is the critical problem. In order to better distinguish the similar law article in the same group, we propose a law distinction learning operation inspired by the graph attention network (GAT) [27]. Our approach differs from GAT in that we use an attention-based mechanism to remove the similarity information between each law article node. This operation can preserve the unique feature information of each law article and thus can distinguish similar law articles. The specific formula is as follows:  
\[V_{L_{i}}^{(l)}=W^{(l-1)}V_{l-1}^{L_{i}}-\frac{1}{K}\sum_{k=1}^{K}\sum_{j\in N _{i}}\alpha_{ij}^{k}W^{k}V_{j} \tag{7}\]  
\[\alpha_{ij}=\frac{exp(LeakyReLU(\beta^{T}[VV_{i}||VV_{j}]))}{\sum_{j\in N_{j} }exp(LeakyReLU(\beta^{T}[VV_{i}||VV_{j}]))} \tag{8}\]  
where \(V^{(l)}\) refers to the representation of law article \(L_{i}\) in the l-th layer; \(N_{j}\) refers to the neighbor set of \(L_{i}\) in graph \(G\); and \(W_{l}\) and \(W_{k}\) are the trainable self-weighted matrix and the neighbor similarity extracting matrix, respectively. \(\alpha_{ij}\) indicates the importance of node \(j\) for node \(i\) and node \(i\) must be the first-order neighbor of node \(j\). In calculating the attention value between nodes, masked attention is used to achieve the above assumptions. The attention mechanism used in this paper was implemented by a single-layer feedforward neural network, in which the activation function was the \(LeakyReLU\) function. The GAT also incorporates a multi-headed attention mechanism, with \(K\) representing K heads in the formula.  
Then, we used the last layer of the network as a vector representation of the law article nodes, denoted as \(V^{(l)}\). This vector contained information about the different points of similar law articles. For each of the \(N\) groups, which were divided in the law article graph, we computed the representation vector separately, with the following equation:  
\[h_{n}=W_{d}V_{L_{i}}^{L}+b^{(d)} \tag{9}\]  
where \(W_{d}\) is a trainable parameter matrix, \(n\in N\) and \(h_{n}\) represents the vector representation of the \(n\)-th law article group.
# Microbiological Follow-Up of Bioreactor-Assisted Must Alcoholic Fermentation by Flow Cytometry
## 1 Introduction  
Bioreactors, which allow for the continuous control of the main alcoholic fermentation (AF) parameters, represent a useful tool in experimental winemaking to guide in-cellar experimentation at a higher scale. The main parameters monitored by bioreactors include temperature, pH, oxygen concentration and pressure in the fermentation chamber. Variations in these parameters have important effects on the fermentation process, as shown by several researchers. For example, [PERSON] et al. [1], studied the pH variation in an artificial must and observed that low initial pH impacted the outcome of fermentation in terms of sugar consumption, biomass production and final alcohol content. Similarly, [PERSON] et al. [2] showed that high temperatures boosted the fermentation rate but could have profound effects on yeast metabolism and final aroma profile. Finally, [PERSON] et al. [3] observed that increased pressure during fermentation led to a higher concentration of esters, usually associated with the fruity attribute in wine. Changes in these parameters primarily affect the metabolism and biology of the yeast responsible of fermentation, including both _Saccharomyces cerevisiae_ (SC) and _non-Saccharomyces_ species. Thus, it is of utmost importance to have a method in place that allows the dynamics of yeast growth to be tracked during bioreactor-assisted fermentation. Such a method should not only give information about the viability of the yeast but also provide functional details that, in turn, could be correlated to the physico/chemical parameter(s) under investigation (e.g., pH, temperature or pressure). Moreover, this method should provide results in a timely manner, to allow for consistent follow-up after experimentation. One of the best candidates to fill this role is flow cytometry (FCM), a single-cell, fluorescence-based technique, widely used in biomedicine [4]. FCM has been used to evaluate the growth of a microorganism of interest in bioreactor-assisted fermentation in various fields including antibody [5], bioethanol [6], and amino-acid production [7].  
In winemaking, FCM has been applied to follow AF at the bench scale [8, 9, 10, 11, 12]; however, to the best of our knowledge, there is a limited amount of data on FCM as a method to follow the yeast growth in bioreactor-assisted fermentation [13]. Thus, we aimed to perform a 20 L-vinfication trial by means of an automated bioreactor and to evaluate the yeastgrowth dynamics using FCM. We applied and compared two different staining protocols to measure the yeast's physiological state. Moreover, we performed FCM data analysis in a classical operator-dependent manner, as well as using an unsupervised software algorithm, to capture all the complexity within the sample in a single two-dimensional (2D) map.
## 3 Proposed Fires Monitoring and Detection Framework  
In this section, we describe the proposed WSN-based fire monitoring and detection system. After reviewing the literature, we summarize that the WSNs should have the following features to be able to successfully monitor and detect forest fires:  
1. A WSN deployed in forest environment should consume energy very efficiently and energy consumption also should be balanced fairly among nodes.
2. It should have the capability to detect a forest fire as early as possible and to estimate the fire location accurately.
3. It is important for firefighting to be able to forecast the spread direction and speed of forest fires.  
We aim to design a framework that incorporates the aforementioned features. Besides that, there may be some other crucial requirements for WSNs for forest fire monitoring and detection such as self-healing, self-organization and security issues. However, they are out of scope of this work. In this paper, the proposed framework involves three main parts: (i) sensor deployment scheme; (ii) a clustered hierarchy network architecture; and (iii) intra-cluster and inter-cluster protocols.