esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Internal Data Markets
## 5 Open Research Questions
### Concrete Data Market Models  
The basic model provided in Section 4.1 is rather abstract. In the case of implementing an internal market, this model needs to be specifically tailored to the needs of the data exchange and the side parameters of the actual ecosystem. This not only includes such issues as integration into the institution's infrastructure and process workflows, but also a specification on the billing, licensing and payment mechanisms (if applicable at all), as well as information on the data, such as the volume, complexity and time constraints. Furthermore, the concrete model also needs to specify what types of pre- and post-processing measures, e.g., fingerprinting, need to be in place, as well as how (and if) versioning of data sets is set up, especially considering that most technical companies already have some kind of versioning in place that needs to be respected in the data market. Using a concrete, specifically tailored model also provides many benefits from a security perspective, especially when the data to be shared are rather specific. The user base can be limited, thus allowing for very concrete measures with respect to access. Furthermore, the knowledge on the data itself can be used to provide additional mechanisms against data manipulation, as well as sanity checks. The main issue here is the generation of the specific models out of a general model and a set of (often unsystematic) requirements. Automating this process would greatly enhance the acceptance of such markets and reduce the setup costs; still, there is a lot of research work to be done, ranging from providing a taxonomy for the potential market owner to define and specify the requirements in a way that allows for automation, down to the automated model generation itself.
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 4 Computational Experiments
### Computational Results
#### 4.2.3 Impact of Valid Inequalities  
We evaluate the separators introduced in Section 3.4 using the same approach as above, comparing the running times and relative gaps for all separators (see Table 3). In terms of the relative gap, the subtour inequalities, as well as their lifted counterparts (see Section 3.4.4) perform best, closing about 25% of the gap of the original formulation. The computational overhead is relatively lower compared to pricing three- or four-cycle-free paths. We evaluated miscellaneous combinations of different separators, finding the combination of lifted subtour and \(D_{k}^{+}\) inequalities to be most efficient.
## 3 Methods for Closed Path Determination  
Basically, there are two strategies to determine a closed path from a given intensity sum map. One is to adaptively extract the closed path along the ridge of the intensity sum map. The reason why we search for the ridge elements is to ensure that all the measured phase slope data along the closed path are valid. Another strategy is to generate a closed path with a perfectly round shape, whose diameter and center could be adaptively varied within a given range. Hence, in this section, we explain closed path determination methods, following each strategy in detail. We introduce three  
Figure 1: Graph illustrating the generalized contour-sum method.  
methods that conform to the ridge-extracting strategy, which are watershed transformation (WT), MAIC extraction, and the combination of watershed transformation and MAIC extraction (WT-MAIC), as well as two methods that conform to the perfectly round circle generation strategy, which are the fixed-center perfectly round circle method (FC-PRCM) and the shifting-center perfectly round circle method (SC-PRCM).
## 2 Results and Discussion
### Optimization of the Extraction Procedure
#### 2.1.2 Central Composite Design
As can be seen, the combined effect of the addition of salt (19.5% NaCl), together with the increase of desorption time (10 min) and extraction time (to 39 min), maximize the response for this compound. Figure 2: Results from the Student’s _t_-test for the main and quadratic effects. Regarding linearity of the response within the range of concentrations presented in Table 3, the correlation factor (R) was higher than 0.995 for all VMSs, except _D3_ and _D6_ and the relative standard deviation of the slope was less than 5%. Two different methods were used to evaluate the limits of detection. For linear siloxanes, the limits of detection (LODs) and quantification (LOQs) were estimated based on a signal-to-noise ratio (S/N) of 3 and 10, respectively. For cyclic siloxanes, they were determined based on 10 lab blanks, i.e., as the concentration of analyte that provides a response equal to the three and ten times their standard deviation, respectively. Nevertheless, these detection limits are still adequate for the intended screening approach using GC-FID. Intra-day precision (repeatability) was evaluated through relative standard deviation (%RSD) of four replicates at different spiking levels (1 ug/L and 5 ug/L), while inter-day precision (intermediate precision) was assessed by determinations of the same sample, analyzed in different days (three days). In both cases, most relative standard deviation values were below 15%, as presented in Table 4. The mean relative standard deviation (RSD) values were about 12% for intra-day precision and about 15% for the inter-day precision, which is acceptable for this type for HS-SPME analysis. are usually expressed as the expanded uncertainty, which is the value of U multiplied by a factor of coverage of 2.
# Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks
## 2 Background
### Coloured Petri Nets  
We chose coloured Petri nets for our approach, which are an extension of classical Petri nets. Petri nets were introduced by [PERSON] as part of his doctoral thesis \"Interaction with Automata\" [30]. The model in Petri nets is an oriented bidirectional graph that contains two types of vertices: places and transitions, which are connected by directed arcs. Places represent state variables, and transitions represent events. The arcs that lead from a place to a transition are called input arcs. Arcs that lead from a transition to a place are called output arcs. In the graphical representation, places are represented by circles and transitions by rectangles (often of different types to visualize varying timing properties). Places can contain tokens modelling objects. Token movement (i.e., state changes) is associated with firing a transition. A transition is fired when its conditions are met. Multiple tokens can be moved in one firing. Places may have a limit (capacity) to the number of tokens they can hold. Petri nets allow for modelling systems containing parallel processes and are widely used for solving application-specific systems design problems.  
There is no distinction between tokens in the basic concept of Petri nets. The use of classical Petri nets for models, in which tokens carry some essential attributes, leads to the cluttering of models, as additional places and transitions have to be deployed to track such attributes (i.e., unfolding). In coloured Petri nets [6; 31; 32], the tokens have values attached to them, which allow for distinguishing the tokens. Individual token types can be defined, similar to a class definition in an object-oriented programming language. Coloured Petri nets can be used to extend the capabilities of timed and hierarchical nets.  
Tokens can belong to different classes, which are distinguished by attributes. Attributes can contain numbers, logical values, text, dates and time. Places can only contain tokens of one class. Transition firing can depend on token attribute values and change them during the firing process. The transition can have different activation and firing modes depending on the input tokens and can contain conditions (guards) that limit the type of tokens that can pass through the transition.
# Mobile Personal Multi-Access Edge Computing Architecture Composed of Individual User Devices
## 3 Performance Evaluation
### Service Delivery Time for the Number of mMECS  
In Figures 10 and 11, the service delivery time is shown with regard to the number of mMECS. For the performance evaluation, we set the task sizes to 3000 and 15000 MI and the results are shown in Figures 10 and 11, respectively.  
Figure 10 shows the service delivery time when the task size is small (3000 MI). Since the proposed scheme uses a user mobile device as MECS, so it is possible to provide a fast service delivery even if the resources of access MECS are in use. Although access MECS can be handled without the help of mMECS due to the small size of computing task processing, it shows that it is possible to provide fast service delivery when the number of mMES is increased.  
On the other hand, Figure 11 shows the service delivery time when task size is large (15000 MI). In the traditional MEC architecture, access MECS, which is delegated a large size of computing task, cannot process other computing tasks until the completion of the delegated computing task. However,  
Figure 11: Service delivery time (Task sizes: 15000 MI).  
Figure 10: Service delivery time (Task sizes: 3000 MI).  
On the other hand, Figure 13 shows the service delivery time when the mobility preference level is high. In case of frequent movement of mobile users, it is impossible to provide fast service delivery because additional operation due to frequent movement and computation result should be received from previously located access MECS. However, the proposed scheme uses a mobile device existing in access MECS as mMCES, so it can provide faster service delivery than existing architecture in a new location. In addition, as the number of mMECS increases, the computing power increases, so it is possible to provide a fast service to mobile users.
## 2 Physical Experiments and Results  
### Physical Models, Monitoring Devices, and Experimental Procedures  
[PERSON] _et al._[6] highlight the advantage of physically-based models: using minimal historical data to produce meaningful results. Thus, a trend of developing and testing physically-based soil erosion models appears [7; 8; 9; 10; 11]. In our study, a slope model is constructed in a flume device made of Plexiglas (Figure 1). The slope inclination is 34\({}^{\circ}\) and the material is from Fengdu Mountain, near to Yangtze River Bank, Chongqing, China, whose surface soil can be easily eroded under heavy rainfall. The soil was air-dried and crushed to different test samples having three particle size distributions, as shown in Figure 2. The soil sample was squeezed by a plate with a 120 N force to form the slope. In order to reduce the \"boundary effect\", we used a covering of polytetrafluoroethylene (PTFE) whose friction coefficient is 0.04 on both sides of the flume. In Figure 1, \(m\) is the moisture transducer (frequency domain sensor) [12] whose contact area with the soil is less than 20 mm\({}^{2}\) (measuring range 0%-100%, resolution 0.1%, deviation \(\pm\)2%); \(p\) is the pore water pressure transducer (diameter 3 cm, height 1.6 cm, measuring range \(\pm\)10 kPa, deviation \(\pm\)0.2%); \(d\) is a channel for drainage; one high-definition digital video camera (5 million pixels, position in Figure 1) is used to record the changes of the eroded soil area (front scene). Every test is conducted under similar initial geometry, and pre-added water content (deviation \(\pm\)3%, average value \(\sim\)5.2%). Persistent rainfall is averagely simulated by three nozzle spray heads (\(\sim\)0.6 m high to slope surface) in five rainfall events. For soil Sample 1, we used three rainfall intensity levels in three experiments (approximately 25, 45, and 65 mm/h, respectively) to investigate the effect of different rainfall intensities on the same material. Then we focused on the effect of the different materials (Sample 1, Sample 2, and Sample 3), by comparing their responses in experiments with a common rainfall intensity of 45 mm/h.  
Figure 1: Geometry of physical model.  
Figure 2: Particle-size distribution curves.
## 3 Methods and Data
### Construction of the WaveNet Network Model
#### 3.2.1 Residual Block
The mapping relationship in the residual block can be succinctly represented as follows:  
\[y=F(x,W_{i})+W_{s}x \tag{3}\]  
In this context, we utilize the symbols x and y to represent the input and output feature maps of the residual block, respectively. The primary objective of the residual block is to acquire knowledge of the residual mapping function \(F(x,W_{i})\), with \(W_{i}\) denoting the set of parameters involved in the learning process. To facilitate the integration of shortcut connections and ensure dimension alignment, a 1 \(\times\) 1 convolutional layer is introduced, with parameters \(W_{s}\) responsible for managing dimension adjustments. These pivotal steps within the residual blocks of WaveNet effectively facilitate the extraction of essential features and maintain stable gradient flow during training. Consequently, this simplifies the training of deep neural networks by addressing challenges related to vanishing and exploding gradients.
## 4 Application
### Pumping Test Results And Analyses  
#### 4.3.1 Pumping Test Scheme  
Field pumping tests were performed prior to the excavation to assess the effectiveness of massive jet-grouted bottom sealing barriers to groundwater control for the excavation. Figure 10 displays the location of all the pumping wells and observation wells installed inside and outside the excavation, respectively. 23 pumping wells (labelled W1 to W23) were screened at the depth of 24-25 m BGS in three excavation zones (West zone, Central zone, and East zone). A total of 21 observation wells (labelled Q1 to Q14 and S1 to S7) were screened at different depths to measure the drawdown outside. Table 4 illustrates the detailed procedure of the field pumping tests.
## 4 Discussion
### Uncertainties in Parameter Sets in the MODIS GPP Algorithm  
We adjusted the key parameter \(\varepsilon_{max}\) in the models that based on the MODIS GPP algorithm (LUE and LUE-SW) at site level. The values in LUE-SW were greater than those in LUE on average, but both were not significantly different from the default values except for CA-Oas, DK-Sor, CA-Mer and AT-Neu, for which the value of \(\varepsilon_{max}\) was greater than 1.3 gC\(\cdot\)MJ\({}^{-1}\) (Table 2). These sites include the biome types of shrub, grassland and deciduous forest. Overall, the optimized \(\varepsilon_{max}\) values in both models were in the range of 0.55-2.8 gC\(\cdot\)MJ\({}^{-1}\), as reported by [PERSON] _et al._[92], who calculated the global gross radiation use efficiency from the data provided by 35 EC flux sites and the MODIS /PAR data. Many studies have optimized \(\varepsilon_{max}\) in the MODIS GPP algorithm based on site data [14, 27]. Those results have also demonstrated that the values for shrub and grassland were larger than those in the look-up table, and \(\varepsilon_{max}\) reached \(\sim\)1.56 gC\(\cdot\)MJ\({}^{-1}\) for deciduous forests [93].  
Our values of \(\varepsilon_{max}\) are acceptable, but an overestimated \(\varepsilon_{max}\) could be induced by an underestimated _f_PAR value [14]. In this study, _f_PAR was calculated based on a function of LAI and \(k\) (Equation (2)). Although we corrected the input LAI based on the site-measured LAI\({}_{\mathrm{max}}\) (Table 1), uncertainties in the change of LAI with phenology still existed at the site level because the monthly data were extracted from a global LAI map [4]. In Equation (2), we simplified the \(k\) as 0.5 across a range of biome types. However, this value is dependent on the leaf distribution [94] and the zenith angle, which ranged between 0.3 and 0.6 [95]. The best estimates of \(k\) would be derived from stands with minimal clumping for each site [94,95].
## 4 Results and Discussions
### Current Distribution  
The surface current distribution with and without the inclusion of the decoupling structure is depicted in Figure 11. The current distribution between the array elements when one antenna is energized, and the other antenna is terminated by matching impedance was examined for both situations. From Figure 11a, a strong coupling could be observed between the arrays on the common ground plane that propagates to the non-energized antenna 2 on the right-hand side. The surface current density (J\(-\)surface) in antenna 2 revealed the undesirable coupling induced by excited antenna 1. To illustrate the effects of the MEBG structure, the antenna was then simulated at the same frequency of 6.85 GHz (Figure 11b). With the MEBG decoupling, it is evident that very little surface current density is flowing towards antenna 2. The MEBG has successfully prevented a substantial portion of the current that would otherwise propagate undesirably to antenna 2. MEBG, therefore, could considerably improve the isolation of MIMO antenna systems.  
Figure 10: MIMO\(-\)UWB antenna S\(-\)parameter (a) S\({}_{11}\), (b) S\({}_{12}\).
# Automatic Digital Modulation Classification Based on Curriculum Learning
## 5 Conclusions  
In this paper, we reported a novel automatic digital modulation classifier called the MentorNet classifier, which consists of two neural networks: StudentNet and MentorNet. The MentorNet supervises the training of StudentNet to overcome the overfitting in the classification process. In order to verify the performance of this classifier, several comparative tests with other classifiers were conducted in the presence of AWGN, Rayleigh fading, carrier frequency offset and phase offset. Experimental results showed the accuracy of the MentorNet classifier and Baseline classifier was much higher than the Inception classifier and classifier based on SVM, which suggests the deep residual network is suitable for digital modulation classification. Meantime, the accuracy of the MentorNet classifier at high SNR was higher than that of the Baseline classifier, indicating the curriculum learning can solve the overfitting of the neural network. In the interference of Rayleigh fading, the MentorNet classifier still owned the highest accuracy, which ranged from 80%-90% at 10 dB SNR as the Doppler frequency shift was within 0-10 kHz, which suggests the outstanding robustness of MentorNet classifier. When the carrier frequency offset and phase offset were taken into account, the accuracy of the MentorNet classifier presented quite different tendencies. When only the carrier frequency offset was considered, the accuracy of the MentorNet classifier showed a smooth reduction from 98% to 85% with \(\Delta f/f_{s}\) ranging within \(1\times 10^{-4}\) and \(2\times 10^{-4}\), while it maintained at 99% in the presence of a 0-10\({}^{\circ}\) phase offset. Moreover, the proposed classifier could also achieve favorable classification performance for analog baseband signals, indicating the transplantation feasibility of the proposed classifier. Although the proposed MentorNet classifier had outstanding performance, when SNR was \(-\)20 dB the classification accuracy remains to be improved.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed analysis tools; and [PERSON] and [PERSON] wrote the paper.  
This research was funded by the China Postdoctoral Science Foundation (2018M633471), the National Natural Science Foundation of Shaanxi Province under Grant (2019Q-270), the AeroSpace T.T. and C. Innovation Program and the China Scholarship Council (201806965054).  
The authors declare no conflict of interest.
# An Ultra-Low Temperature Transcritical R744 Refrigeration System for Future Detectors at CERN LHC
### Modular R744 Chiller  
To address the high level of reliability and large amplitude of cooling capacity to be requested throughout the lifetime of the detector, a novel modular design for the R744 chillers has been proposed [11].  
Figure 1: Overview of ATLAS and CMS cooling system pipeline connections and distribution.  
Inside the dedicated buildings it is foreseen the installation of a group of interconnected vapour-compressing modules (compressor slices) as illustrated in Figure 2, with dedicated air gas coolers and a common equipment module with water gas cooler and receiver.  
Each compressor slice is designed to cope with the vapour-compressing and heat rejection needs for a given cooling capacity request and will solely operate in case of need. Decoupling the whole unit into independent modules enables the possibility for the maintenance teams to intervene with no risk of perturbing the process.  
In case of an unexpected shutdown of the electrical network, the system needs to keep a minimum cooling of the detectors while it evacuates the remaining heat (minimum requirement of \(50\,\mathrm{kW}\)). With a modular scheme, it will be possible to plug the power cabinets of a few compressor slices to a diesel power supply units, which is quite an advantageous feature in terms of cost and footprint for the whole installation.  
The R744 liquid supply to the services cavern and the return of evaporated gas to the compressor slices is established via long pipes with no thermal insulation and hence, at ambient temperature. ATLAS and CMS final units will share the same design but in the number of compressor slices modules and the dimensions of connection pipelines [11].
# Cognitive Similarity-Based Collaborative Filtering Recommendation System
###### Abstract
Besides, the system needs to respond immediately to online requests and make recommendations to all users regardless of their purchase and rating history, thus requiring high scalability. * **Sparsity**: In fact, many commercial recommendation systems are used to evaluate very large sets of products. Therefore, the user-item matrix used for collaborative filtering will be very sparse, and the performance or predictions of CF systems are challenged. In several situations, specifically, the cold start problem occurs when having a new user/item in the system. It may not find the similar user/item because there is not enough information (it is also called the new user problem or new item problem [9,10] ). Besides, neighbor transitivity is another problem with sparse databases. The users with similar preferences may not be identified if they have not rated any of the same items. So, it will reduce the effectiveness of a recommendation system based on comparing users in pairs to make predictions. This paper has three primary research contributions: (i) propose a cognitive similarity approach and collect the real cognitive similarity data based on a crowdsourcing system (called OurMovieSimilarity) [11]. (ii) formulation of a pre-computed model (the three-layered  
Figure 1: The Collaborative Filtering process. architecture) of cognitive similarity to extract the cognitive similarity from users. (iii) proposed the cognitive similarity-based collaborative filtering recommendation system. In particular, we create a crowdsourcing system [11; 12; 13; 14] to collect the cognitive data from the user. Then, we propose the three-layered architecture [15] to extract the cognitive information from the user. Our architecture is bottom-up and structure made of three superposed networks that are strongly linked:  
* **User network** relating users on the basis of explicit from the cognitive network. * **Cognition network** relating cognitive similarity between users based on selecting items similarity. * **Item network** relating items based on the basis comparing features extracted from them. The remainder of this paper is organized as follows. In the next section, we present some of the research related to user-based collaborative filtering. In Section 3, we present a definition of cognitive similarity and propose the three-layered architecture to extract the cognitive similarity. We present the recommendation system based on cognitive similarity in Section 4. The details of our experiment, data set, evaluation, and result will be provided in Section 5. In the final section, we provide concluding remarks and directions for future works.