esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks
## 3 Problem Modeling
### Link Weight Characterization
Thus, the load accumulation factor \(\delta_{u,v}=\dfrac{\mu_{u,v}-\mu_{\text{min}}}{\mu_{med}-\mu_{\text{min}}}\) can be defined to characterize the influence of the link load on the link weight. The link cost \(\cos t_{u,v}^{tns}\) for non-time-sensitive services when calculating paths is:\[\cos\mu_{u,v}^{ns}=(1+J_{u,v}+\delta_{u,v})\times delay_{u,v}^{ns},u\
eq v \tag{8}\]  
Here, \(delay_{u,v}^{ns}\) represents the propagation delay between satellite nodes \(u\) and \(v\). Moreover, considering the potential resource competition between non-time-sensitive services and time-sensitive services, paths are first allocated for time-sensitive services. The remaining link resources are then used to allocate paths for non-time-sensitive services. The initial value of the load ratio \(u\) of link \((u,v)\) is \(\mu_{u,v}^{init}=\frac{load_{u,v}^{ls}}{\mathcal{C}\times T_{\tau}}=\frac{ \sum\limits_{s,d\in V}\varphi_{s,d}^{u,v}\times\lambda_{\tau,s,d}^{ts}}{C}\), where \(\varphi_{s,d}^{ts}\) is the number of shortest paths between satellite \(s\) and satellite \(d\) that pass through link \((u,v)\), and \(\lambda_{\tau,s,d}^{ts}\) represents the spatiotemporal traffic factor between satellite \(s\) and satellite \(d\) at time \(\tau\).
## 3 Results
### Variation in Available Predictive Features  
Wearables are able to collect vast amounts of personal data, and most studies use different combinations of the same commonly selected features. For wearables, these include activity (often measured as a number of steps or time in motion), sleep (derived from activity), and heart rate. When smartphones were used, additional features commonly included phone use (apps, communication) and location. However, some research groups utilized less common features. For example, [PERSON] et al. [49] used Bluetooth to infer how many different people the user met. Based on how frequently devices were scanned to be in proximity, devices were categorized as \"self\" (most frequently scanned) and \"other\" (less frequently scanned). Moreover, as the study population consisted entirely of college students who lived on campus, common locations on campus were labeled and visits to these locations were tracked. Both features offered additional insight into the social network and activities of the participants. These types of features are difficult to translate to the general public but could be interesting in targeting populations that spend the majority of their time frequenting the same general area.  
[PERSON] et al. [51] had each participant wear two Empatica E4 wristbands, one on each wrist. This allowed consideration of the symmetry of electrodermal activity between the left and right wrist. The number of skin conductance responses and magnitude of EDA showed stronger asymmetry on days when mental health was poor. The study population was small, so these results should be considered preliminary. A follow-up study with a slightly larger sample size used the same two-wristband approach and found that two of the top ten features were related to EDA, particularly the left/right asymmetry [60]. For practical application, two wristbands are less convenient for the user, but EDA asymmetry in depression may still be interesting to investigate for short-term application or from a fundamental research perspective.  
Less commonly, survey data were included as a feature alongside wearable data. For example, one study used a selection of nine Hamilton Depression Rating Scale (HDRS)-21 questions in addition to wearable activity data to predict HDRS-21 scores. This produced an incredibly accurate model (99% accuracy), but this should be considered with caution as using part of the ground truth to predict the ground truth likely inflated the accuracy [57]. Another study first created an algorithm using a combination of wearable-collected sleep data, patient demographic and lifestyle survey data, and Patient Health Questionnaire (PHQ)-9 data to predict PHQ-9 severity levels in between survey moments. The static demographic data were found to be most important for this prediction. A second algorithm then used all available data to predict the PHQ-9 score 3 months after baseline. The previously predicted PHQ-9 development was one of the most important features of this second algorithm. The authors acknowledged that the need for surveys should be minimized to optimize future applications of such algorithms [58].
## 2 Materials and Methods
### Host Strain and Bacteriophage
#### 2.1.3 Thermal Inactivation of Bacteriophage K5  
Bacteriophage K5 was characterized regarding its behavior with response to heat. Pure bacteriophage K5 lysate (Section 2.1.2) with an initial bacteriophage titer of \(N^{\star}\) = 7.2 \(\times\) 10\({}^{8}\)-7.3 \(\times\) 10\({}^{9}\) pfu mL-1 was filled into screw-capped stainless steel tubes (inner volume: 1.5 mL, hermetically sealed, see details in [45]) and heated at temperatures of 68-75 \({}^{\circ}\)C in a water bath (HAAKE S 14P, Thermo Fisher Scientific Inc., Waltham, MA, USA). To avoid uneven temperature distribution within the tubes, the experiment was started with a warm-up phase of 1 min [45; 46]. Then, holding times ranging from \(t\) = 0-10 min were applied, followed by a cooling step in ice water. Bacteriophage titers were determined after the 1 min warm-up phase (\(N_{0}\)) and after respective holding times (\(N_{t}\)) using the plaque assay (Section 2.1.2).  
The thermal inactivation data can be used to determine the decimal reduction time \(D_{\theta}\), which is a measure of the heat stability of organisms. It is defined as the time required to decrease the bacteriophage titer by 90% (1 log unit) of the initial titer \(N_{0}\) at a defined temperature \(\theta\). Mathematically, it is described by the negative inverse slope of the log-linear phase of the thermal inactivation curve (Equation (1)):  
\[D_{\theta}=-\frac{1}{m} \tag{1}\]where \(D_{\theta}\) is the decimal reduction time at a defined temperature (min) and \(m\) is the slope of the log-linear regression line (min\({}^{-1}\)) [47].  
Additionally, the data were modeled according to the Arrhenius equation as described previously [43; 45] (Equation (2)):  
\[\frac{N_{t}}{N_{0}}=\left[1+(n-1)\cdot k_{ref}\cdot t\cdot N_{0}^{n-1}\cdot\exp \left(-\frac{E_{a}}{\text{R}}\right)\cdot\left(\frac{1}{T}-\frac{1}{T_{ref}} \right)\right]^{\left(\frac{1}{(1-n)}\right)} \tag{2}\]  
where \(N_{t}\) is the bacteriophage titer (pfu mL\({}^{-1}\)) after the thermal treatment at time \(t\) (s), \(N_{0}\) is the bacteriophage titer after the 1 min warm-up phase (pfu mL\({}^{-1}\)), \(n\) is the order of reaction (-) and unequal to 1, \(k_{ref}\) is the death rate at the reference temperature (s\({}^{-1}\) (pfu mL\({}^{-1}\))\({}^{(1-n)}\)), \(E_{a}\) is the activation energy (J mol\({}^{-1}\)), R is the universal gas constant (8.314 J mol\({}^{-1}\) K\({}^{-1}\)), \(T\) is the absolute temperature (K), and \(T_{ref}\) is the reference temperature (K) [47].
## 6 Discussion  
When constructing embankments made of soils that, as a result of mechanical compacting, are characterized by an inadequate strength and/or stiffness, it is possible to improve them by using chemical stabilization by means of various binders. The most popular stabilizing agents available include cement and various additions (fly ash, bottom ash, etc.). The procedure of strengthening with the use of this method includes: properly fragmenting the layer to be stabilized, adding a suitable amount of cement, mixing it into a uniform phase, laying it in place for thickening and properly curing.  
The use of cement can also be helpful when stabilizing other materials, such as waste, as was the case for one chemical plant in southern Poland. During the removal of a storage area for hazardous waste produced during the operation of the chemical plant, it was necessary to construct a new, properly secured storage site-- an engineered landfill. The existing waste, similar to a very soft cohesive soil, was to be inferred in suitable quarters at a storage site with a height of up to 17 m. Due to the near-zero shear strength, it was necessary to strengthen the waste using cement stabilization to improve its strength parameters. These ideas (using slag cement) are not considered to be sustainable due to their relatively high carbon footprint, but cement stabilization is widely used due to the process's simplicity and a relatively high level of confidence. Initial macroscopic tests indicated that a 10% slag cement (CEM II/B-S-32.5 R) addition changes its state from very soft consistency to very stiff consistency. However, it was necessary to determine the physical and strength parameters of the waste, which was necessary to calculate the stability of the planned engineered landfill. Achieving sufficient stability, expressed by the required safety factor SF (\(\geq\)1.5) was the measure of suitability of waste stabilization with cement. The Department of Geotechnics and Roads of the Silesian University of Technology was commissioned to determine the selected physical and strength parameters of the stabilized waste for this study. The basis was tests in a shearbox test apparatus, which involved 87 samples taken at random from the storage site with a volume of approx. 72,000 m\({}^{3}\). The tests performed and analysis of the obtained results demonstrated that after adding 10% of slag cement (by weight), the chemical waste was characterized by an angle of shearing resistance of approx. 32\({}^{\circ}\), a cohesion of approx. 25.9 kPa, and a unit weight of approx. 14.6 kN/m\({}^{3}\).
# An Architecture for Biometric Electronic Identification Document System Based on Blockchain +
## 3 Authentication Methods
### User Authentication
#### 3.2.1 Fingerprint Recognition System  
Biometric fingerprint-based recognition is the oldest method and one of the most developed. As presented in [33], there are different types of readers, such as optical, capacitive, ultrasonic and thermal for the digitization of the fingerprint characteristics. In general, a reader acquires and recognizes the fingerprint pattern, called minutiae, and additional features like for example body temperature. As explains in [31], a minutiae contains the information about bifurcations, ridge endings, dots, core and delta points of the fingerprint. A fingerprint reader acquires a numerous quantity of minutiae to be compared with the minutiae stored, generating a numerical result that represents the probability of a match between the captured and the stored fingerprint. The system can be more robust depending on the number of minutiae acquired. The advantages of the fingerprint systems among others are distinctiveness or uniqueness, permanence and performance in terms of accuracy, speed, and robustness [34]. However it has also some disadvantages like lack of universality when people do not have a fingerprint because of some accident, acceptability and circumvention because of easily imitation using an artifact or a substitute.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 3 System Performance
### EH Optimization  
In view of the expressions for the average harvested energy derived above, it is straightforward to observe that they depend on: first, the channel and link conditions (i.e., attenuation and turbulence, fundamentally); second, on the transmitter and receiver systems with a given pointing misalignment loss, geometric spread, and maximum peak power, \(P_{m}\); and third, on the parameter \(\zeta\). In this section, we consider all these parameters as uncontrollable variables that depend on the channel and the physical devices, except for the \(\zeta\) variable, that can be adjusted to maximize the harvested energy.  
Therefore, we notice that if \(\zeta\) increases, then the average harvested energy also increases; however, this fact adversely affects on the BER performance of the system. In this respect, there exists an interesting trade-off between EH and reliability. We consider in this work that the communication system includes a simple forward error correction (FEC) decoder, which defines a maximum pre-FEC BER, normally referred to as BERtarget. Such a target is the maximum BER of the coded bits before the FEC decoder to guarantee an error-free transmission with high probability.  
We assume two widely adopted target BER values of BERtarget =\(5\times 10^{-5}\) and BERtarget = \(10^{-8}\) related to different FEC options as defined in [49]. Interestingly, the latter target BER is related to a BASE-R code, which is also know as Fire Code FEC (FC-FEC) as is defined in (clause 74 of [49]). This latter code requires a very small computational complexity for its decoding.  
Thus, we pose the optimization problem for the design of the proposed FSO system with EH as follows  
\[\arg\min_{\zeta\in[0,1)}\text{AEH}\] \[\text{s.t. BER}<\text{BER}_{\text{target}} \tag{19}\]  
The above problem is solved numerically in Section 4 to determine the optimal \(\zeta\) value and assess the maximum AEH that can be obtained with the proposed framework under realistic and diverse channel conditions.
## 6 Discussion  
### Capability of the [PERSON] et al. Equation (1) for a Dynamic Experimental Setup  
To investigate the capability of Equation (1) for calculation of the artificial and real vocal folds tissue elastic modulus, we performed the measurement in a wider frequency range from 50 Hz to 500 Hz compared to the previous studies. The results of Sections 5.2.1 and 5.2.2 revealed a strong dependency of the model Young's modulus on the excitation frequency. Therefore, as is demonstrated in Figure 10a, the equation can be an accurate approximation of the silicone sample modulus of elasticity only in frequencies lower than 100 Hz, while, in the higher frequencies, the equation does not have the proper functionality and leads to error. Moreover, since, in a dynamic measurement, many of the material properties are a function of the excitation frequency as are explained in Section 5, the impact of the equation items on the calculation may change by the excitation frequency. On the other hand, the effect of the pipette wall contact force and friction and the radius of the pipette which is also related to the excitation frequency should be considered in an analytical approximation for estimating the modulus of elasticity. Therefore, the Equation (1) cannot be used for the frequency range of human speech. We modified the equation by adding a new correction factor considering the effect of the excitation pressure frequency for both the boundary conditions as the Equations (12) and (13).
## 2 Materials and Methods
### Applied Equipment  
In this study, the OptiTrack-Motion Capture System (NaturalPoint, Inc., Corvallis, OR, U.S.A.) was used for the purpose of optoelectronic motion analysis, allowing evaluating the target-reaching movement. The applied OptiTrack operates on the basis of a passive marker, which during motion recording reflects the emitted IR light from seven 250e cameras. The cameras record the image of a marker placed on the patient's upper body, with a maximum resolution of 832 \(\times\) 832 pixels, and a frame rate of 100 frames per second.  
Additionally, one camera recorded video footage at 100 frames per second. This number of cameras allowed for accurate imaging of the marker's movement in space, without any loss of signal during the measurement. The data were recorded with Motive ver. 1.7.4 exported to C3D file format for processing. Markers with a diameter of 14 mm were placed symmetrically on both upper limbs.  
In Figure 1, the complete set of markers applied in the study protocol is presented.  
Muscle bioelectrical activity measurement (sEMG) was performed using a TeleMyo DTS surface electromyography (Noraxon), following the SENIAM methodology [16]. The superficial muscles of the right upper limb (biceps brachii /triceps brachii-lateral head) were studied. Before the study, the electrode sticking site was prepared by removing hair and cleaning the skin to improve electrode adhesion. Surface electrodes (Ag/AgCl) were placed on the muscle belly between the motor point and the tendon attachment, along the longitudinal midline of the muscle.  
The TeleMyo DTS system (Noraxon) had the following technical specifications: device fundamental noise, less than 1 \(\upmu\)V RMS, input impedance greater than 100 M\(\Omega\), CMR (common-mode rejection ratio) greater than 100 dB, sampling frequency 1500 Hz, and gain 500. NORAXON MR-XP 1.07 Master Editionx software was used for signal processing and EMG analysis.
## 4 Methodology  
In order to extract the in-context triples which provide useful clues for reasoning, an in-context triple extractor is proposed. The extractor is designed based on the encoder-decoder structure. Figure 2 depicts the overall framework of the proposed rule-and-reinforce triple extracting method. The training procedure of the extractor is composed of two main phases:  
**(1) Logical rules guide in-context triples retrieval and extractor pre-training** (Figure 2a). In this phase, the in-context triples for each query triple are retrieved based on whether they can support the logical rules corresponding to the current reasoning. These in-context triples with corresponding query triples are collected as training data, and the extractor can be trained with the guidance of logical rules;  
**(2) Reinforcement learning with LLM's feedback as rewards** (Figure 2b). In this phase, the feedback of LLMs when incorporating the in-context triples generated by the pre-trained extractor is collected. The feedback is utilized as the rewards to further train the extractor through reinforcement learning.
## 5 Discussion and Conclusions
In the first case, interestingness measures such as typicality, IC\({}^{++}\), or utility (with higher _a_) will yield patterns with a bias towards completeness; on the other hand, measures such as relative risk, the Laplace estimate, and utility (with \(a=0\)) will predominantly prioritize discriminant but potentially infrequent patterns. If both characteristic and discriminant patterns are studied or for an initial exploration of a dataset before specifying further analysis questions, measures such as novelty or F1 score, which balance completeness and discriminant power, may present a suitable compromise. Besides quantitative criteria such as completeness and discriminant power, other principles may be considered, e.g., intelligibility of a measure [52]. Attempts have thus been made to develop multi-criteria decision aids for selecting interestingness measures [52]. Ultimately, the choice of interestingness measure in a specific task is a subjective decision and may even be adapted in iterative data analysis; empirical studies comparing computational measures and human assessment of pattern interestingness have observed considerable differences for different human analysts or datasets [53]. Class-labeled datasets have been, and continue to be, prominent in music data mining. Most existing research has focused on identifying discriminant patterns, which alone do not provide an exhaustive picture of the data. The current study complements previous work by systematically investigating interestingness measures for characteristic patterns and illustrating their implications in a detailed discussion of discovered patterns. Conceptualization, K.N. and D.C.; methodology, K.N. and D.C.; software, K.N. ; formal analysis and validation, K.N. and D.C.; data curation, K.N. ; visualization, D.C.; writing, original draft preparation, K.N. ; writing, review and editing, K.N. and D.C. All authors read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# A Controlled-Environment Quality Assessment of Android GNSS Raw Measurements
## 2 GNSS Positioning and Basic Performance of Android Smartphones
### Basic PVT with Raw Measurements
The triangle indicates the average position of the three different models, while the colored points represent all the position solutions during the 10 min test. Covariance ellipses are also depicted by considering the 1-\(\sigma\) and 2-\(\sigma\) confidence interval of the horizontal joint distribution of the positioning solutions. It can be clearly seen that the Xiaomi MI 8 device outperforms the others, both in terms of accuracy and precision, by almost one order of magnitude. This is clearer in Figure 4, which shows the differences of the North and East co-ordinates of the devices from the true co-ordinates. Mi 8 measurements are, at the same time, more stable and accurate, while P10 and S8 measurements led to errors of up to tens of meters. A significant bias in the North direction was clearly visible on P10 and S8 devices. Figure 3: Comparison of the Position, Velocity, and Time (PVT) (Weighted Least-Squares) (WLS), as estimated by the three different devices, in a real environment. The true reference position is shown at the top, along with the average, the 1-\(\sigma\), and the 2-\(\sigma\) ellipses for all the three smartphones. Table 1 reports summary data that show the Huawei P10 having the worst performance, while the Samsung S8 seems to be slightly better. The MI8 outperforms all other devices, confirming that the quality of recent GNSS chipsets is higher and, in general, improving with time. Vertical positioning performance is poorer compared to the horizontal performance in all devices. Indeed, the algorithms used to remove the atmospheric biases were based on empirical models, able to remove, on average, 50% of the delay and thus residual uncompensated delay that could affect the results.
## 4 Discussion
### Enhanced Exploration  
Notice that GP-LLC does not learn inherent network traffic dynamics, making it impossible to differentiate convenient long-term actions from greedy actions. For example, GP-LLC won't adopt long-term resource consolidation policies, in which each channel is ingested by a defined subset of nodes to amortize the overall ingestion resource consumption. On the other hand, we argue that the main reason that keeps NFVDeep above a satisfactory performance is the leakage of the enhanced exploration mechanism. In fact, we demonstrate that the acceptance ratio of E2-D4 QN surpasses every other algorithm because we add enhanced exploration, which enriches the experience replay buffer with non-optimal SFC deployments during training, preventing the agent from getting stuck at local optima, which we think is the main problem with NVFDeep under our environment conditions.  
In practice, in all the NFVDeep or backtracking algorithms, i.e., the algorithms without the enhanced-exploration mechanism, at each VNF assignment, the candidate nodes are filtered based on their utilization availability. Only non-overloaded nodes are available candidates. If the agent chooses an overloaded node, the action is ignored, and no reward is given. Our model instead performs assignment decisions without being constrained by current node utilization to enhance the exploration of the action space. The well-designed state-space codifies important features that drive learning towards sub-optimal VNF placements:  
1. The request vector codifies useful information about the requests that help our agent extract the incoming traffic patterns.
2. The ingestion vector helps our agent to optimize ingestion-related resource demand by avoiding session assignments to VNF instances that do not ingest the content requested at the moment of assignation.
3. The maximum utilization vector gives our agent resource utilization awareness, making it able to converge to assignation policies that optimize processing times and preserve QoS.  
Consequently, our agent learns optimal SFC Deployment policies without knowing the actual bounds of the resource provision or the current instantiation state of the VNF instances that compose the vCDN. It learns to _recognize_ the maximum resource provisioning for the VNFs and also learns to evict assignations to non-initialized VNFs thanks to our carefully designed reward assignation scheme.