esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Model Checking GitHub Repositories for Software Project Analysis
## 2 Background
### Probabilistic Computation Tree Logic  
To perform model checking for Markov models, property specifications must be written in Probability Temporal Logic (PTL). PTL serves as the formal logic framework used for reasoning about the properties and behaviors of probabilistic systems [21]. It extends classical temporal logic by integrating probabilistic operators, enabling the specification and verification of properties with probabilistic aspects. In our study, we leverage a specific instance of PTL called pCTL. pCTL allows the expression of probabilities such as reaching a specific state, the expected time until a certain event occurs, or the likelihood of satisfying certain conditions in probabilistic systems [22]. pCTL formulas consist of state and path formulas, incorporating transient state probability operators denoted as P and steady-state probability operators denoted as S. Path formula properties are composed of operators like X (neXt), U (Until), and F (Future), which allows for reasoning about future occurrences using the U operator. The formula can be represented as follows:  
* State formula \[\Phi::=true\,|\,a\mid\
eg\Phi\,|\,\Phi_{1}\wedge\Phi_{2}\,|\,P_{\triangle p}[ \varphi]\,|\,S_{\triangle p}[\Phi]\]
* Path formula \[\varphi::=X\Phi\,|\,\Phi_{1}\,U^{\leq N}\,\Phi_{2}\]
* F operator \[F\Phi\equiv true\,U\,\Phi\]  
We use PRISM 4.7 as the model-checking tool. PRISM [23] automatically verifies and quantifies properties expressed in pCTL by automatically exploring the entire state space of DTMC models. PRISM allows us to provide quantitative information in addition to probabilistic values by integrating the reward structure into DTMC. The reward structure assigns numerical values to system states or transitions, capturing quantitative aspects such as costs or utilities associated with specific events [24]. In this paper, the function \(\rho\) is added to the state formula to use a state reward structure. In the formula for the reward used in this paper, \(C\leq k\) represents the cumulative reward value up to step \(k\), and \(F\,\Phi\) represents the expected value until it is reached.  
* State reward function \[\rho:S\rightarrow\mathbb{R}_{\geq 0}\]
* State formula with reward \[\Phi::=R_{=7}[C\leq k]\mid R_{=7}[F\Phi]\]  
PRISM also allows the usage of filter operators, which address the limitation of PRISM requiring the specification of an initial state. We utilize filter operators to find averages or values that satisfy specific conditions. It selects the states that satisfy _cond_ as the initial state and calculates the average or values of the result of performing \(\Phi\). The formalism used in our paper is as follows:  
* Filter formula to find the average \[filter(avg,\Phi,cond)\]
* Return the value for the single state satisfying states \[filter(state,\Phi,cond)\]
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### General Characteristics of Wetland Publications  
#### 3.1.1 Scientific Productions Trend  
Figure 4 reflects the temporal distribution of the total number of reviewed papers along with the publication trends from three top publishing countries among 344 papers reviewed using PRISMA from 1997 to 2022. As shown, there is an increasing trend of scientific publications highlighting the importance of wetlands to the scientific community. The apparently drastic reduction from 2021 to 2022 can be attributed to the fact that the query results are until the middle of 2022. Considering the growing number of RS sensors and their promising performances in environmental monitoring tasks, about 67% (\(n=232\) out of 344) of the papers were published in the last five years (2018-2022).
# Load Balancing Strategies for Slice-Based Parallel Versions of JEM Video Encoder
## 3 Parallel Approaches  
Slices are fragments of a frame formed by correlative (in raster scan order) CTUs (see Figure 7). These are regions of the same frame that can be decoded (and also encoded) independently, which offers a valid parallelization approach for video encoding and decoding processes. However, slice independence has a drawback in that the existing redundancy between data belonging to different slices cannot be exploited, and thus the coding efficiency in terms of the R/D performance decreases. Moreover, slices are composed of a header and data, and although this is useful in terms of providing an encoded video sequence with error resilience features (since the loss of a single slice does  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Characteristics** & **HEVC** & **JEM** \\ \hline Intra-modes & 33 & 67 \\ List MPMs & 3 & 6 \\ N\({}^{\text{o}}\) of Neighbors for MPMs derivation & 2 & 5 \\ Interpolation filters & 2-tap Linear & 4-tap Cubic or Gaussian \\ Boundary filter samples & 1 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Differences between HEVC and JEM with respect to the spatial prediction.  
Figure 6: Examples of limit prediction filters for intra-modes corresponding to modes 30–34 in HEVC [15].  
not prevent the rest of the slices in the same frame from being properly decoded), the inclusion of a header in each slice also causes a decrease in the R/D performance.  
Based on the slice partitioning of the JEM video encoder, each video frame is divided into as many slices as threads to be spawned. The number of threads in the parallel region can be set as a parameter or can be obtained depending on the current state of the computer system. Hence, the number of threads (and consequently the number of slices), and the slice size are computed before starting the encoding process. In this work, we have developed two algorithms, the first of which requires synchronization processes while the second is a completely asynchronous algorithm.
# Testing the Galileo High Accuracy Service User Terminal (HAUT) in Static Scenarios +
## 6 Positioning Performance by Galileo HAS User Terminal Using Galileo HAS Corrections  
The positioning performance provided by the HAUT using Galileo HAS corrections and configuring the User Algorithm to assume no dynamics (as opposed to the configuration used for the Galileo HAS performance characterization presented in the HAS SDD, which considers kinematic conditions) was evaluated for a period of 31 days from 25 February to 27 March 2023. The Galileo HAS User Terminal, located in Munich (Germany), was switched on on 24 February to converge and left untouched until 8 March, when the campaign had to be interrupted for a few hours to perform maintenance of the antenna installation. The day on which the Galileo HAS User Terminal was switched on again is excluded from the analysis. The positioning accuracy provided by the HAUT for 30 days in 24 h periods from 0 h UTC is presented in Table 1.  
Figure 2: HAUT user terminal.  
It is to be noted that the positioning accuracy results from Table 1 are only considered representative of static, open-sky conditions after full convergence time of the EKF. Therefore, they are not representative of the performance in kinematic conditions, which are the conditions assumed for the HAS performance characterization presented in the HAS SDD [2]. This explains the different level of performance between Table 1 results and the SDD performance levels.
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 4 Performance Evaluation and Discussion
### Simulation Environment and Setup
#### 4.1.2 Downlink Frame Scheduling  
There are two options for downlink data scheduling in IEEE 802.11 ah. The first one is based on stations transmitting PS-Polls to retrieve downlink data, whereas the second one implies additional intelligence at the AP to calculate wake-up times for stations and to schedule downlink packets appropriately. We opted to implement the latter, namely transmission scheduling of downlink frames from the AP to the stations. DL packets must be sent during the corresponding RAW slot when the station is actively listening, otherwise the station will never receive them. When the AP MAC layer receives a packet for transmission from the layers above, it will check the destination of the packet and determine its corresponding AID. Based on the AID, the exact RAW slot can be determined and the AP will schedule the transmission at the beginning of the slot after the next DTIM beacon. This gives the AP the ability to indicate whether there is pending data for the RAW group of that destination and thus ensures the destination will be listening. To prevent the AP from sending data to stations outside their slots (for example due to backing off in heavy channel contention), we assigned a separate DCF manager with its own queue to each RAW slot. Each DCF manager will only have access to transmit its frames in the queue during the corresponding RAW slot, in exactly the same way the DCF managers of stations are restricted.
## 4 Results and Discussion
### Xrd  
In Figure 4, a comparison of the XRD patterns of BA and TGPF is shown. This comparison was carried out to observe any phase changes between BA and TGPF. Only one pattern of TGPF is presented here because all of the TGPFs demonstrate a similar pattern.  
A hump can be seen in the pattern of the TGPF between 15\({}^{\circ}\)-40\({}^{\circ}\)(20) which is absent in the pattern of the BA. It is attributed to the glassy phase in the TGPF derived from the transformation of SS in  
Figure 4: XRD pattern of bottom ash (BA) and TGPF.  
Figure 3: SEM images for TGPFs.  
a vitreous foam. The four phases from BA patterns (i.e., quartz, mullite, anorthite, and hematite) are also present in the TGPF. The anorthite peak of the TGPF is slightly higher than that of the BA, which indicates that geopolymerization has occurred with the formation of anorthite hydrate [16; 17]. The peak of quartz in both the patterns is high, but it is slightly higher in TGPF than for BA.
## 5 Results and Analysis
### CSR5  
SURAA outperforms the CSR5 scheme, overall, although by relatively smaller margins (average speedup: 1.1\(\times\), max: 1.94\(\times\)). The highest speedup achieved for SURAA against CSR5 for any single matrix among the 26 matrices of our dataset is 1.94\(\times\) (for matrix _rail4284_). SURAA achieves a higher throughput against CSR5 for all the 26 matrices except for four matrices, _thermal2_ (0.71\(\times\)), _amazon0312_ (0.78\(\times\)), _in-2004_ (0.63\(\times\)), and _bundle1_ (0.90\(\times\)). The reasons for low performance of SURAA for the first two matrices have already been discussed while discussing the HYB performance (i.e., _npr_\(\leq\) 32). The average _npr_ for the third matrix, _in-2004_, is 12.23, and it has a medium _npr_ variance; see Table 3 and Figure 6. Therefore, the reasons for the low performance of SURAA for this matrix are the same, i.e., _npr_\(\leq\) 32 implies no invocation of dynamic kernel in SURAA. The potential solutions to address these limitations of SURAA have also been mentioned earlier. The _npr_ variance of the fourth matrix, _bundle1_, is high, with an average _npr_ of 72.84. This matrix has neither low average _npr_ nor low _npr_ variance. However, bundle1 is a small (10,581 rows), square, symmetric matrix and the difference in SURAA and CSR5 performance is quite small, 9.30 versus 10.30 GFLOP/s, respectively. Future work will focus on further investigation of SURAA's performance to improve its behavior in such cases.
## 2 Theoretical Background
Consequently, the measured data can be better described by applying a logistic function instead of an exponential function:  
\[AV=\frac{a}{\left(1+\left(\frac{\varepsilon_{AC}}{c}\right)^{b}\right)^{g}}+d \tag{5}\]  
where \(b\), \(c\), and \(g\) are the parameters determining the slope and inflection point of the logistic function; \(d\) and \(a\) are the lower and upper limits of the logistic function, respectively; and \(AV\) and \(\varepsilon_{AC}\) are as explained earlier. Since the air void content of drilled hot-mix asphalt core samples cannot exceed 20% in practice, the value of the parameter \(a\) is fixed at 0.20. A further expectation is that as the values of relative permittivity approach that of the air (\(\varepsilon_{\text{air}}=1.0006\)), the function shall approach a 100% air void content. Therefore, the lower limit \(d\) is established using the following formula:  
\[d=\frac{0.0008}{\left(\varepsilon_{AC}-1\right)} \tag{6}\]Considering the above-mentioned aspects and substituting the above equation into Equation (5), the recommended MnDOT (Minnesota Department of Transportation) model is based on the following final formula:  
\[AV=\frac{0.20}{\left(1+\left(\frac{\varepsilon_{AC}}{c}\right)^{b}\right)^{g}}+ \frac{0.0008}{\left(\varepsilon_{AC}-1\right)} \tag{7}\]  
The remaining three parameters in the MnDOT model can be determined by minimising the sum of the square differences between the gyratory specimens and modelled data [36]. This approach makes it possible to measure the in situ air void content using a GPR device without a core sample calibration.
## 4 Classification of MAC Protocols Based on Channel
### Distributed Single-Channel MAC Protocols
#### 4.1.6 Map  
Medium access for PLNC is a contention-free MAC protocol used to provide quick and reliable data transmission in VANETs. It addresses two fundamental problems: collision due to hidden terminals in CSMA/CA-based networks and handling excess control messages in dense TDMA-based network scenarios. It is a decentralized location-based scheme, where the network is divided into sub-zones of equal length. Vehicles incorporate CSMA/CA schemes to access the channel; the successful node transmits and is called the intra-zone relay node [72]. The transmission occurs in two phases: the access phase, when nodes transmit packets to relay nodes, and the broadcast phase, where the relay forwards the network-coded packet. MAP handles the hidden terminal problem by priority-based indexing [73]. To prevent dissemination delays, a session order prioritization scheme is implemented. It provides reliable and faster transmission.  
There is a possibility of merging collision as this protocol only handles access collision; delay, Packet Delivery Ratio, and network throughput are not analyzed. The network model presented is based only on a 1D network.
## 5 Performance Evaluation  
This section describes the experimental setup to analyze the performance of the proposed protocol and the existing protocols. The comparison includes the communication and computational costs. The communication costs were evaluated in terms of the number of exchange message and the total size of these messages. The computational costs were evaluated on an embedded system that can be implemented on actual IoT devices. The protocol was tested with a secp256k1 256-bit EC, with a 256-bit size for the SHA2 hash function. The identity of a node \(ID\) and a challenge \(\mathrm{C}^{\mathrm{f}}\) for a PUF primitive were assumed to be 8 bytes.
###### Abstract  
Sun-Induced fluorescence at 760 nm (F\({}_{760}\)) is increasingly being used to predict gross primary production (GPP) through light use efficiency (LUE) modeling, even though the mechanistic processes that link the two are not well understood. We analyzed the effect of nitrogen (N) and phosphorous (P) availability on the processes that link GPP and F\({}_{760}\) in a Mediterranean grassland manipulated with nutrient addition. To do so, we used a combination of process-based modeling with Soil-Canopy Observation of Photosynthesis and Energy (SCOPE), and statistical analyses such as path modeling. With this study, we uncover the mechanisms that link the fertilization-driven changes in canopy nitrogen concentration (N%) to the observed changes in F\({}_{760}\) and GPP. N addition changed plant community structure and increased canopy chlorophyll content, which jointly led to changes in photosynthetic active radiation (APAR), ultimately affecting both GPP and F\({}_{760}\). Changes in the abundance of graminoids, (%graminoids) driven by N addition led to changes in structural properties of the canopy such as leaf angle distribution, that ultimately influenced observed F\({}_{760}\) by controlling the escape probability of F\({}_{760}\) (Fesc). In particular, we found a change in GPP-F\({}_{760}\) relationship between the first and the second year of the experiment that was largely driven by the effect of plant type composition on Fesc, whose best predictor is %graminoids. The P addition led to a statistically significant increase on light use efficiency of fluorescence emission (LUE\({}_{\text{f}}\)), in particular in plots also with N addition, consistent with leaf level studies. The N addition induced changes in the biophysical properties of the canopy that led to a trade-off between surface temperature (Ts), which decreased, and F\({}_{760}\) at leaf scale (F\({}_{760\text{leaf,fw}}\)), which increased. We found that Ts is an important predictor of the light use efficiency of photosynthesis, indicating the importance of Ts in LUE modeling approaches to predict GPP.  
**Keywords:** sun-induced fluorescence (SIF); gross primary production (GPP); fertilization; nitrogen; phosphorus; light use efficiency; SCOPE; canopy structure
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 5 Giardia intestinalis  
Giardia genus of flagellated protozoans of the family Hexamitidae, phylum Fornicata, clade Metamonada [2] comprises the eight species--G. agilis, G. ardeae, G. crictidarun, G. micrioti, G. muris, G. peramelis, and G. pstitat--associated with amphibians, herons, hamsters, voles and muskrats, rodents, in bandicoots, and budergiers, respectively, and G. intestinalis, responsible for human infections. These organisms colonize the small intestine of mammals [138]. _G. intestinalis_ is divided into assemblages, or subtypes, A-H, and sub-assemblages are distinguished based on different conventional PCR or qPCR assays on genes encoding the small subunit ribosomal RNA (SSU rRNA), glutamate dehydrogenase (_gdh_), triosphosphate isomerase (_tpi_) or \(\beta\)-giardin (_bg_) [15; 139]. Assemblages A and B have a wide host range and cause most infections in humans with the latter responsible for most cases. Co-infections with Assemblages A and B were reported by 41 studies in the MENA region. Assemblages C and D are associated with dogs and other canids, E with livestock, F with cats, G with rodents, and H with seals [26; 95; 138]. Human infections with Assemblages C, E, and F were reported in Egypt and Iraq [95]. The possibility that _Giardia_ assemblages could represent distinct species is under consideration [15].  
G. intestinalisAssemblage A can be subtyped into sub-assemblages AI, AII, and AIII by multilocus sequence typing (MLST), with sub-assemblages AI and AII mainly involved human and animal giardiasis worldwide, and sub-assemblage AIII more common in hoofed animals. An Assemblage-A-specific multilocus sequence typing (MLST) method scheme can facilitate source tracking in foodborne outbreaks [26]. Assemblage B was previously subtyped into BIII and BIV, but later, a new typing system for Assemblage B was developed [95].  
G. intestinalishas a simple life cycle comprising the trophozoite, which causes the symptoms, and the cyst, which represents the infective form shed with the stools of the host. Its ability to cause infection is favored by the low infective dose of 10 to 100 cysts in humans and by the large number of cysts shed by a single individual, reported to be \(2.5\times 10^{7}\) G. intestinalis cysts annually in a study in the Netherlands. High-level shedding by different animal hosts, including insects, determines high environmental contamination. Moreover, a high survival ability of cysts from weeks to months was reported in drinking water and food. The small size of G. intestinalis, 8-12 um in length, allows their penetration and survival in water filters such as sand filters. In addition, their ability to survive at low temperatures determines their persistence in refrigerated conditions [26].
## 4 Case Study  
### Project Profile  
The ultra-high arch dam is a concrete double curvature arch dam, which is in southwest China. The dam crest elevation is 1885 m, the lowest elevation of the foundation surface is 1580 m, the normal pool level of the reservoir is 1880 m, and the maximum dam height is 305 m. The concrete arch dam is shown in Figure 2. The reservoir has been impounded since December 2012. From the beginning of 2014, the reservoir has been impounded, lifted, and operated in three stages, and dropped slightly at the end of the second stage. The third stage of water storage reached the normal pool level from June 2014 to the end of 2014, and then the reservoir operated normally. The change in water level is shown in Figure 3. The starting surface of the water level altitude of the dam is the mean sea level of the Yellow Sea (Qingdao tidal station) in China.  
Figure 1: Flow chart of the deformation zoning and monitoring model of an ultra-high arch dam operating at a complex water level.  
Figure 2: The concrete arch dam.  
According to water level variation characteristics, three phases of water level conditions are selected for comprehensive cluster analysis.  
The first phase: from December 2012 to June 2013, that is, the water storage stage with the rapid rise of the reservoir water level.  
The second phase: from July 2013 to June 2014, that is, the reservoir water level rises to the normal pool level and falls back.  
The third phase: from July 2014 to June 2015, that is, the first annual cycle of reservoir water level operation, which can represent the variation law of subsequent normal operation of the reservoir.  
The research object is the observation data of deformation along the river flow direction of 32 groups of displacement monitoring points of the dam. The layout of displacement monitoring points is shown in Figure 4. The displacement monitoring system of the inverse plumb line is automatic. The sampling frequency is taken, and the actual monitoring frequency is once a day. The layout of the inverse plumb line and the displacement monitoring points are shown in Figure 5. Since the floating ball can move freely, the displacement of the dam can be measured by the moving distance of the inverse plumb line.