esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 5 Physical Network Optimization  
In the previous section, the algorithm has given several feasible physical RBs for the MS to switch in. The goal of this section (the third step in Figure 4) is to propose a method to finally choose one particular network among the given choices based on the analysis of penalty function.  
We propose a new DP-DQN algorithm based on deep reinforcement learning in this section to solve the optimization problem--select the appropriate access physical channel to minimize the overall network cost (i.e., from the perspective of core network overhead instead of single MS).  
It is worth mentioning that the training method and use of DQN used in this study are slightly different from the conventional situation. In this study, the training of DQN is located in the controller side of the core network, while the actual deployment of the model is located in the MS side. This design is because we hope to achieve an equivalent feedback loop through core network controller training and marginal terminal deployment, so that the mobile terminal can consider the access selection problem from the perspective of core network overhead.  
After the training is completed, DQN will be deployed to the mobile terminal. Here, we draw on the idea of federated learning by deploying the edge decision model while absorbing the real-time data of each mobile station for final training to improve the accuracy of the model and the overall efficiency of the network. So we call it DP-DQN, or Distributed-Deployed DQN.  
As you can see in Figure 5, during the training process, we will simulate some of the possible access request from the MS to the network. Then, the cost of certain actions will be calculated based on the designed formula to estimate the overhead of this allocation decision made on the network.  
We define the actions State Space and Action Space here. Instead of the Reward Function in the previous section, we define a Penalty Function to put the most emphasis on the action of the MS. In other words, if the MS makes a request which will put more burden on the core network, penalty will be given to the MS to make sure the overhead of the network is being kept at a relatively low level.  
Figure 5: Proposed DP-DQN pre-training process at the side of the core network controller.
# Defect Detection in Striped Images Using a One-Dimensional Median Filter
## 2 Materials and Methods  
### Equipment and Images  
The algorithm developed in this research was implemented with Python 3.6 and OpenCV 3 running on a Windows 7 operating system on a PC with an Intel Xeon 3.2-GHz CPU and 18 GB of RAM. The images to be inspected were 8-bit 4096 \(\times\) 3072 pixel grayscale images. One of the images is illustrated in Figure 1a. The striped background feature is obvious. The defects can be categorized into two types: scratches, as shown in Figure 1b, and stains, as shown in Figure 1c. Scratches and stains may exist in the same image, as shown in Figure 1a. The minimum number of pixels for stains was about 80, but the number of pixels for the scratches varied. From Figure 1a, there are also stripe-free areas, which look like the number sign #. These are referred to as intervals in this paper. The striped areas between the intervals are referred to as blocks. There are nine connected blocks in each of the images processed. While there may appear to be 12 blocks in Figure 1a, the top portions of the middle three blocks are connected to their bottom portions, respectively, so that there are only nine connected blocks. The heights and the widths of the intervals between the blocks were constant in all images, but the sizes of the blocks varied. This study took advantage of the fixed-sized intervals of the images in the proposed algorithm.
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 3 _WeakHEO_ Navigation Performance  
In order to test the achievable performance in terms of position and velocity determination we have selected two representative portions of the MTO, each of one-hour duration.  
The first portion starts approximately at 36,000 km of altitude (altitude of the Geostationary Earth Orbit) and a second portion starts approximately at the average distance of the Moon from the centre of the Earth of 384,400 km, very close to the apogee of the MTO. Figures 16 and 17 illustrate the ECI 3D position and velocity accuracy, respectively, of a least square (LS) single-epoch solution, a pure propagated (Pp) solution (obtained only propagating the initial condition through the orbital forces model), and the orbital filtered (OF) solution, which integrates by means of the developed adaptive EKF the LS together with the Pp solution. The LS solution is as expected very noisy due to the noise, which affects pseudorange measurements and it increases with the altitude because of the increasing GDOP (see Figure 18). On the other side, the Pp position solution has a significant drift due to the error in the initial condition; in fact, after about 250 s the position error reaches 50 m as expected because of the initial 0.2 m/s error in the initial velocity. (0.2 m/s \(\times\) 250 s = 50 m). The OF solution is several times more accurate; the GPS measurements prevent the orbital propagation solution drifting, while the orbital propagation smooths the GPS solution. Figures 19-21 illustrate the same quantities but for the MTO portion at Moon altitude  
Figure 16: 3D position error for the considered portion which starts at GEO altitude for: the single-epoch least square (LS) solution (in **blue**); the orbital filtered (OF) solution (in **red**); and the propagated (Pp) solution (in **green**).  
Figure 17: 3D velocity error for the considered portion which starts at GEO altitude for: the single-epoch least square (LS) solution (in **blue**); the orbital filtered (OF) solution (in **red**); and the propagated (Pp) solution (in **green**).  
Figure 18: GDOP during the MTO portion that starts at 36,000 km altitude.  
Figure 19: 3D position error for the considered portion at Moon altitude for: the single-epoch least square (LS) solution (in **blue**); the orbital filtered (OF) solution (in **red**); and the propagated (Pp) solution (in **green**).  
Figure 20: 3D velocity error for the considered portion at Moon altitude for: the single-epoch least square (LS) solution (in **blue**); the orbital filtered (OF) solution (in **red**); and the propagated (Pp) solution (in **green**).
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Bio-Inspired Energy-Efficient Routing Protocols (Bio-Inspired E2 RPs)
#### 5.2.1. Energy-Efficient Routing Clustering Approach for UWSNs (E2 RCA-UWSNs)  
Despite the fact that, UWSN clustering is a well-known method. Clustering for UWSN energy-efficient routing protocols has just recently been deployed. In 2015, the first UWSN cluster routing protocol with improved energy efficiency was introduced. The E2 RCA-UWSNs, in which a particular node serves as a CH, it was introduced by the author in [132]. Each individual node has a connection to the sensor Rx node; once these connections are made, the other communication nodes in each CH, when start to operate. The cluster members (CM) select the CH based on the network's shortest path between communicating nodes. Utilizing certain communicating nodes in this suggested routing strategy may aid to decrease the energy efficiency of UWSNs. However, it is very difficult and impossible to install these particular communicating nodes in this manner in UWSNs.
## 5 Numerical and Engineering Examples
### Numerical Example 1  
Five different conditions of the epistemic uncertainty variable \(x\in[10,20]\) were analyzed as follows:  
Case 1: \(x\) is represented using a single interval \([10,20]\);  
Case 2: \(x\) is represented using multiple intervals \([10,13]\), \([14,16]\), and \([19,20]\);  
Case 3: \(x\) is represented using a triangular fuzzy number \([10,13,20]\);  
Case 4: \(x\) is represented using evidence theory, the non-overlapping subintervals are \([10,14]\), \([14,17]\), and \([17,20]\), and the corresponding BPAs are 0.3, 0.5, and 0.2, respectively; and  
Case 5: \(x\) is represented using evidence theory, the overlapping subintervals are \([10,15]\), \([13,18]\), and \([17,20]\), and the corresponding BPAs are 0.3, 0.5, and 0.2, respectively.  
The uncertain design intervals \(\big{[}x_{lower},x_{upper}\big{]}\) of epistemic uncertainty are the same \([10,20]\) in Case 1-Case 5 according to the algorithm presented in Section 4.2. The bounds of four statistical moments \(M_{1}\sim M_{4}\) of \(x\) in Case 1-Case 5 were calculated according to the methods presented in Section 3, and the results are listed in Table 2. The converge procedure used to calculate the bounds of \(M_{2}\) in Case 2 is shown in Figure 5. A series of sampling points of \(M_{1}\sim M_{4}\) were randomly selected and the corresponding cubic normal distribution functions were constructed. In total, 101 sampling points \(x^{\tau}=10+0.1(\tau-1)(\tau=1,\cdots,101)\) were selected by dividing the uncertain design interval \([10,20]\) uniformly. The CDFs at these sampling points \(x^{\tau}\) with different \(M_{1}\sim M_{4}\) values in Case 1-Case 5 were calculated and are shown in Figure 6. The maximum and minimum CDF values at \(x^{\tau}\) were calculated by comparing the CDF under every \(x^{\tau}\), and the probabilistic representation functions of \(x\) in Case 1-Case 5 are shown in Figure 7. Results indicate that the epistemic uncertainty with different representation functions can be probabilistically represented using the proposed algorithm. Compared with the methodology presented in [35], the proposed algorithm extends the application of the cubicnormal transformation method from interval variables to multiple types of epistemic uncertainties. The uncertain interval can be decreased by obtaining more uncertainty information. Only a single interval was available in Case 1, and the uncertainty interval at every value of \(x\) was large, while the evidence theory and BPAs of three subintervals were available in Case 4 and Case 5, and the uncertainty interval was smaller than that in Case 1.  
Figure 5: Optimization iteration procedure of calculating bounds of \(M_{2}\) in Case 2.  
Figure 6: Cumulative probability density function of epistemic uncertainties (**a**) Case 1; (**b**) Case 2; (**c**) Case 3; (**d**) Case 4; (**e**) Case 5.
## 3 Materials and Methods
### Datasets  
#### 3.1.1 Slum Reference Map  
Bandung City Government has undertaken a systematic mapping of areas classified as slum housing and settlements, adhering to the criteria established in the Ministry of Public Works and Public Housing Regulation Number 14 of 2018, which focuses on the prevention and improvement of the quality of slum housing. The Mayor of Bandung's  
Figure 1: Administrative map of Bandung City, Indonesia, highlighting urban villages and sub-districts. Reproduced with permission from Government of Bandung City. Document: Urban Slum Prevention and Upgrading Plan; published by the office of the Bandung City housing and settlement area, 2023 [19].  
Decree Number: 648/Kep.1227-DPKP3/2020 delineates specific locations of slum housing, revealing a diverse range of conditions across the city. Notably, light slum areas encompass approximately 474.0809 hectares across 44 urban villages, while medium slum areas account for 17.865 hectares within 19 urban villages. The Sukahaji urban village in the Babakan Ciparay sub-district has been identified as the largest slum area, covering 30.85 hectares, underscoring the persistent challenges that the Bandung City Government must navigate in its efforts to address slum conditions effectively. Additionally, a detailed inventory of slum areas has been compiled and is available as per the Mayor's Decree.  
The 2020 slum mapping data depicted in Figure 2, which is based on Bandung's mayor's regulation, is used in the slum classification modeling process. This data consists of two main categories: 'light slum' and'medium slum.' Of the available data, 859 sub-districts fall into the 'light slum' category, while only 44 fall into the'medium slum' category.  
In addition, we integrated key slum indicators developed by governmental bodies and academic sources to establish a comprehensive definition of slum areas, along with a corresponding set of indicators. Additionally, the official reference map was adjusted in accordance with this definition, as elaborated in the subsequent Data Preparation Section. This modified map served as a benchmark for evaluating the accuracy of slum maps generated through remote sensing imagery and street view imagery utilizing a deep learning network [16].
## 3 Library Design and Functionalities
### Performance Evaluation  
A pre-compiled FMU is provided for each building model and can be used with different weather files. This allows the users to train their controllers (i.e., RL agents or models for MPC) with different weather files, while the weather file for final evaluation is fixed. These fixed weather conditions on a predefined period of time ensure comparability of the control performances via the implemented KPIs. The characteristics of these fixed evaluation scenarios are displayed in Table 2. The defined KPIs fall into the categories of thermal comfort (related to temperature constraints) and objective KPI (related to the objective to minimize).  
KPI Definition  
For the thermal comfort, a range of acceptable temperatures is defined. The tracked KPIs are the average deviation from the target temperatures for each controlled thermal zone and the total number of range violations. Let the desired temperature range be defined by the interval \(I=[a,b]\). Then the average deviation \(d(T,I)\) for temperature measurements \(T=\{t_{i}:i=1,\dots,N\}\) is defined as  
\[d(T,I):=\frac{1}{N}\sum_{i=1}^{N}\|t_{i}\|_{I} \tag{1}\]  
where \(\|t\|_{I}=\begin{cases}a-t,\text{ if }t<a\\ 0,\text{ if }t\in I\\ t-b,\text{ if }t>b\end{cases}\).  
The number of total violations \(v(T,I)\) is defined as  
\[v(T,I):=\sum_{i=1}^{N}\delta(t_{i},I) \tag{2}\]  
where \(\delta(t,I)=\begin{cases}0,\text{ if }t\in I\\ 1,\text{ if }t\
ot\in I\end{cases}\).  
The average energy exchanged with the grid is tracked for the models based on the Apartments and Apartments2 buildings. Let \(E_{prod}=\{e_{prod,i}:i=1,\dots,N\}\) be the set of \(N\) consecutive measurements of produced energy and \(E_{con}=\{e_{con,i}:i=1,\dots,N\}\) of consumed energy. Then the average energy exchange \(e(E_{prod},E_{con})\) is defined as  
\[e(E_{prod},E_{con}):=\frac{1}{N}\sum_{i=1}^{N}|e_{prod,i}-e_{con,i}|. \tag{3}\]  
In the evaluation scenario, the goal is to minimize this quantity and therefore maximize the self-consumption of produced energy.  
The objective for the Offices, MixedUse, SimpleHouse and SwissHouse buildings is to minimize their power consumption. Let the mean power demand for \(N\) simulation steps be given by \(D=\{d_{i}:i=1,\dots,N\}\). The minimization objective is again given by averaging over the measurements, so the average power demand \(p(D)\) is defined as  
\[p(D):=\frac{1}{N}\sum_{i=1}^{N}d_{i}. \tag{4}\]The environments based on the Seminarcenter building track the CO\({}_{2}\) emissions for the installed gas boiler and the varying electricity mix. A minimization of this emission is the focus of their evaluation scenario. Let the emission values be given by \(C=\{c_{i}:i=1,\ldots,N\}\). The computed KPI for those measurements is the average emission \(g(C)\) defined as  
\[g(C):=\frac{1}{N}\sum_{i=1}^{N}c_{i}. \tag{5}\]  
Instead of using predefined KPIs, it is also possible to define custom KPIs. An example of this is given in Appendix A.2.
## 3 Data and Methodology
### News Selection  
As mentioned in Section 2, the information disorders spectrum is wide, and it includes mis-/dis-/malinformation practices, which means that the reduction of the general problem to a \"true or false\" game is much too simplistic. In fact, professional fact checkers debunk a wide variety of malicious pseudo-journalism activities, flagging news articles in many different ways: reported news include not only those entirely fabricated but also those that cherry-picked verifiable information to nudge a malicious narrative, or those that omitted or twisted an important detail, thus telling a story substantially different from reality.  
For our experimental setting, we selected 20 articles that we were able to divide into two equally sized groups of true (identified from now on with a number from 1 to 10) and false news (from 11 to 20)--see Appendix A.3 for the complete list and additional information. Such division was straightforward in the majority of cases: we classified news as true when they were not falsified in any professional debunking sites, and we also confirmed their veracity on our own. To challenge the user a little bit, we selected lesser known facts, sometimes exploited from some outlets as click-baiting, and sometimes with a poor writing style, with the exception of news 9 and 10, which we used as control questions. Marking a news item as false can be much more ambiguous. However, we selected 10 stories that were dismissed in at least one debunking site. In particular, article n. 14 was particularly difficult to classify as false for the reasons explained in Appendix A.3, but we kept it anyhow: in fact, when evaluating accuracy scores of Fakenewslab users, we are not calling out the gullibility of some of them, but we are only monitoring their activities and reactions, which may be dependent on the environmental setting they were subject to. Thus, it is important to stress here once again that we are more interested in the observed differences between rooms' outcomes than in assessing people's ability to guess the veracity of a news. Hence, even if in the following sections we will make use of terms such as \"correct\", \"wrong\", \"false/true positives\", or \"false/true negatives\" for the sake of simplicity, we must recall that we are measuring the alignment of users' decisions with our own classification just to assess those differences with respect to some given baseline.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Room** & **Finished Tests** & **Completion** \\ \hline
1: Headline & 21.72\% & 81.76\% \\ \hline
2: Full text & 17.42\% & 70.00\% \\ \hline
3: Source & 20.81\% & 79.18\% \\ \hline
4: Social ratings & 20.50\% & 79.97\% \\ \hline
5: Random ratings & 19.55\% & 79.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tests completion percentages grouped by rooms. Users are distributed randomly following an uniform distribution in the five rooms, but complete tests in Room 2 are only 17.42% of the total. Users in Room 2 left the test unfinished almost 10% more times than users in other rooms.
## 3 Results and Discussion
### Phenolic Content of Honey
As far as we know, no studies in the literature evaluated the scavenging capacity and reducing the power of honey from _P. undulatum_. For other types of honey, some authors studied these parameters (DPPH, ABTS, and FRAP); however, for the DPPH assay, it was impossible to compare the available data in the literature since different reaction conditions were employed. For example, [PERSON] et al. [PERSON] et al. (2018) analyzed commercial Portuguese honey, including eucalyptus and heather honey samples, and reported DPPH radical scavenging activity values of 17.3% for eucalyptus honey and 32.7% for heather honey. Another study determined the radical scavenging capacity of honey from _Trifolium_ spp., and obtained an inhibition percentage of 23.3% using the DPPH method. In contrast, the same honey samples exhibited a FRAP value of only 0.089 mmol TE/100 g, which is a lower value compared to our study [PERSON] et al. (2018). A Spanish research group applied a similar methodology to the ABTS assay developed in the present work, analyzing three heather honey samples and obtaining values ranging from 0.123 \(\pm\) 0.002 mmol T/100 g to 0.449 \(\pm\) 0.021 mmol T/100 g [PERSON] et al. (2018). When compared with our results, it is possible to verify that they are within the range of the values obtained in our study. According to [PERSON] et al. [PERSON] et al. (2018) the phenolic compounds are correlated with antioxidant capacity, which could justify the present results since sample 1 was the sample with higher phenolic content and generally showed the highest antioxidant capacity. However, the samples that demonstrated the lowest phenolic content did not present the lowest antioxidant capacity, which could indicate that honey may contain other components or/and factors that could affect the antioxidant capacity.
###### Abstract  
Piezoceramic based active sensing methods have been researched to monitor preload on bolt connections. However, there is a saturation problem involved with this type of method. The transmitted energy is sometimes saturated before the maximum preload which is due to it coming into contact with flat surfaces. When it comes to flat contact surfaces, the true contact area will easily saturate with the preload. The design of a new type of bolt looseness monitoring sensor, a smart washer, is to mitigate the saturation problem. The smart washer is composed of two annular disks with contact surfaces that are machined into convex and concave respectively, to eliminate the complete flat contact surfaces and to reduce the saturation effect. One piezoelectric patch is bonded on the non-contact surface of each annular disk. These two mating annular disks form a smart washer. One of the two piezoelectric patches serves as an actuator to generate an ultrasonic wave that propagates through the contact surface; the other one serves as a sensor to detect the propagated waves. The wave energy propagated through the contact surface is proportional to the true contact area which is determined by the bolt preload. The time reversal method is used to extract the peak of the focused signal as the index of the transmission wave energy; then, the relationship between the signal peak and bolt preload is obtained. Experimental results show that the focused signal peak value changes with the bolt preload and presents an approximate linear relationship when the saturation problem is experienced. The proposed smart washer can monitor the full range of the rated preload.  
blott loosen; active sensing; smart washer; non-destructive monitoring Article
## 3 Intelligent Dynamic Offloading Algorithm
### Forest Fire Prediction  
In this context, this paper classifies works from data collection to analysis for forest fire prediction and spread prevention based on two proposed offloading.  
The works at the data centric are as follow:  
* Real-time local data collection through forest fire sensor.
* Create a forest fire knowledge database.
* Data preprocessing.  
Data collected in real-time by the sensor mainly include temperature, relative humidity, carbon dioxide, and wind direction. Also, it collects historical data such as seasons and weather and knowledge data such as mountainous terrain from external platforms. Finally, the database is designed at each boundary node by preprocessing the data, such as removing duplicate data and creating new data by merging the data.  
The works at the task centric are as follow:  
* Fire prediction.
* Wildfire spread area (direction) and velocity prediction.
* Forest fire type prediction.
* Prediction of embers landing points.
* Prediction of fire after landing.  
The above tasks are performed in the same order as above and analyzed using real-time local data, collected knowledge data, and combined new data. Since the tasks progress sequentially, fire prediction is the most important, and the bottom is the least important.  
Unfortunately, the edge node, which performs all of these tasks, is overloaded that occurs due to rapid weather changes, a large amount of data and simultaneous execution of the tasks of forest fireprediction and spread prediction. Thus, a relaxed edge node collaborates with other nodes to prevent damage or reduce the scope through quick analysis and response.