esa-sceva/satcom-chunk-collection huggingface dataset:
# Rentable CDN Using Blockchain and Proof of Provenance
## 4 Analysis
### Fee Structure  
The proposed fee structure exists to discourage malicious activities, such as colluding and cheating while encouraging fast transfer speeds and fair pricing.  
There are largely two fees: content consumption fee and content delivery fee. Content consumption fee is set by Creator, whereas the content delivery fee is a fixed amount relative to the content size. Content delivery fee can be further broken down to verification fee, delivery fee, network maintenance fee, and delivery deposit fee. The verification fee is paid to Verifier for verifying proof of pieces. The delivery fee is paid to Uploader for delivering the content to Downloader. The network maintenance fee is used as an incentive for Uploaderers to keep the content. Finally, the deposit fee, which is equal to the verification and transfer fee is allocated as well. This deposit is used to pay for the duplicate pieces and to discourage collusion and cheating. Any leftover deposit is returned to Downloader during the payment settling phase.  
Table 4 shows an example breakdown of the content delivery fee. To discourage collusion, Verification fee should be strictly greater than the delivery fee.  
The piece size is set to 0.256 mb, as this is recommended size for BitTorrent, but this can be modified as needed. The verification fee is only dependent on the number of pieces because the verification is done per proof of piece. The delivery fee is dependent on both the size and the number of the pieces since network usage is based on the content size and the number of delivered pieces. Unlike the other two fees, network maintenance fee is only dependent on the size, but this is equally shared among all Uploaders who sent in proof of pieces for that content and Verifier. This fee is provided to Uploaders for hosting the content in Storage, and Verifier for maintaining infrastructure for its verification service.  
For a simple example, suppose Uploader A and Uploader B have delivered a content of sized 256 mb (1000 pieces) to Downloader A, and \(c_{1}=0.03,c_{2}=0.01,c_{3}=0.01,c_{4}=0.5\). Then, content download contract has \(2\times 1000\times 0.03=60\) for verification fee and the deposit, \(2\times 256\times 0.01+2\times 1000\times 0.01=25.12\) for delivery fee and the deposit, and \(256\times 0.05=12.8\) for the network maintenance fee. Then, suppose Uploader A delivered 500 new pieces and 40 duplicate pieces, and Uploader B delivered 500 new pieces and 2 duplicate pieces. Then, Verifier receives \(1042\times 0.03=31.26\) for the verification fee, Uploader A receives \(540\times 0.256\times 0.01+540\times 0.01=6.7824\) for the delivery fee, and Uploader B receives \(502\times 0.256\times 0.01+502\times 0.01=6.30512\) for the delivery fee. Finally, network maintenance fee of \(0.05\times 256=12.8\) is shared evenly between Uploaders and Verifier. In total, contract held 97.92 and fee of 57.14752 was used, thus 40.77248 is returned to Downloader A.
## 4 Results and Discussion
### Lateral Distance Between Parallel Vehicles  
#### 4.4.1 Distribution Characteristics of Lateral Distance  
In Section 3.1, we classify all vehicles into two classes, car and truck, so that the combinations of parallel vehicles are of the following three types: car-car (CC), car-truck (CT) and truck-truck (TT). Relative frequency statistics for lateral distance with a group distance of 0.2 m are shown in Figure 12 (the dashed line is the fitted normal distribution curve), and the descriptive statistics are shown in Table 6. The results of the Kolmogorov-Smirnov test indicate that all lateral distances do not obey a normal distribution. All distribution curves have positive skewness coefficients, with CT being the largest. The lateral distance distribution of the TT is more centralized than the other two combinations due to the larger dimensions of the truck which limited the available lane space to adjust the lateral distance. Comparing the wheel distances at the same typical percentile for different vehicle combinations, the pattern is \"CC > CT > TT\"; for the geometric center distance at the lower percentile, the pattern is \"CC < CT < TT\", and the 75 th and 85 th quartiles follow the trend of \"CC < CT = TT\". The high percentile values of the geometric center distance for CT and TT combinations are close because some of the cars will maintain as much lateral distance from the truck as possible to ensure driving safety, space permitting. We also find that the geometric center distance of all the quartiles except the 15 th percentile is greater than the lane width of 3.75 m. This indicates that vehicles in parallel do not travel near the centerline of the lane at the same time, which does not meet the assumption that \"vehicles travel along the centerline of the lane\" in the design of the roadway.  
Figure 12: Lateral distance distribution of different vehicle combinations in parallel. (**a**) Geometric center distance; (**b**) Wheel distance.
# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
## 4 Results and Discussion
### Multicollinearity Test for the Explanatory Variables  
To determine whether the independent variables used in this paper are collinear, we subjected the explanatory variables to multicollinearity test using the variance inflation factor test. The result is presented in Table 5. The independent variables do not show the presence of multicollinearity, as none of the variance inflation factor values exceeded \(5\), which is a reasonable cut-off point for multicollinearity. Researchers have recommended variance inflation factors of less than \(5\) as an acceptable threshold for multicollinearity [74; 75].
# Research on a Dynamic Calibration Method for Photogrammetry Based on Rotary Motion
## 1 Introduction
The circular trajectory standard device can provide stable circular orbits of various rotational speeds; the photogrammetry system can take pictures of a measured target at a certain rotational speed, calculate the three-dimensional coordinates of the two marked points on the measured target, and finally realize the evaluation of the dynamic measurement performance of the photogrammetry system. The main contributions of this study are as follows:  
1. Based on the circular trajectory standard device, we propose a dynamic parameter calibration method, which can effectively realize the calibration of dynamic length measurement errors and other parameters. 2. The design error of the calibration system is analyzed based on the motion characteristic of the circular trajectory device. 3. The proposed method can provide a solution for the dynamic measurement performance evaluation of photogrammetry systems. In Section 2, we introduce the proposed method, including the calibration system and its principle. In Section 3, we analyze the design error of the calibration system. In Section 4, we describe our experiments and analyze the experimental data. In Section 5, the uncertainty of the calibration system is calculated.
## 5 Experimental Analysis
### Ablation Study  
We investigate how the number of GNN layers affects the performance of our parser. We train and test the GNNSDP(GCN) in PSD formalism datasets of English and Czech. The number of GCN layers increases from 1 to 3.  
Table 7 shows the results of GNNSDP(GCN) with different numbers of GCN layers in basic embedding settings. From the results, we can see that:  
* GNNSDP(GCN) with only one GCN layer still performs better than the biaffine parser, demonstrating that higher-order information is beneficial for improving SDP.
* The performance of GNNSDP(GCN) is gradually improved when the number of GCN layers is increased, demonstrating that stacking more GNN layers is able to capture better higher-order information.  
Figure 3: LF1 score of different sentence lengths in three formalisms. \(*\)-T represents that only the POS tag is used as feature. \(*\)-TCL represents that the POS tag, character-level, and lemma embeddings are used as features. \(*\)-TCLB represents that the POS tag, character-level, lemma, and BERT embeddings are used as features.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 4 Software Implementation
### Duty Cycling  
Wiselib's core components are implemented for the different platforms as generic Facets. Facets are interfaces that internally interact with the hardware and wrap up the device specific code with a number of generic methods common to all target platforms. For example, the Radio Facet offers the \"send\" method that sends a payload using the device's 802.15.4 radio, and the \"receive\" callback that can be used to receive incoming messages.  
The duty cycling mechanism is implemented as a standalone facet of Wiselib. It provides basic functionality so that protocols and application running on top of the operating system can easily allow the device go to sleep when no operation is required with a generic method call (allow_sleep). The allow_sleep method is internally implemented for each one of the Wiselib's platforms using the hardware specific operating system calls.  
On top of the above basic functionality, a duty cycling protocol is implemented that changes the device status from sleeping to operational based on a defined schedule. The implementation is based on the basic approach that clearly depicts an idea of duty cycling over a predefined period. The implemented algorithm requires two parameters to define the duty cycling process: a percentage rate value and a period of time over which to apply the duty cycling operation. For examples with a rate of 30% and a period of 1000 ms, the sensor device will be operational for 300 ms and go to sleep for the other 700.  
Note that the actual operation of the duty cycling algorithm slightly differs from the requested values, as timer events that are set while the device is operational causes it to wake up, execute the requested handler and then sleep again. Although these events are not supposed to be time-consuming (as the are interrupt handlers), frequent timers or interruptions can counter effect the effects of a duty cycling algorithm.
## 4 Applications
### Enzyme-Free Biosensors
#### 4.2.3 Adrenaline  
The detection of adrenaline (or epinephrine), a neurotransmitter and a hormone, is of great interest for medical and anti-doping purposes. Adrenaline level in human plasma is very low (0.2 nM) [141].  
[PERSON] et al. [121] developed an e-textile integrated biosensors encompassing a cotton yarn functionalized with PEDOT:PSS, a Pt-wire used as gate electrode and human sweat as electrolyte (Figure 16, see also Section 3.3.5). The detection of adrenaline concentration above 1 \(\upmu\)M has efficiently been performed. [PERSON] et al. have also developed a super-hydrophobic OECT device in which a finite number of micro-electrodes are independently controlled for site-selective measurements of analytes based on their size and charge [103; 104]. The competition between Marangoni convective flows and diffusion within the volume of a drop of solution deposited on the super-hydrophobic OECT enables a time- and space-resolved separation of biological species with high sensitivity, selectivity and reliability. The sensing of a solution containing both CTAB and adrenaline has been demonstrated. Finally, [PERSON] et al. [102] fabricated epinephrine sensors where the gate electrode was modified with carbon nanomaterials, including single-walled carbon nanotubes (SWCNTs), graphene and graphene oxide, which can promote electron transfer reactions and enhance the electrocatalytic activity of the gate electrodes. A detection limit down to 0.1 nM was measured with a gate modified with Nafion and SWCNTs.
## 2 Materials and Methods
### Methodology
#### 2.1.1 Phase I: Statistical Model  
The statistical analysis adopted in this study at the municipal scale is known in the literature as the \"information value method\" [35], and it is based on bivariate correlations between the independent variables (i.e., each available spatial variable derived from significant thematic maps, such as slope, aspect, flow accumulation) and the dependent variable (i.e., the slow-moving landslides inventoried in an official map). The resulting landslide susceptibility computational map is used as input for producing the final landslide susceptibility zoning map of the area based on appropriately defined terrain zoning units.  
The statistical methodology introduced herein is based on the distinction, proposed by [PERSON] et al. [30], between terrain computational units, _TCUs_, and terrain zoning units, _TZUs_. _TCUs_ refer to the spatial domains used to define a model for landslide analyses, while _TZUs_ are spatial domains used to produce a landslide map for zoning purposes. The level of discretization of the area is related, for both spatial domains, to the scale of analysis. In particular, the size of _TCUs_ is associated with the spatial resolution of the map, whereas the size of _TZUs_ is associated with the desired informative resolution of the zoning. In this study, regular square grid cells are used as _TCUs_. However, the information associated with such elementary pixels is inadequate to be directly used in a zoning map, because the associated level of discretization of the spatial domains would be too small. Therefore, focal statistical techniques are employed to obtain _TZUs_ with the same dimensions of the _TCUs_ yet containing information related to a larger area around them [36]. The value of the final zoning susceptibility index, _ISTZU_, is assigned to each _TZU_ according to the following equation:  
Figure 1: Procedure to study the interaction between slow-moving landslides and the road network at the municipal level by combining information from susceptibility maps, DInSAR data analyses and damage surveys.  
\[IS_{TZU}=\frac{\sum_{k-1}^{N}IS_{TCU(k)}}{N} \tag{1}\]  
where \(IS_{TCU(k)}\) is the computational susceptibility index of the \(k^{\text{th}}\)\(TCU\) belonging to the area of influence of the \(TZU\), and \(N\) is the number of \(TCU\)s belonging to the area of influence of the \(TZU\).  
The resulting zoning maps, whose number depends on the number of characteristic dimensions of the areas of influence considered in the analyses, are comparatively evaluated by employing ROC curves. The optimal zoning map may be considered the one that maximizes the area under curve, \(AUC\), of the ROC curves.
## 4 The Proposed Algorithm
### The Two Refining Schemas  
We use two refining schemas to improve solution quality. Both schemas work on the global best particle produced by the main algorithm, and attempt to eliminate the idle time periods of the teachers. They influence the structure of the global best by swapping randomly chosen pairs of timeslot cells belonging to the same day. The days of the week are successively processed. At each day, a certain number of swaps is performed. In the first refining schema, this number is empirically set to 750,000, while in the second one, to 800,000.  
The first refining phase is described as follows. First, the fitness for each day of the week is calculated for the global best, counting only hard constraint costs and the teacher gaps' soft constraint costs. If this fitness value equals zero, i.e., the specific day violates no constraints, the local search procedure proceeds to the next day of the week. If a day with a positive fitness value is found, then two different timeslots are chosen and all possible swaps between these are performed, considering the corresponding acceptance probabilities. However, in this case, invalid swaps that increase the fitness are not accepted. Subsequently, the fitness of the newly formed day of the global best is calculated. If the new fitness value is equal to or lower than the original value, this day's structure is copied to the structure of the global best, until a transformed day with an equal or better fitness is found.  
The only other point that differentiates the first refining schema from the second, apart from the maximum number of swaps performed, is that in the latter one, there is a back-tracking mechanism, such as the one used in the main algorithm. Indeed, the second refining procedure acts as follows: if the algorithm is unable to update the global best, the search restarts from the last successful updating point. As it is easily derived, any modification caused due to these swaps cannot alter the violation cost of class-dispersion and/or the cost of teacher-dispersion, since these swaps take place within a day, while the two aforementioned constraints are computed based on the entire week's timetable. Hence, such swaps could only improve the global best fitness by reducing the number of teacher idle periods. Please note that each refining schema is triggered only if there are teacher idle periods in at least one day of the week.
# Fake Base Station Detection and Link Routing Defense +
## 8 Future Directions Discussion
### Impacts on Multimedia, Teleinformatics, and IoT Communications  
Cellular networking based on base stations, such as 4G LTE and 5G NR, is popularly used for multimedia, teleinformatics, and IoT communications. In urban and developed communities, in contrast to wireless local area network (WLAN) or Wi-Fi based on access points, cellular networking coverage is not limited to local areas and therefore supports communication applications across larger geographical regions, for example, across towns and cities. In some underdeveloped communities lacking the WLAN infrastructure, cellular networking provides the dominant communication infrastructure to support multimedia and IoT applications.  
The disruption of the connectivity provision and availability by fake base stations, our subject threat in this paper, disables the communication infrastructure operations and thus disables the applications building and relying on the infrastructure. Therefore, against successful fake base station threats, there is no data handling or services for any communication applications, including multimedia and IoT applications. Our work enables the recovery against such threats by detecting, blacklisting, and avoiding fake base stations, i.e., detection triggers reconnection and link routing to avoid fake base stations. As a result, the user equipment can recover from a fake base station attack and connect to a different, legitimate base station to enable connectivity provision and multimedia, teleinformatics, and IoT applications. Future directions include the impact of investigations of our defense scheme implementation on such applications (especially those with mission-critical or low-latency requirements, c.f., 5G Ultra-Reliable Low-Latency Communication, or URLLC) and the design and implementation advancements to fulfill the application requirements in availability, reliability, latency, or bandwidth.
## 2 Materials and Methods
### Assessment of PEF-Mediated Conductivity Changes  
For PEF treatment, the potato samples were bisected along their longitudinal axis. The two-needle electrodes were inserted 1.3 cm deep into the perimedular area, perpendicularly to the cut plane. A previously established set of IRE pulse parameters was utilized as a standard IRE protocol and starting point for our experiments, which included NP = 70 unipolar pulses with an applied voltage of PV = 1000 V (and, consequently, a maximum electric field strength of 1000 V/cm), a pulse duration of PW = 100 \(\upmu\)s, and a pulse interval of PI = 100 ms.  
In four sets of experiments, one parameter from the standard irreversible electroporation protocol, i.e., NP, PI, PW, or PV, was varied at a time, while the remaining parameters were kept at the standard values to assess PEF-mediated conductivity changes. Table 1 summarizes the four sets of experiments alongside with the temperature and conductivity of the standard solution. Each set of experiments was performed on six different potatoes (N = 6). Consequently, up to 10 different PEF treatments were conducted per sample, along with two untreated control measurements, on the available perimedullar area. The dimensions of the potatoes were measured using a digital caliper (Forum, E/D/E GmbH, Wuppertal, Germany) with a resolution of 0.01 mm.  
To verify that the PEF treatment did not cause any tissue heating, the temperature of the potato tissue close to one of the electroporation needle tips was measured after each PEF treatment by inserting a K-type temperature probe of the Multimeter into the needle hole, approximately 10 s after retracting the electroporation needle. Immediately afterwards, i.e.,approximately 30 s after PEF treatment, the EIS spectrum was recorded. To that purpose, the four-point probe was positioned vertically about 1 cm deep in the potato tissue, in between the marks left by the electroporation needles.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 2 Methods
### Encoder  
As shown on the left part of Figure 2, the patch partition divides the image into the non-overlapping patches. After the patch partition, the input image is reshaped to be \(\frac{W}{4}\times\frac{H}{4}\times 48\), where \(W\) and \(H\) refer to the width and height of the original input image. The number of image patches is \(\frac{W}{4}\times\frac{H}{4}\). Then, the patches are sent into the linear embedding layer. The linear embedding layer produces tokenized input with the dimension of \(\frac{W}{4}\times\frac{H}{4}\times C\). A consecutive structure of dual Swin Transformer blocks embedded with SF module is developed, which is shown in Figure 4. Following three groups of such a structure and the patch merging layer, the encoder outputs the feature of \(\frac{W}{32}\times\frac{H}{32}\times 8C\). In the process, Swin Transformer blocks are responsible for the representation learning without changing the size of feature dimension and resolution. Then, the patch merging layer divides the input patches into 4 equal parts and concatenates them together. The feature size is downsampled by \(2\times\) after the procedure. To reduce the dimension of concatenated features from \(4\times\) to \(2\times\), a linear layer is utilized in the patch merging layer.