esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 IEEE 802.15.6: The Key-Enabling Standard for WBANs
### Coexistence and Interoperability  
In the IEEE 802.15.6 proposal, a WBAN is defined as a pico-area network, with a coordinator and a set of associated sensors in a TDMA mode. To ensure a real-time efficient transmission, the WBAN coordinator allocates, in the Contention Free Period (CFP), one dedicated time-slot for each sensor in the WBAN. In this case, as long as no other WBANs are present in the close vicinity, Inter-WBAN interference is avoided, reducing thus the packet error rate (PER) of Intra-WBAN communications. Nevertheless, when multiple WBANs are co-located, since none of them has the ability to stop the transmission of the others, the WBANs obviously interfere with each other (_i.e._, Inter-WBAN interference).  
Intra-WBAN and Inter-WBAN coexistence can be typically be classified as collaborative and non-collaborative. In collaborative coexistence, there is coordinator-to-nodes (for Intra-WBANs), and coordinator-to-coordinator (for Inter-WBANs), sharing of important information to avoid interference. Typical information includes the channel number, the operating frequency, the transmission power, _etc_. In non-collaborative coexistence, WBANs do not share any information for interference mitigation. Since the coordinator controls the Intra-WBAN communications, the different WBAN nodes have a low probability of operating at the same time over the same channel. However, chances of interference between WBANs are much higher in the non-collaborative case.  
With reference to the MAC header of the general frame format of the standard, there is a field called 'frame control' consisting of four octets (as shown in Figure 8), which contains the key information about interference and interoperability as well as the various security levels. More details on the 'frame control' can be found in section 5.2.1.1 of the standard [9].  
The standard proposed three different non-collaborative techniques, according to the frequency band used and the mobility of the network that a coordinator could employ for coexistence and/or interference mitigation between Intra-WBANs and Inter-WBANs. These include _beacon shifting_, _channel hopping_ and _interleaving_ which are explained (in brief) below. The coexistence format (which is part of the frame control field of the MAC header) is illustrated in Figure 15.  
The Beacon Shifting field is set to one if beacon shifting is currently enabled, or is set to zero otherwise. Similarly, the Channel Hopping field is set to one if channel hopping is enabled, or is set to zero otherwise. The super frame Interleaving field is set to one if the sender (a coordinator) supports active super frame interleaving and command frames, or is set to zero otherwise.
## 2 The RG8 Test
### Blocks Subjected to Restrained Shrinkage  
Three blocks (RG8, RG9 and RG10) were designed to study the restrained shrinkage of massive concrete structures at an early age. Produced using the same concrete (C50/60), RG8 served as the reference, while RG10 used a thicker concrete cover (5 cm instead of 3 cm) and the RG9 reinforcement ratio was reduced (to 0.56% instead of 2%).  
The RG specimens were divided into three parts (Figure 1):  
* A central block (5.10 m \(\times\) 0.80 m \(\times\) 0.50 m), plus a 0.4-m transition zone on each side with a gradual increase in the width. The reinforcement map is given in Figure 1a; in the cross-section, it includes a combination of 32-mm diameter (10) and 12-mm diameter (4) reinforcements; stirrups are 16 mm in diameter, and they are spaced at 200 mm.
* Two heads (0.9 m \(\times\) 2.2 m \(\times\) 0.9 m) serve to support the struts described below;
* Two steel struts fastened on the two heads to restrain strain evolution, as shown in the picture in Figure 1.  
Due to hydration effects, the evolution in both temperature and shrinkage was substantial in such a massive structural element. The purpose here is to show how this evolution, in a situation where shrinkage is restrained, promotes the formation of cracks prior to any external mechanical loading. The two steel struts restrain shrinkage of the central part of the beam and, consequently, cause cracking at an early age.  
All specimens were fully instrumented (9 internal temperature sensors, 24 vibrating cords, 3 internal optic fibers, 12 strain gauges on the rebar, etc., see [2; 3] for details). The external temperature and solar exposure of the specimen were also measured during the test.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 3 6G-IoT: New Technologies and Emerging Services
### Edge Computing and Pervasive Artificial Intelligence  
Digital network devices, even those at the network's edge, have increasingly sophisticated computing capabilities. Therefore, we can utilize their computing power to assist with and, under certain conditions, optimize network performance [56; 57]. Moreover, with the advent of AI, these devices will be able to extract information and expertise from the data they communicate with other entities on the network, identifying and categorizing services using learning models. Edge AI applications have emerged as a significant area of research, demonstrating their effectiveness in a variety of agricultural applications. For instance, cutting-edge computer vision methods utilizing deep learning can facilitate the extraction of navigation lines in intricate farmland environments for field robots [58]. Moreover, the integration of lightweight neural networks and edge computing enables real-time crop detection by harvesting robots [59].  
The future of edge AI also involves a shift in the traditional approach of training models on powerful servers and then deploying them to edge devices. With the advent of federated learning (FL), the training process becomes decentralized, and the training data are stored on the edge devices where they originated [16]. In this approach, edge devices contribute to the training process by sending local model updates computed using their own data to a central server. The central server aggregates updates from multiple devices, modifies the global model, and then distributes the updated version to the edge devices. This iterative procedure continues until the global model reaches the desired performance level. As a result, the need for data transmission from the network's edge to the cloud is significantly reduced, leading to increased security and an accelerated training process [60; 61].  
Overall, edge computing and edge AI offer the benefits of local data processing, which reduces the need to send data to the cloud. This, in turn, helps to alleviate the traffic load on the backbone network. However, it is important to note that edge computing and edge AI cannot be universally applied to all deployment scenarios. They are primarily designed to provide immediate responses for real-time applications, such as navigation, coordination, and collaboration of autonomous vehicles and robots in farming operations. On the other hand, they are not well-suited for data-intensive tasks like large-scale analytics, high-resolution image processing, and augmented reality applications. Consequently, for complex tasks that require significant computational resources, it is still necessary to transmit data to the cloud. The extent to which edge computing and edge AI can reduce the need for communication with the cloud is dependent on the requirements of the specific application, the nature of the data, and the capabilities of the edge device.
## 3 Results and Discussion  
### Rheological Parameters  
PPHPSCCs mixes were evaluated for fresh parameters such as filling ability throughout slump flow test as well as passing ability during L-box test. The parameters of fresh mixes are listed in Table 3.  
Figure 3 illustrates the typical slump flow diameter measurement and height of L-box filling. The slump flow diameters of the reference HPSCC and PPHPSCCs mixes are shown in Figure 4. The HPSCC control mix reached the maximum flow diameter of 722.5 mm. The diameter of PPHPSCC mixes flow were within the range of 570-715 mm. Except for the PPHPSCC-0.25 mix with the highest content of PP fibres, the slump flow diameter of other mixes complied with the requirement of European guidelines for SCC [41]. The HPSCC, PPHPSCC-0.025, and PPHPSCC-0.05 mixes were categorised as SF2 class, as the slump flow results were in the range of 660-750 mm. The mixes of class SF2 are appropriate for columns and walls. On the contrary, the PPHPSCC-0.075, and PPHPSCC-0.125 mixes were categorised as SF1 class due to the obtained flow diameters between 550 and 650 mm. The mixes of SF1 class are appropriate for tunnel linings, housing slabs, deep foundations and piles.  
All the mixes of PP-fibre-reinforced concrete reached the maximum flow diameter that was lower than the control mix, which indicates a negative influence of the PP fibre inclusion on the workability (Figure 3a). The impact of increased PP fibre fraction on worsened flowability of the HPSCC corresponds to previous conclusions [29; 45; 46; 47]. The T\({}_{500}\) time given in Table 3 shows mixes with high viscosity. Each of the mixes was characterized by a flow time of 4.1 to 5 s. Figure 5 shows the L-box blocking ratio results of the reference HPSCC and PPHPSCCs mixes. These ratios (H\({}_{2}\)/H\({}_{1}\)) were obtained as values between 0.63 and 0.94 for all groups of mixes. The results indicate that the PP fibres decreased blocking ratios i.e., passing abilities, but there were in the range of 0.8-1, and in accordance with the guidelines [41], except the PPHPSCC-0.25 mix.  
Figure 4: Variation of slump flow diameter with different PP fibre volume content.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Designation** & **HPSCC** & **PPHPSCC-0.025** & **PPHPSCC-0.05** & **PPHPSCC-0.075** & **PPHPSCC-0.125** & **PPHPSCC-0.25** \\ \hline Slump flow & & & & & & \\ (mm) & 722.5 & 715 & 705.5 & 580.5 & 570 & 485 \\ T\({}_{500}\) (s) & 4.1 & 4.3 & 4.5 & 4.8 & 5 & — \\ L-box, PA & 0.94 & 0.90 & 0.88 & 0.85 & 0.82 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fresh properties of HPSCC and PPHPSCC.  
The tendency of the results obtained on the basis of testing the rheological properties of the mixes is consistent with the research of [PERSON] [48]. Based on the rheometric analysis of the workability of the mixes with the addition of polypropylene fibres with a length of 3, 6 and 12 mm and the volume content of 0.1%, 0.2%, 0.3%, an increase in the viscous flow resistance was observed along with the increase in fibre content and its decrease after increasing the dose of superplasticizer.
# Coupled System of Dual-Axis Clinostat and Helmholtz Cage for Simulated Microgravity Experiments
## 2 Design and Construction
### Cultivation Experiments  
To investigate the clinostat's effect on plant growth, we performed several preliminary cultivations (Figures 14 and 15). We used _Lepidium sativum_ L. (Figure 14) and _Phaseolus coccineus_ L. (Figure 15) as test plants because they are easy to grow and have a short vegetation period. The figures present the two one-week growth results in the clinostat and in the reference static sphere. Videos of the cultivation process of _Phaseolus coccineus_ L. can be obtained on request from authors. Gravitropism is highly visible in all the plant's parts: stems, roots, and leaves. Cotton was used as a substrate material because of its good water-retention capabilities. In the first experiments to block the movement of the breeding, a properly perforated foil with holes was used. This is not the only solution. It is also possible to use the appropriate mesh made of plastic material. The results have preliminary characteristics, and future experiments in a variety of circumstances will be conducted, especially with the inclusion of magnetic-field variation.  
Figure 14: Results of _Lepidium sativum_ L. cultivations: (**a**) cultivation in the clinostat with humidity 35% and light 10% of its maximum value, (**b**) cultivation in the clinostat with humidity 75% and light 8% of its maximum value; the static cultivation results (**c**) and (**d**) corresponding to the conditions of (**a**) and (**b**), respectively.  
Figure 15: Results of _Phaseolus coccineus_ L. cultivations. Each left panel shows results for normal gravity, while a right panel shows the simulated microgravity conditions. The photographs depict levels of growth after (**a**) 6 days, (**b**) 10 days, (**c**) 13 days, and (**d**) 14 days. In zero-gravity conditions, the plant cannot develop a root system around the dominant direction. Its growth is retarded, and the emerging leaf growth is chaotic.
## 3 System Design
### Network Model
The parameters, namely, throughput, delay and data delivery ratio are the main consideration of this mechanism, which helps to detect the optimal path for transmitting the data to the BS. Thus, this technique enhances the performance of the WMSN in multiple aspects, particularly by increasing the lifespan and reducing energy consumption. Based on the QoS demands, the mechanism necessitates the data to flow through trouble free paths for delay sensitive and time critical data packets.
## 4 Experimental Results
### Evaluation Metrics  
To evaluate the performance of the weld seam recognition network in complex environments, we selected several common evaluation metrics, including \(mPre,mRecall,\)\(mIoU,\) and \(F1\_score\).  
Mean precision (\(mPre\)) measures the proportion of correctly identified weld seam pixels in the areas predicted as weld seams via the model. The formula for \(mPre\) is as follows:  
\[mPre=\frac{1}{N}\sum_{i=1}^{N}\frac{TP_{i}}{TP_{i}+FP_{i}} \tag{18}\]  
Figure 3: (**a**) Original welding image; (**b**) mirror transformation including horizontal and vertical flipping; (**c**) random rotation with an angle range of \(\pm\)45\({}^{\circ}\); (**d**) addition of Gaussian noise to help the model learn to handle different types of interference.  
where \(TP\) represents the number of pixels correctly identified as weld seams, and \(FP\) represents the number of pixels incorrectly identified as weld seams. \(mPre\) helps assess false positives, especially in complex noisy environments.  
Mean recall (\(mRecall\)) measures the proportion of correctly identified weld seam pixels in all the actual weld seam areas. The formula for \(mRecall\) is as follows:  
\[mRecall=\frac{1}{N}\sum_{i=1}^{N}\frac{TP_{i}}{TP_{i}+FN_{i}} \tag{19}\]  
where \(FN\) represents the number of pixels incorrectly classified as background noise. \(mRecall\) reflects how well the model detects all weld seam areas.  
The mean intersection over union (\(mIou\)) measures the overlap between the predicted weld seam area and the actual weld seam area. This is important because weld seams often have long, curved, or complex shapes, requiring high boundary recognition accuracy. The formula for \(mIou\) is as follows:  
\[mIou=\frac{1}{N}\sum_{i=1}^{N}\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}} \tag{20}\]  
where \(TP\) represents the intersection of the predicted feature area and the true feature area. \(mIoU\) helps assess how accurately the model captures the weld seam boundaries and shapes.  
\(F1\_score\) provides a balanced measure of \(mPre\) and \(mRecall\) through their weighted harmonic mean. The formula for \(F1\_score\) is as follows:  
\[F1\_score=\frac{2\cdot mPre\cdot mRecall}{mPre+mRecall} \tag{21}\]  
where \(F1\_score\) balances \(mPre\) and \(mRecall\), making it especially useful for tasks where both false positives and false negatives are critical. These evaluation metrics and their formulas comprehensively measure the model's performance. They reflect both the model's recognition accuracy and its robustness in complex backgrounds, ensuring the weld seam recognition model's effectiveness and stability in industrial applications.
## 3 Survey of Techniques for Updating
### Update Fault Detection and Recovery
#### 3.4.2 Over-the-Air Programming (OTAP)  
This was developed to support the requirements of scalability, fail-safety, reliability, and applicability for resource constrained hardware [83]. Two firmware images are stored in EEPROM, one for temporary storage of new software, and one for recovery (a _fallback_ image). The OTAP Agent uses existing network stacks for communication: it requires 1-to-1 bi-directional communication with a management station for control, and 1-to-N communication from the management station for data (the update). Before dissemination, a preparation request is sent to the selected targets. The update is then transmitted using the 1-to-N network functionality, in multiple packets sized according to the underlying protocol capabilities, using Reed-Solomon coding for redundancy to reduce the performance impacts of unreliable transmission, which are restored using Gaussian elimination and stored in the temporary image section of EEPROM. Afterwards, the network manager polls each target for missing segments, which are re-sent using 1-to-N communication to enable the efficiencies of overhearing. The bootloader is designed to be fail-safe, the image is verified, power-failure and recovery will never result in an inconsistent state (stored in EEPROM), and unexpected resets are handled. Beacons are periodically broadcast by the management station; a sequence of missing or invalid beacons lead to a reset and a reprogram--connectivity loss is used as an indicator for a possible software failure. The OTAP Agent runs as an application, receiving these beacons (sent using the 1-to-N protocol), and storing them in an area of shared memory to be checked by the bootloader periodically (using its reserved timer). The hardware watchdog and a timer are reserved for the bootloader; updates are checked to ensure they do not attempt to access the watchdog timer, and the interrupt vector table is overwritten to ensure the bootloader maintains access for resets.  
Direct support for autonomous operation is provided through the use of beacons (as in [82]) where loss of software update communication indicates a software fault and results in recovery to a fallback image. Feedback of the dissemination is provided through end-to-end ACKs, and a network-wide fallback can be achieved by halting the transmission of beacons from the management station.
## 1 Introduction
Towards to this goal, we have a profound study of the inter-user interference about its prevalence and significance in realistic BSN deployment scenarios in this paper. The main contributions of this paper are as follows: First, the significance of inter-user interference is investigated from the perspective of its prevalence and resultant performance degradation in a realistic BSN deployment. Second, the effects of BSN density, traffic load, and transmission power on the inter-user interference are studied quantitatively. The rest of this paper is organized as follows. In Section 2, we review the related works on BSNs. Section 3 depicts network model in this paper. In Section 4, we characterize the inter-user interference in terms of packet error rate (PER). In Section 5, we describe a case study in a realistic BSN deployment in hospital and show the results on performance evaluation of the wearable sensor system. Section 6 provides discussion. Section 7 draws the conclusion.
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## 5 Applications to the Internet of Things
### Applications in Sensors Networks  
Regarding sensor networks, the proposal presented here can be interesting, specifically for its application in wireless sensor networks (WSNs). WSNs have evolved significantly in the last few years, generating a promising research area about a fruitful and useful technology. This technology involves  
Figure 3: Types of MANET nodes.  
two types of entities: Sensor nodes and base stations. In general, base stations are more powerful than sensor nodes, but this trend is changing thanks to advances in low-powered technology.  
Nowadays, it is possible to create a WSN using platforms like Arduino, Raspberry Pi, Odroid, Intel Edison, _etc._, which can have several sensors. Since some applications in WSNs only require the information of a specific sensor, the scheme proposed in this paper can be used for these platforms to send the sensor information independently and in an authenticated way by using a broadcast beacon to send the data of each sensor. Thus, sent data will only be accessible to legitimate entities.  
The applicability of the proposed solution is more to sensor platforms, such as those coordinated through Arduino, than to individual sensors, because Android can be installed in them. Thus, the secure communications based on the proposed scheme can be used on any sensor platform based on Arduino, such as Odroid.  
Besides, the proposed authentication scheme can be used to complete other solutions and to add a layer of security. For example, existing WSN models based on preloaded parameters, like broadcast keys for sensor nodes, can be improved by using the scheme presented in this paper to generate shared keys and using them as broadcast keys for the sensor nodes.
# Heavy Metals in Urban Street Dust: Health Risk Assessment (Lublin City, E Poland)
## 2 Materials and Methods
The equations for these parameters take the following form:  
\[\text{ADD}_{\text{inh}}=\text{C}\times\frac{\text{inR}\times\text{EF}\times \text{ED}}{\text{PEF}\times\text{BW}\times\text{AT}}\]  
\[\text{ADD}_{\text{derm}}=\text{C}\times\frac{\text{SL}\times\text{SA}\times \text{AB}\times\text{EF}\times\text{ED}}{\text{BW}\times\text{AT}}\times 10^{-6}\]  
\[\text{ADD}_{\text{ing}}=\text{C}\times\frac{\text{inR}\times\text{EF}\times \text{ED}}{\text{BW}\times\text{AT}}\times 10^{-6}\]  
where C is the concentration of the element, ingR is the age-dependent ingestion rate, inhR is the age-dependent inhalation rate, EF is the frequency of exposure to pollutants, ED is the exposure duration, SA is the skin area exposed to pollutants, SL is the skin adherence factor, ABS is the dermal absorption factor, PEF is the particle emission factor, BW is the average body weight, and AT is the average exposure time (Table 1). Figure 1: Locations of sampling sites against the background of the Lublin road network (and traffic density): 1, main roads; 2, secondary roads; 3, 101–500 cars per hour; 4, 501–1000 cars per hour; 5, 1001–1500 cars per hour; 6, >1500 cars per hour; and 7, sampling sites. The hazard index is the sum of the quotients of the three forms of pollutant intake divided by the corresponding element-specific reference doses (Table 2). It is calculated using the following equation:  
\[\text{HI}=\left(\frac{\text{ADDi}}{\text{RfD}}\right)_{\text{ing}}+\left(\frac{ \text{ADDi}}{\text{RfD}}\right)_{\text{inh}}+\left(\frac{\text{ADDi}}{\text{RfD }}\right)_{\text{derm}}\]  
where ADDi is the dose of element i, and RfD is the reference dose of the element. The proportions in this equation refer to non-carcinogenic effects. The probability of such effects occurring is very low for HI < 1, between 1 and 4 the risk of non-carcinogenic effects is possible, for HI > 4 the risk is high [26; 28]. Lifetime average daily dose (LADD) is represented with equations identical to ADD, only with the parameter AT changed to AL (average life) [43]. For carcinogenic substances, i.e., As, Cd, Cr, Ni, etc., LADD values are multiplied by an appropriate slope factor (SF), and thus the cancer risk range (CR) is obtained:  
\[\text{CR}=\text{SF}\times\text{LADD}\]  
These results are considered hazardous when cancer risk ranges (CR) are greater than \(10^{-6}\)[26] or \(10^{-4}\); a definitive lower risk limit is still to be established [24]. In this study, health risk (hazard index and cancer risk) was determined for both adults and children. Basic descriptive statistics (mean, standard deviation and variation coefficient) of health risk indicators were calculated. HI and CR spatial distribution maps were prepared with the Spatial Analyst (QGIS) package through IDW interpolation using the collected data.