esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Quality Changes in Cement-Lined Water Pipe Networks
###### Abstract  
This research focused on a model setup fed with underground water pumped into a water supply network. The scope of the research included a long- and a short-term stagnation of water in the setup and a water flow of 0.1 m/s. The water supplied into the system gradually lost its corrosive properties and developed calcium carbonate precipitation capability. These processes indicated that cement components migrate from the lining into the water. Apart from the primary components, the presence of the following trace elements was found: iron (399.6 ppb), magnesium (17.8 ppm), zinc (26.9 ppb), barium (22.6 ppb), boron (21.6 ppb), sodium (10.8 ppm), gallium (2.1 ppb). At early stages of the operation, recorded quantities were not significant. Leaching of the trace elements from cement increased after water was chlorinated. In turn, the highest percent increases were observed in the case of zinc (217%), boron (19%) and gallium (12%), whereas the increase for sodium, iron, barium and magnesium in water was insignificant (reaching a few percent). A repeated analysis of samples of stagnant water in the setup showed that most of trace elements were below the detectability level. Final concentrations of all elements identified in the water after its contact with internal cement mortar lining were much lower than the limit values set out for drinking water.  
water quality; internal cement mortar pipe lining; secondary water contamination +
Footnote †: journal: Material and Materials Research Tables
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 8 LoRa/LoRaWAN/Wi-Fi Use Cases
### USV+UAV: Ensuring an Ad Hoc Data Link for Devices in Faraway Regions of the Delta  
Boats and drones can act as temporary access points to ensure a data link between some access regions. For example, local people who possess agricultural lands monitored using agricultural sensors can remotely verify the status of parcels using an ad hoc network created when the boats pass through the area covered by the wireless sensors network for smart agriculture.
## Notation and Preliminaries  
### Invariants  
We first define the concept of _invariants_ by borrowing the notation and definition in [27].  
**Definition 1** (Invariants).: _In one geometric problem, assume that we obtain \(N\) putative correspondences whose indices are in set \(\mathcal{C}=\{1,2,\ldots,N\}\). Let \(\mathcal{S}\subset\mathcal{C}\) denote a subset of \(\mathcal{C}\) with a fixed size, \(x\) denote the vector stacking all variables, \(\mathbf{y}_{\mathcal{S}}\) denote the measurements w.r.t. subset \(\mathcal{S}\), \(\mathbf{\eta}_{\mathcal{S}}\) denote the corresponding noise measurements independent of \(\mathbf{x}\), and \(\mathbf{h}_{\mathcal{S}}(\mathbf{x},\mathbf{\eta}_{\mathcal{S}})\) denote the mathematical model that is used to define the relation between \(\mathbf{x}\) and \(\mathbf{y}_{\mathcal{S}}\) given by \(\mathbf{y}_{\mathcal{S}}=\mathbf{h}_{\mathcal{S}}(\mathbf{x},\mathbf{\eta}_{\mathcal{S}})\)._  
_If we find a function \(\mathbf{f}(\mathbf{y}_{\mathcal{S}})\) where \(\mathbf{f}(\mathbf{y}_{\mathcal{S}})=\mathbf{f}(\mathbf{h}_{\mathcal{S}}(\mathbf{x},\mathbf{\eta}_{ \mathcal{S}}))\) is always satisfied and also independent of \(\mathbf{x}\) no matter which \(\mathcal{S}\subset\mathcal{C}\) is selected, we can consider \(\mathbf{f}\) as an invariant function, also abbreviated as an invariant._
## 1 Introduction  
[PERSON] and [PERSON] (1986) defined stress as, \"A particular relationship between the individual and the environment that is evaluated by that one as threatening or overflowing of its resources and that puts endangering their well-being.\" Stress has different meanings and can develop as a result of different types of situations and relationships, such as work, family and academics [1].  
New university students, especially those in health sciences programs, report high levels of stress with negative health consequences related with academic performance and wellbeing [2; 3].  
First, this results from the fact that they are facing a new, totally unknown environment that is different from their secondary education environment, which forces behavioral changes in order to adapt to the new setting [4]. Second, the responsibility that comes with dealing with patients and their pathologies is extremely stressful [5].  
One of the factors most clearly linked to the appearance of stress in new students is associated with work overload [6]. It is necessary to differentiate between an excess of work (quantitative overload) and the excessive difficulty and complexity of it (qualitative overload). As a consequence of the limited amount of time that can be dedicated to their studies, students' personal limitations, a curriculum with an excessive number of subjects and/or excessive demands, in addition to other reasons, students can become overwhelmed [7].  
Determination of the negative impacts related to the best options to fight stress during the university years and posterior working life, may improve the overall health and the mental wellbeing of people, and their sanitary work health [8].  
In the context of pediatric studies, there are no reliability and repeatability measurement instruments to quantify stress in students. We cannot find many studies that measured stress in podiatry students using general scales for measuring stress, such as the Undergraduate Sources of Stress Survey (USOS) [6; 7; 8; 9], or test-retest reliability tools or ad hoc qualitative scales [10].  
One reliability and repeatability instrument which showed adequate psychometric properties was the tool named Assessment of Stress in Nursing Students (ASNS) [11]. This tool comprised 30 items and six domains: (1) practical activity performance domain; (2) professional communication domain; (3) time management domain; (4) environment domain; (5) professional education domain; and (6) theoretical activity domain. The factor analysis of this tool's psychometric properties showed that the instrument is reliable, resulting in Cronbach's alpha (\(\alpha\)) values that ranged from 0.71-0.87 [12].  
This tool was previously used to determine stress parameters in nursing students, leading to excellent findings [12; 13; 14].  
This instrument has appropriate domains for measuring stress in the podiatric context; thus, this study aimed to perform the transcultural adaptation of the nursing items to the podiatry context to create the Spanish version of the tool named Assessment of Stress in Podiatry Students (ASPS) and test its reliability and repeatability [15; 16].
## 4 Results and Discussion
### Facies Identification  
Facies was characterized by using well log data, including core and well log report. Facies in well logs were distributed based on petrophysical logs cut-off. The input data in facies identification were the volume of shale, the volume of coal, porosity, and water saturation (Sw). Based on the interpretation, facies were characterized as the gas sand, brine sand, shale, and coal that had different cut-off. Gas sand had less than 0.4 volume of shale, less than one of the volumes of coal, less than 0.7 of effective water saturation, and more than 0.05 effective porosity proposition. Brine sand had less than 0.4 of the volume of shale, less than one of the volumes of coal, and more than 0.7 effective water saturation proposition. Shale had more than 0.4 volume of shale proposition; meanwhile, coal had less than 0.4 volume of clay and more than one volume of coal proposition.  
Figure 8: Seismic and well log comparison for interval D + 49, which was shown in blue rectangles, and D34/36, which was shown in red rectangles (**a**) Seismic cross section which was penetrated by well log I-8, (**b**) well log tracts from left to right gamma-ray, neutron and density, resistivity, acoustic impedance, s-wave and p-wave, and facies.  
Figure 9 illustrates the facies proportion in wells where facies characteristic in this research area was dominated by shale interbedding with sand. Different color codes were assigned to interpret facies to provide better representation in defining the lithologies. Red was for gas sand, blue was for brine sand, grey was for shale, and black corresponded to coal. The predicted facies was validated by core reports to ensure the result was reliable. The comparison showed a great similarity between the predicted log from petrophysical logs cut-off and measured data from the core reports. The facies logs were later used to validate seismic responses.
# Bounce: A High Performance Satellite-Based Blockchain System
## 2 Related Work
### Miniature Satellites and Their Uses  
With launch costs as low as $300 k [24], and the increased availability of commercial off the shelf (COTS) components, miniature satellites are becoming more common. For example, miniature satellites (a few centimeters on a side), called CubeSats, have becomewidespread [25; 26; 27]. One can build one's own ground station using commercial parts and make use of Ground Stations as a Service [28; 29; 30]. Even big banks have become involved. J.P. Morgan Onyx [31] deployed Consensusy Quorum [32], an Ethereum-based blockchain, to a Low Earth Orbit satellite, and then executed a smart contract on it.  
The _Bounce_ protocol is partly inspired by the work of SpaceTee [33] which puts a hardware security module (HSM) in a CubeSat. The authors of the paper founded a company called CryptoSat, and they launched their own satellite [34], and have been providing cryptographic services such as supplying randomness to Ethereum KZG [35; 36].  
Authors of [37] use satellites as an extra data transmission channel to speed up the transmission of blocks. Authors of [38] designed a satellite-aided consensus mechanism for permissionless blockchains, which used random oracles sent from satellites to select the next block proposer. If the selected block proposer fails to generate a block (e.g., due to missed oracles or satellite link outages), the protocol allows the blockchain to continue evolving. This might lead to temporary forks or orphaned blocks, and the system resolves these through Proof of Work-like longest-chain selection rules. However, users can collude to cause a fork.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Environmental Applications
#### 2.3.1 Water Monitoring  
Water, either for drinking or oceanic is an important factor in human lives, therefore the monitoring of water has a great academic interest for researchers as described below.  
The researchers in [18] designed a WSN application to evaluate the quality of fresh drinkable water. They designed a Cyber physical system (CPS) called PipeSense, which is an in-pipe system for  
Figure 10: The subcategories of the environmental WSN applications and the types of the sensors used in them.  
Figure 9: A monitor that can both display and transmit vital signs [17].  
water monitoring that utilizes RFID (Radio Frequency Identification) based WSN. The network can provide information about water demand or water quality and various repair information such as weak spots or pipe leakage. The in-pipe RFID sensors collect information from the system and send them to the data servers, where algorithms provide decision support.  
A WSN application for marine environment monitoring is presented in [19]. In order to prevent damage to the flora and fauna of a fish farm from feed and fecal waste, the authors designed an Underwater WSN (UWSN) with ground based wireless sensor nodes capable of monitoring the pollution of the farm. The sensor nodes are mobile in a limited space in order to measure a greater area.
## 3 Results and Discussion
### Water Stability Performance  
In addition to high-temperature rutting and low-temperature cracking, water damage is also a common problem in asphalt pavements. As a commonly used cold patch, (modified) emulsified asphalt mixtures are prone to water seepage into the interior when repairing a pit, producing vacuum negative pressure or hydrodynamic pressure inside and continuously washing the mixture. This can eventually lead to spalling, traveling, or more serious distress in the asphalt pavement. Based on the importance of water stability, the four mixtures were analyzed by water-immersion Marshall and freeze-thaw splitting tests.
# An Experimental Study on Repairing of Reinforced Concrete Beams Having Damaged Longitudinal Bars
## 3 Results and Discussion
### Load-Displacement Curves
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & \begin{tabular}{c} **Prc** \\ **(kN)** \\ \end{tabular} & \begin{tabular}{c} **Pu** \\ **(kN)** \\ \end{tabular} & \begin{tabular}{c} **\(\Delta\)y** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **\(\Delta\)u** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **K** \\ **(kN/mm)** \\ \end{tabular} & \
\begin{tabular}{c} **E** \\ **(kN-mm)** \\ \end{tabular} &
\begin{tabular}{c} **Failure** \\ **Mode** \\ \end{tabular} \\ \hline RefB & 22.33 & 71.39 & 7.6 & 139.3 & 18.3 & 10.1 & 9411 & SY-CC \\ RefB-D & 0.59 & 7.89 & 19.5 & 27.8 & 1.4 & 0.9 & 179 & CF-CC \\ RB1-SNSM-S & 20.80 & 57.76 & 5.8 & 11.9 & 2.1 & 10.8 & 486 & SY-CC \\ RB2-SNSM-G & 12.95 & 26.54 & 8.8 & 43.0 & 4.9 & 3.5 & 1142 & B/M-CC \\ RB3-NSM-G & 19.70 & 40.16 & 7.5 & 30.6 & 4.8 & 4.8 & 2091 & B/M-CC \\ RB4-NSM-G-U & 20.00 & 43.94 & 8.9 & 47.7 & 5.4 & 5.1 & 2068 & B/M-CC \\ RB5-EBR-CS & 16.74 & 43.91 & 8.7 & 15.8 & 1.8 & 6.3 & 767 & E/C \\ RB6-EBR-CS-U & 18.10 & 59.11 & 16.7 & 33.3 & 2.0 & 4.8 & 1971 & TR \\ RB7-EBR-S & 27.96 & 87.92 & 5.9 & 28.0 & 4.7 & 14.7 & 2687 & SY-M/C \\ RB8-EBR-G & 17.96 & 32.56 & 9.0 & 9.2 & 1.0 & 5.1 & 250 & B/M-M/C \\ \hline \hline \end{tabular}  
* CC—Concrete crushing. CF—Concrete brittle fracture. TR—Tensile rupture of the CFRP sheet, SY-Steel yielding. E/C—debending at the epoxy–concrete interface; B/M—debonding at the bar–mortar interface, M/C debonding at the mortar layer–concrete interface.  
\end{table} 
Table 6: General experimental results.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline
**Symbol** & **Definition** & **Symbol** & **Definition** \\ \hline \(\theta\) & The main lobe angle of the directional antenna & \(l_{1}\) & The length of the segment of 1\({}^{\prime}\)s in a specific control sequence \\ \hline \(N\) & The number of sectors & \(l_{2}\) & The length of the UAV’s unique ID \\ \hline \(\theta_{S}\) & The width of the main lobe for the switched beam antenna & \(U\) & The maximum number of UAVs in a FANET \\ \hline \(\theta_{P}\) & The main lobe angle of the phased array antenna & \((x_{i},y_{i})\) & The location of the \(i\)-th UAV \\ \hline \(\varphi\) & The direction of communication & \(v_{i}\) & The speed of the \(i\)-th UAV \\ \hline \(\psi\) & The direction of the main lobe boresight & \(\zeta_{i}\) & The angle between the movement direction of the \(i\)-th UAV and the positive direction of the \(X\) axis \\ \hline \(\mathbf{u}\) & The indexes of sectors & \(t_{i}\) & The time that information is transmitted in by the \(i\)-th UAV \\ \hline \(P_{b}\) & The UAV \(b^{\prime}\) sector that UAV \(a\) is located in & \((x_{\text{T}},y_{\text{T}})\) & The transmitter location \\ \hline \(P_{a}\) & The UAV \(a^{\prime}\) sector that UAV \(b\) is located in & \((x_{\text{R}},y_{\text{R}})\) & The receiver location \\ \hline \(l_{a}\) & The sector that UAV \(a\) points towards in the initial state & \(\text{D}_{\text{T,R}}\) & The distance between the transmitter and receiver \\ \hline \(S\) & The antenna scan sequence & \(\text{D}_{\text{max}}\) & The maximum communication distance \\ \hline \(S_{a}\) & The antenna scan sequence for UAV \(a\) & \(T_{CS}\) & The delay for sensing and judging channels’ availability \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of variable and parameter.
# A Controller Switching Mechanism for Resilient Wireless Sensor-Actuator Networks
## 2 Related Work
Controller switching considering network protocols has also been studied. In wired networks, software-defined networking to dynamically change network configuration for controller switching was studied [34; 35]. In the case of wireless networks, the authors in [36] proposed a distributed two-tier computing architecture comprising local controllers and edge servers that could communicate over wireless networks. Switching was dynamically optimized between local and edge controllers in response to varying network conditions. One controller was locally located, whereas the other was remotely located in order to improve overall performance. However, the proposed approach is different because it aims to ensure stability against controller failure in multihop WSANs.
# Null Subcarrier Index Modulation in OFDM Systems for 6G and Beyond
## 4 Proposed NSC-OFDM-IM
### Receiver Design and Detection
#### 4.1.1 Log-Likelihood Ratio Processing
As an example, let us define the index of the minimum real value in the first LLR subgroup, which is given by,  
\[\tilde{X}(z)=\{\tilde{X}(1),\tilde{X}(2),\ldots,\tilde{X}(\tilde{M})\} \tag{21}\]  
Then, the index, which indicates the minimum LLR value in the first subgroup, can be expressed as,  
\[\tilde{i}=\arg\min_{z}\tilde{X}(z) \tag{22}\]  
The proposed NSC-OFDM-IM index demapper is given in Table 2.  
The index of the minimum LLR value corresponds to the null carrier location in the subgroup, and by using the index \(\tilde{i}\), we easily calculate the decimal value of the corresponding bit sequence. The decimal value of the transmitted bit sequence is expressed as,  
\[\tilde{z}=\tilde{i}-1 \tag{23}\]  
The decimal value \(\tilde{z}\) is converted to binary, resulting in the bit \(\tilde{p}_{0}\) sequence. In order to obtain the complex data from the active subcarriers, we first exclude the null carrier index from the frequency domain subgroup, and then, based on the order of the complex constellation mapper, the complex demapping process takes place to obtain the transmitted bit sequence \(\tilde{p}_{1}\). The number of bits, \(\tilde{b}_{\kappa}\), per each complex group, and the total number of received bits, \(\tilde{m}\), are defined as,  
\[ \begin{split}\tilde{b}_{\kappa}&=\tilde{p}_{0}+ \tilde{p}_{1}\\ \tilde{m}&=\{\tilde{b}_{1},\tilde{b}_{2},\ldots, \tilde{b}_{K}\}\\ &=K(\tilde{p}_{0}+\tilde{p}_{1})\end{split}  \tag{24}\]
## 5 Discussion
Filtering of waveforms and spatial averaging reduces the effect of outliers but especially in the transition from FYI and MYI more research is necessary to quantify those effects on sea ice thickness retrievals. This is of great importance to reduce uncertainties in sea ice thickness estimates. While ambiguous waveforms introduce errors to freeboard estimates, local changes of sea ice characteristics leading to such irregular waveforms contain information about small-scale features of the sea ice surface. Leads of thin ice or open water even smaller than the resolution of the SAR imagery are easily discerned by the altimeter due to their high PP and peak power. Caution needs to be taken to separate real leads and lead-like waveforms that are characterized by lower peak powers [38]. Lead detection is a necessary step in all sea ice freeboard retrievals and crucial to determine an optimal local sea surface height as a reference for the freeboard [39]. FYI within MYI floes most likely originates from refrozen leads or cracks in the ice and even small features are easily detected by waveform changes towards peakier more powerful returns. Freeboard estimates can be biased by off-nadir impact of the smoother reflectors [29; 35]. Small-scale features with an increase in backscatter intensity in the SAR imagery, i.e., large ridges and old ice floes, are characterized by a decrease in PP and peak power in the altimeter waveform sequence. Features a few hundred meters in size can usually be identified but can only be unambiguously assigned to the correct cause by the SAR imagery. Freeboard measurements are, however, not significantly impacted by these small features compared to the surrounding FYI because of the reasons explained above for mixed ice cases. Acquisition of robust freeboard estimates over such features therefore requires this feature on the one hand to be large enough that the altimetric footprint is not affected by surrounding areas and on the other hand to provide enough valid freeboard measurements to obtain a meaningful average value. Based on the analysis of many MYI floes in our data set, we suggest that a floe size in the order of 10 km in along-track and a few kilometers to both sides across-track with a minimum of 10 valid freeboard measurements should act as a guideline for robust estimates of floe freeboard and hence ice thickness. Altimetric waveforms can reflect small-scale changes of the sea ice surface in size scales from a fraction of the altimeter footprint for leads to a few hundred meters for ridges and old ice floes. The complexity of the waveform and the leading edge in particular introduced by non-homogeneous altimeter footprints, however, impede reliable freeboard estimates on these small scales. SAR imagery as additional data aids the interpretation of the altimeter data and facilitates the understanding of waveform responses to surface changes.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 4 Expt. 2: Spectral Reallocation Preferences for Band-Limited Speech
### Results
#### 4.3.2 Intelligibility  
Apart from the CF feature, intelligibility was at or close to ceiling in the quiet condition and, as expected, reduced progressively with noise level. Scores were generally lower in noise than in Expt. 1 due to the restricted speech bandwidth available. To analyse the impact of bandwidth manipulations in Expt. 2, a general linear mixed-effects model with the same effects structure as its counterpart in Expt. 1 was constructed. The CF feature was omitted from this analysis since it is rather different from the other three features in having a fixed bandwidth on an octave scale, and intelligibility is clearly lower for the CF feature at all SNRs. The mixed-effects model indicated significant effects of SNR (\(\chi^{2}(3)=1303,p<0.001\)) and feature (\(\chi^{2}(2)=51.3\), \(p<0.001\)) together with a significant interaction (\(\chi^{2}(6)=94.0\), \(p<0.001\)). Post hoc pairwise comparisons showed that intelligibility was different at all SNRs for all features (max \(p=0.003\)). Intelligibility was identical in the quiet (min \(p=0.11\)) and 0 dB (min \(p=0.42\)) conditions for all 3 features but differed in the \(-3\) and \(-6\) dB conditions (all \(p<0.001\)) apart from in the \(-3\) dB condition, where high-pass filtering and bandwidth modification led to the same intelligibility.