esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 2 Related Work
### MAC Protocols Leveraging Wake-Up Receivers
In this paper, we propose a protocol dedicated to star networks, and unlike most of the previously-cited protocols, this protocol was implemented on sensor nodes and evaluated in the field. Regarding EH-WSNs, [PERSON] et al. [38] compared the energy consumption of the TICERprotocol [37] with and without ULP WuRx. Through simulations, showed that using a ULP WuRx drastically reduces the energy cost of communications. The energy thus saved is used to increase the node throughput. This approach is nonetheless still energetically expensive, as it requires the nodes to send WuBs, which is done at high transmission power and a low bitrate because of the current WuRx sensitivity. [PERSON] et al. proposed a power unit for WSN nodes [21], which features multi-source energy harvesting, multi-storage adaptive recharging, as well as wake-up capabilities using ULP WuRx, which enables the control and configuration of the power unit in an energy-efficient way. In this paper, the proposed ULP WuRx-based communication scheme is fully coordinated by the sink of a star network. The risk of collisions is canceled, and the energy cost of a transmission is therefore reduced and constant. This allows an accurate control of the consumed energy by the EM.
# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 3 Methods
### Predictive Optimization of the Heat Demand  
#### 3.3.1 Minimum Variance Optimization  
The cost function for minimum variance optimization minimized the variance of the heating power while not increasing the total heat demand and keeping the indoor temperature between defined limits as follows:  
\[J_{k}=var\Big{(}P_{optimal,k}\Big{)}+P_{Penalty,k}+\sum_{t=1}^{N}T_{Penalty,k}(t), \tag{3}\]  
where \(P_{optimal,k}\) is the heating power during the optimization period, \(N\) is the number of hours in the optimization period, \(P_{Penalty,k}\) is the penalty for an increase in total heat demand and \(T_{Penalty,k}\) is the penalty for indoor temperature outside the defined limits. The optimization was subject to the following constraints:  
\[\sum_{t=1}^{N}\Big{(}P_{optimal,k}(t)-P_{forecasted,k}(t)\Big{)}\leq 0, \tag{4}\]  
\[P_{min,k}(t)\leq P_{optimized,k}(t)\leq P_{max,k}(t)\text{ \ for each }t=1,\ldots,N, \tag{5}\]  
\[T_{in,min,k}(t)\leq T_{in,optimized,k}(t)\leq T_{in,max,k}(t)\text{ \ for each }t=1,\ldots,N, \tag{6}\]  
where \(P_{optimal,k}(t)\) and \(P_{forecasted,k}(t)\) are the optimized and forecasted heat demands at time \(t\), respectively. \(P_{min,k}(t)\) and \(P_{max,k}(t)\) are minimum and maximum heating powers at time \(t\) and \(T_{in,optimized,k}(t)\), \(T_{in,min,k}(t)\) and \(T_{in,max,k}(t)\) are optimized, minimum and maximum indoor temperatures at time \(t\), respectively.  
The heat demand was optimized for 480 h in each individual building applying the receding horizon or sliding time window approach [15; 32]. In each iteration, the heat demand and the indoor temperature were forecasted for 48 h and the optimization result of the first hour was saved meaning that one-hour control horizon was assumed. Then the 48-h forecasting horizon was advanced one hour and optimized again, saving the result of the first hour, until results for 480 h were achieved. The variance of the heating power was minimized for the 48-h forecast horizon and for all the past results as illustrated in Figure 1, using the _pattern search_ algorithm from the Global Optimization Toolbox of MATLAB(r). In addition, the heat demand was not allowed to increase meaning that the optimized heat demand had to be equal to or smaller than the forecasted heat demand (Equation (4)) and the hourly heating power was constrained between building specific maximum and minimum values (Equation (5))evaluated from the measured data. During the optimization, the indoor temperature was allowed to change between \(-0.5\)\({}^{\circ}\)C and \(+1.0\)\({}^{\circ}\)C from the simulated indoor temperature (Equation (6)) enabling the use of buildings as short-term heat storage.
# Stress-Dilatancy Behavior of Alluvial Sands
## 7 Friction Angle of Sand  
The effective friction angle of sand can be calculated from the following equation [19]:  
\[\sin\phi^{\prime}=t/s^{\prime}, \tag{27}\]  
where  
\[t=\frac{1}{2}\big{(}\sigma_{1}^{\prime}-\sigma_{3}^{\prime}\big{)}, \tag{28}\]  
\[s^{\prime}=\frac{1}{2}\big{(}\sigma_{1}^{\prime}+\sigma_{3}^{\prime}\big{)}. \tag{29}\]  
Equation (27) can have the form  
\[\mathit{sin}\phi^{\prime}=\big{(}\sigma_{1}^{\prime}/\sigma_{3}^{\prime}-1 \big{)}\big{/}\big{(}\sigma_{1}^{\prime}/\sigma_{3}^{\prime}+1\big{)}. \tag{30}\]  
For triaxial compression,  
\[\sigma_{1}^{\prime}=p^{\prime}+\frac{2}{3}q, \tag{31}\]  
\[\sigma_{3}^{\prime}=p^{\prime}-\frac{1}{3}q, \tag{32}\]  
\[\sigma_{1}^{\prime}/\sigma_{3}^{\prime}=(3+2\eta)/(3-\eta), \tag{33}\]  
and  
\[\mathit{sin}\phi^{\prime}=3\eta/(6+\eta). \tag{34}\]  
For the DFS (\(\eta=\eta_{F}\)), the mean value of \(\sin\phi^{\prime}=0.602\) (\(\phi^{\prime}=37.0^{\circ}\)) (Figure 9a), and for the FS, \(\sin\phi^{*}=0.613\) (\(\phi^{*}=37.8^{\circ}\)) (Figure 9b).  
After analyzing data from the drained triaxial compression tests of many sands, [PERSON] [20] proposed the relationships  
\[\phi^{\prime}-\phi_{cs}=3I_{R}, \tag{35}\]  
\[(\delta\varepsilon_{v}/\delta\varepsilon_{1})_{min}=-0.3I_{R}, \tag{36}\]  
where \(0<I_{R}<4\) is a sand parameter. For sands, \(\phi_{cs}=\phi^{o}\)[18]. Combining Equations (35) and (36) gives the following relationship:  
\[\phi^{\prime}-\phi^{o}=-10(\delta\varepsilon_{v}/\delta\varepsilon_{1})_{min}. \tag{37}\]  
The experimental values \(\phi^{\prime}-\phi^{o}\) for the DFS \((\delta\varepsilon_{v}/\delta\varepsilon_{1})_{min}=(\delta\varepsilon_{v}/ \delta\varepsilon_{a})_{F}\) and the experimental values \(\phi^{*}-\phi^{o}\) for the FS \((\delta\varepsilon_{v}/\delta\varepsilon_{1})_{min}=(\delta\varepsilon_{v}/ \delta\varepsilon_{a})_{F*}\) are shown in Figure 10a and Figure 10b, respectively.  
All experimental values of friction angles are larger than those calculated from [PERSON]'s equation (Equation (37)). The maximum difference for the DFS is \(1.2^{\circ}\) (Figure 10a), and it is \(2^{\circ}\) for the FS (Figure 10b). The stress ratio-plastic dilatancy equation (1) obtained from the FSC well describes the relationship \((\phi t-\phi^{o})-(\delta\varepsilon_{v}/\delta\varepsilon_{a})\) for the DFS, and [PERSON]'s experimental relation (37) can be treated as an approximation of this relation. In the authors' opinion, the differences are affected by the transverse anisotropy of sand in the tested sample due to the sample preparation method and small differences between the DFS and FS.  
Figure 10: _Cont_.  
Figure 9: Effective friction angle of sand for (**a**) DFS and (**b**) FS.
# Experimental Study of Accuracy of High-Rate GNSS in Context of Structural Health Monitoring
## 2 Methods and Materials
### Experiments  
#### 2.3.1 Set Up of Shaking Table  
A shaking table test was carried out on the 6 th floor of Block Z, The Hong Kong Polytechnic University. Specifically, as shown in Figure 1a, the shaking table was equipped with a GNSS antenna and a MEMS triaxial accelerometer to record the dynamic displacement response of the shaking table. An LDT (Laser Displacement Transducer, model: SENPUM LM-S15) that can record linear displacement at up to 1,000 Hz with an accuracy of 0.01 mm was used to record the true displacement. Another GNSS receiver was set up in an open environment about 100 m away from the shaking table to serve as the base station for the RTK solution, and the location of the base and rover stations is shown in the Figure A1. A GNSS antenna was connected to the data logger (Figure 1c) to synchronize all the sensors to GPS time.  
At the start of the experiment, the shaking table remained stationary for over 1 h to obtain the initial coordinates of the antenna. Both dynamic and quasi-static motion signals were then loaded to the shaking table and recorded by the GNSS, accelerometer, and LDT. Specifically, the quasi-static motion (amplitude of about 6 cm) was firstly loaded with very low velocity to avoid possible oscillations. Subsequently, following a short stationary time (about one minute), three sets of sinusoidal motions with different amplitudes and peak frequencies were loaded successively, with a 50-s interval between any two sets.
## 3 Modeling of the Microgrid
### Battery Model  
Mathematical modeling and dynamic simulation of battery storage systems can be challenging due to their nonlinear nature. The published literature has presented several thermal models of lithium-ion battery packs [9; 10].  
In [9], a suitable, convenient dynamic battery model is presented that can be used to model a general battery storage system. The proposed dynamic battery model can analyze the effect of temperature, cyclic charging/discharging, and voltage stabilization effects. Temperature makes a difference to three battery parameters in an equivalent electrical circuit battery model. These are the polarization voltage \(K\), the battery constant \(E_{0}\), and the exponential coefficients, \(A\) and \(B\). The dynamic battery model can be described as [9]:  
\[E_{(q,t)}=X_{E0}(T).E_{0}-X_{K}.K\bigg{(}\frac{Q}{Q-q}\bigg{)}+A\ exp^{-X_{B} R_{q}}\ \ldots \tag{10}\]where \(E_{(q,t)}\) = no load voltage (V), \(E_{0}\) = battery constant voltage (V), \(K\) = polarization voltage (V), \(Q\) = battery capacity (Ah), \(A\) and \(B\) = exponential constants, and \(q\) = charge or extracted capacity (Ah).  
The thermal effect on \(A\) has been ignored to make the model simpler, since \(A\) and \(B\) are highly related. For this reason, the mathematical relationships are:  
\[X_{n}=f(T)=A+BT+CT^{2}.. \tag{11}\]  
\[n=f(K,E_{0},B.C) \tag{12}\]
# A Survey on Emerging Blockchain Technology Platforms for Securing the Internet of Things
## 3 Blockchain Platforms
### IOTA Blockchain  
IOTA is a distributed ledger platform that employs a Directed Acyclic Graph (DAG) structure, as opposed to a traditional blockchain. Its intended use is for the Internet of Things (IoT) ecosystem, with an emphasis on scalability, low fees, and secure data transfer between devices [55]. The IOTA platform comprises numerous key features and components, including Tangle Technology, Zero Fees, Decentralised Consensus, MAM (Masked Authenticated Messaging), IOTA Tokens (MIOTA), Smart Contracts (IOTA Smart Contracts--ISCP), Qubic, and Decentralised Identity (DID) [56].  
The technology uses a DAG-based structure called the Tangle, allowing for parallel processing and scalability, while Zero Fees ensures a feeless environment. Decentralised Consensus is achieved through a process called \"Tip Selection\", thus eliminating the need for miners or validators [57]. The MAM protocol enables private communication and data integrity. IOTA Tokens (MIOTA) are utilised for value transfer and data transactions within the Tangle. IOTA is developing smart contract functionality through the IOTA Smart Contracts Protocol (ISCP), which enables developers to create decentralised applications and automate agreements on the IOTA network [58]. The IOTA development caught the attention of some researchers such as [55; 56; 58]. They all focused on identifying the gaps in IOTA to further improve it.  
[PERSON] et al. [38] adopted IOTA with Edge computing (EC) to tackle the obstacles of real-time processing, resource allocation, and storage provision in IoT devices. They conducted an in-depth survey to identify recurring problems with IoT systems and provided a counter solution to it using EC and IOTA blockchain. Additionally, IOTA explores decentralised identity solutions for secure and private identity management. IOTA has partnered with various companies and organisations to explore real-world applications of its technology, including supply chain management, smart cities, energy management, and more.
## 2 Materials and Methods
### Testing Materials  
#### 2.1.1 Soft Clay  
The experimental soft clay was sourced from a subgrade located in the coastal area of Zhuhai, Guangdong Province, China. The sampling site, situated in a coastal delta region, features a complex topography and abundant groundwater resources. Natural samples of soft clay were collected automatically using a medium-sized core drill. Based on basic ASTM standards, physical, strength, and mineralogical tests were conducted to characterize the physical properties of the material, the detailed results of which are provided in Table 1. The X-ray diffraction test, conducted following ASTM D4926-15 (2015) [24], indicated that the claystone consists of non-clay minerals including quartz (33.1%), feldspar (7.5%), chlorite (4.4%), and clay minerals including montmorillonite (12.9%) and kaolinite (42.1%). Following the ASTM standards (D4318-17e1, ASTM D6938-17 ae1) [25, 26], Table 1 details the physical properties of the soft clay, revealing characteristics such as its high natural water content, high compressibility, low load bearing capacity, and low permeability. The soft clay we collected falls within the category of a saturated cohesive soil based on its liquid limit value, transitioning between a soft plastic and fluid plastic state. Figure 2 depicts the particle size distribution of the soft clay, characterized by smaller particles, classifying it
## 5 Experiment and Discussion
### Active Damping Strategy Experiments  
Another experiment was carried out in order to verify the effectiveness of ADS. In this experiment, a slope signal was chosen as the reference signal, as shown in Figure 14. The ADS was disabled during the experiments. The pressure of the metering-out chamber was regulated to 25 bar. The load force was adjusted to 50 kN. When \(t=1\) s, the load force suddenly decreased to zero. The oscillation occurred due to the low pressure at the metering-out chamber. Figure 15 shows the rod displacement. The system started to oscillate from \(t=1\) s and became stable until \(t=1.22\) s, the oscillation occurred for 0.22 s. The maximum overshoot was 15.2 mm. Figure 16 shows the pressure of both the metering-in side and the metering-out side.  
The comparative experiment was carried out and the ADS was enabled. The load force was adjusted to 50 kN. The load force suddenly decreased to zero when \(t=1\) s. The system started to oscillate from \(t=1\) s and it became stable until \(t=1.13\) s. The oscillation occurred for 0.13 s. The maximum overshoot was 13.6 mm. Figure 17 shows the pressure of both the metering-in side and the metering-out side. The ADS played a vital role to raise the metering-out reference signal when the rod displacement began to oscillate. The oscillation time was reduced by 41% and the maximum overshoot was reduced by 10.5% when compared with the results of the experiments.  
Figure 14: Position tracking under external force disturbance (without ADS).  
Figure 12: The estimate of \(\theta_{l2}\).
###### Abstract  
The objective of this work is to analyze the behavior of short temporal baseline interferometric coherence in forested areas for L-band spaceborne SAR data. Hence, an exploratory assessment of the impacts of temporal and spatial baselines on coherence, with emphasis on how these effects vary between SAOCOM-1 L-band and Sentinel-1 C-band data is presented. The interferometric coherence is analyzed according to different imaging parameters. In the case of SAOCOM-1, the impacts of the variation of the incidence angle and the ascending and descending orbits over forested areas are also assessed. Finally, short-term 8-day interferometric coherence maps derived from SAOCOM-1 are especially addressed, since this is the first L-band spaceborne mission that allows us to acquire SAR images with such a short temporal span. The analysis is reported over two forest-production areas in Argentina, one of which is part of the most important region in terms of forest plantations at the national level. In the case of SAOCOM, interferometric configurations are characterized by a lack of control on the spatial baseline, so a zero-baseline orbital tube cannot be guaranteed. Nevertheless, this spatial baseline variability is crucial to exploit volume decorrelation for forest monitoring. The results from this exploratory analysis demonstrates that SAOCOM-1 short temporal baseline interferograms, 8 to 16 days, must be considered in order to mitigate temporal decorrelation effects and to be able to experiment with different spatial baseline configurations, in order to allow appropriate forest monitoring.  
SAOCOM-1; Sentinel-1; InSAR coherence; forest plantations 2022
# A Sub-50 nW Implantable Body Temperature Sensor and Its Application to a Rat
## 4 In Vivo Application
### In Vivo Measurement Result
[PERSON] presented a system-on-chip (SoC) for three-lead ECG, respiration rate, and body temperature measurement [22]. A 600 k\(\Omega\) thermistor at 37 \({}^{\circ}\)C was used for body temperature measurement. A direct temperature to digital converter was developed using delta sigma modulation architecture, consuming 1.8 \(\upmu\)W at 0.9 V. It is a static transducer-driving method that continuously supplies current to the transducer. [PERSON] developed a temperature sensor for biomedical applications [23]. The sensor uses a 600 k\(\Omega\) on-chip resistor whose resistance varies with temperature. It has a measurement circuit architecture based on a sigma-delta (SD) modulator, and power consumption can be reduced to 700 nW by utilizing a power-down mode. [PERSON] developed a temperature measurement circuit for the biosensor interface [24]. A Wheatstone bridge was constructed with a 100 k\(\Omega\) thermistor and an equivalent resistance of a switched capacitor. The active low-pass filtered voltage of the Wheatstone bridge is used to control the output clock frequency of the VCO, and the output clock of the voltage-controlled oscillator (VCO) is used to drive a switched capacitor type resistor. The power consumption was 9.2 \(\upmu\)W due to the static driving of the Wheatstone bridge and the high-frequency VCO driving. [PERSON] developed a SoC capable of measuring photoplethysmography (PPG) and body temperature [25]. A Wheatstone bridge was constructed using a 66 k\(\Omega\) on-chip resistor as a temperature transducer, and an SD-ADC was used to measure the Wheatstone bridge output voltage. The static transducer driving method and the conventional SD-ADC were used, resulting in high power consumption of 35 \(\upmu\)W. Looking at the trends of previous studies, it can be seen that dynamic operation rather than static operation is advantageous for implementing low power in temperature transducers.
## 5 Experimental Analysis and Discussion
### Comparison with State of Art Models  
The paper compares the performance of three alternative routing protocols, DSDV, AODV, and AOMDV, on two different mobility models, the random waypoint mobility model (RWM) and random direction mobility model (RDM), with a static sink node. During this simulation-based investigation, key factors affecting the experiment included the simulation environment, metrics, and parameters. The values of these factors have been acclimatized according to the requirement of the experiment; these values and factors are  
Figure 5: Performance analysis of RWM and RDM on various routing protocols in terms of average end-to-end delay.  
Figure 6: Performance analysis of RWM and RDM on various routing protocols in terms of throughput.  
not imperative to be provided. During experimentation, all three routing protocols have been evaluated by varying numbers of nodes with different mobility models. The results of the experiment lead us to conclude that AOMDV outperforms other routing protocols at a higher number of nodes, as explained in the conclusion and future work section.
## 2 Materials and Methods  
### Orbit Selection  
The main tradeoff that drives the mission concept is between spatial resolution and mission lifespan: the limited size of the CubeSat does not allow for a large optical system; thus, in order to achieve a good spatial resolution, a low orbit is recommended. The ideal orbit is therefore an inclined orbit at \(\sim\)500 km height and 55\({}^{\circ}\) inclination so as to cover most areas of interest (urban regions) and for orbit stability purposes [13]. A common choice for Earth Observation (EO) missions with optical sensors is a Sun-Synchronous Orbit (SSO) with a determined Local Time of Ascending Node (LTAN)  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Attitude Requirements** & & \\ \hline Pointing knowledge at nadir & 120 & arcsec \\ Pointing stability & 20 & arcsec/s \\ \hline
**Main Exploitation Requirements** & & \\ \hline Digitalization & 12 & bits \\ Data storage on board & \textgreater{}1 & Gb \\ GSD at nadir pointing & \textless{}30 & m \\ Swath at nadir pointing & \textgreater{}30 & km \\ \hline
**Main Radiometric Budget** & **SNR (dB)** & **MTF (dp/mm)** \\ \hline Blue band (440–510 nm) & 35 & 25 \\ Green band (520–590 nm) & 35 & 25 \\ Red band (620–680 nm) & 35 & 25 \\ Red edge band (690–730 nm) & 35 & 20 \\ NIR band (850–890 nm) & 30 & 20 \\ Extra band (if available) & 30 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design requirements provided by the Institut Cartografic i Geologic de Catalunya (ICGC).  
designed to acquire images of the ROI with a constant illumination. However, the proposed orbit is not an SSO since the nature of the mission is strongly focused in the region of Catalonia, Spain, and the chosen orbit reduces the revisit time. In practice, the final orbit will be the best available one, taking into account that CubeSats are launched as a piggyback on a larger primary satellite that is the one that drives the orbit selection.
# Iteratively Refined Multi-Channel Speech Separation
## 3 Experimental
### Evaluation Metrics  
The SDR of blind source-separation evaluation (BSS-Eval) [25] and scale-invariant signal-to-distortion ratio (SI-SNR) were chosen as the main objective measures of separation accuracy. Furthermore, perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and SIR were used to further evaluate the accuracy of the separated speech. It is worth noting that during the evaluation process, the first microphone was selected as the reference by default.