esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Applications of Microbial Fuel Cells in Sensors and Biosensors
## 3 MFC as a Self-Powered Biosensor versus a Traditional Whole-Cell Biosensor  
The high sensitivity of the MFC's performance to a number of environmental and biological factors make them suitable to act, in turn, as biosensors [107; 108; 21]. Therefore, properly combining energy generation and sensing functionalities from MFCs, self-powered devices, i.e., operated without any external electricity supply, can be designed to operate autonomously for remote detection and quantification of desired parameters [109; 110]. Indeed, several reviews have already been reported regarding those unique and novel applications posed for MFCs as electrical biosensors [54; 111].  
At this point, it should be mentioned that the IUPAC definition for \"electrical biosensor\" is a self-contained integrated device providing specific analytical information using a biological recognition element contacting with an electrochemical transduction element (anode/cathode electrodes in MFCs) [112; 113]. Biological components for the analyte recognition in conventional biosensors are mostly enzymes, fluorescence proteins, and other fluorescence/pigment molecules [114; 115; 116]. In a conventional biosensor, an external power source is required to operate all electronic components, including the transducer and others, responsible for the signal conversion, amplification, and transmission, which noticeably limits applications in remote environmental monitoring. In contrast, the biological sensing element in the case of an MFC are the microbes, which give a measurable response as an output electrical current through the anode/cathode electrodes from the MFC itself [117; 48; 118]. Since the analytical output signal is already measured as an electric current, MFC-based biosensors do not need an external transducer. Therefore, MFC-based biosensors are considered to be recyclable and sustainable devices with higher cost- and energy-efficiency than conventional biosensors [111].  
According to what has been discussed so far, monitoring microbial activity and related parameters, such as biochemical oxygen demand (BOD) or detection of inhibitors and toxicants, appear as the most obvious applications for MFC-based biosensors. Especially promising are their applications for rapid monitoring of environmental factors such as pH, temperature, composition, and concentration  
Figure 5: Electron conversion efficiency and energy generation from fermented wastewater under 1 k\(\Omega\) of the external resistance [100].  
of organic matter, and other parameters related to the quality of water effluents (Table 1), for which MFC-based biosensors have been considered as the next generation of biosensing technology [30].
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 4 The K3P Algorithm
### Tripod in Support Phase  
Given the desired position of the center of mass \({}_{B}\mathbf{X}_{k+1}\) (line 11 of Algorithm 1) and the current tripod supporting the robot \(\frac{B}{f}\mathbf{A}_{k}\) (lines 1 and 3 of Algorithm 1), K3P defines the new configuration for the support tripod (line 13 of Algorithm 1) constrained by  
\[\overset{W}{f}\mathbf{A}_{k}=\overset{W}{f}\mathbf{A}_{k+1}. \tag{1}\]  
Such a constrain implies that all limbs corresponding to the support tripod remain fixed to the ground with respect to \(W\). In consequence, the gait generated is slip-free and none of the limbs loses contact with the ground. We can solve for \(\overset{B}{f}\mathbf{A}_{k+1}\):  
which the K3P algorithm can guarantee a horizontal travel distance of 70% of maximum limb extension. Therefore, K3P considers \(l_{max}\) to be equal to the 70% of the theoretical maximum extension of the legged robot (see Figure 3a), with a walking height within the shaded range in Figure 3c.  
The second parameter that determines the location of frame \(L\) is based on the instantaneous turning radius \(\rho\) of the CoM \(B\). Such a turning radius is given by the ratio between the current velocity \(\dot{\mathbf{r}}=\parallel\dot{x},\dot{y},\dot{z}\parallel\) and the angular velocity around the \(Z_{B}\) axis \([\dot{\mathbf{q}}]_{\psi}\) (line 12 of Algorithm 1). If \(\rho\) is above a given threshold \(l_{\rho}\), then \(L\) is located straight ahead \(\frac{1}{2}l_{max}\) meters from \(B\) in the direction of \(\dot{\mathbf{r}}\) (see Figure 4a); otherwise, it is located over the instantaneous circular trajectory such that the arc length from \(B\) to \(L\) is equal to \(\frac{1}{2}l_{max}\) (see Figure 4b). Determining the most suitable position for \(L\) based on the magnitude of the instantaneous turning radius \(\rho\) is how the algorithm differentiates from straight and turning maneuvers.  
The third and last parameter \(l_{g}\) represents how high the tripod will be raised during the swing phase. This parameter is expressed as a percentage of clearance \(l_{h}\) and it can be changed dynamically, depending of the roughness of the terrain.  
The desired position for the tripod in the swing phase is computed in line 13 of Algorithm 1. Similar to Section 4.2, an error metric \(\mathbf{c}_{L,k}\) is fed to a PD controller to generate a control command \(\mathbf{u}_{L}\) over the subset legs in the swing phase, lines 14 and 15 of Algorithm 1, respectively. Then, the position of the tripod performing the swing phase at time instant \(k+1\) can be computed (line 16 of Algorithm 1).
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
## 5 Conclusions  
The main aim of this paper was the identification of key applications and technical modes and settings for a high-resolution X-band system like TerraSAR-X. A further goal was to determine how the mission is utilized by the scientific user community. Generally, data are requested from all fields of geo-research with a largely homogeneous distribution of the corresponding number of proposals over the different spheres. The general applicability and appreciation of SAR data was confirmed. However, the evaluation of surface displacements is clearly revealed as the key application of a high-resolution SAR system. Mostly, SAR interferometry is applied, but also feature tracking techniques are of increasing importance. Future high-resolution SAR systems should support and strengthen this application, e.g., by maintaining and improving the orbit precision and interferometric baseline stability.  
The three-meter spatial resolution of the StripMap mode is well-suited for systematic monitoring on a regional scale while meter and submeter resolution of the Spotlight mode enables detailed but spatially limited analyses. A future mission would benefit from an extended across track coverage of the three meter StripMap mode. Spatially higher resolution of less than one meter would improve local studies and the evaluation of surface displacements.  
Scientific users make use of polarimetric SAR capabilities, although these are not a main feature of TerraSAR-X. The demand has been growing over the recent years and a future system would benefit from the availability of fully polarized SAR data.  
All geoscientific fields request systematic monitoring and the creation of time series for the investigation of seasonal, interannual, and even decadal variations. The use of single scenes for analyzing local and singular events is a minor issue.  
**Author Contributions:** [PERSON] as the TerraSAR-X Science Coordinator reviewed all TerraSAR-X proposals and wrote the paper. [PERSON] performed the keyword analysis of the TerraSAR-X scientific proposals. [PERSON] carried out the literature review and the corresponding keyword analysis. [PERSON] evaluated the TerraSAR-X archive and prepared the scientific acquisitions statistics. [PERSON] prepared the geographic distribution of scientific acquisitions. [PERSON] performed the English editing. [PERSON] and [PERSON] strengthened the paper through critical discussion and review. All authors contributed to the paper by discussions and revisions of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Self-Learning Power Control in Wireless Sensor Networks
## 6 Experiments
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quantization Level** & _cca_ & _retr_ & _lat_ \\ \hline
0 & 1 & 0 & NA \\
1 & 2 & 1 & low \\
2 & 3 & 2 & medium \\
3 & 4 & 3 & high \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantization levels for CCA attempts, retransmissions and latency. never reaches 0. Exploring gives the possibility to choose power levels that result in low PRR and low Q-values. When \(e\) drops below 0.1 (i.e., after 550 min), PRR remains above 87%. As a last note, PRR and Q-value drop in Figure 16 after 200 min, which is explained by the fact that someone entered in the lab. The environment changed and its representation in the system moved into a new state that was not explored previously, causing the drop and a new learning curve. Differently from Section 5, the convergence is longer because the inter-arrival time of packet generation is 3 s, thus the PRR is calculated around every 30 s. We also analyse the energy consumption of the transmitter node and compared it to the homogeneous case, when the maximum transmission power is set. The values of the parameters for the energy calculation are taken from the datasheet of the transceiver CC2420 [60]. The nodes can be in four different operational modes: sleep, idle, transmission and reception. In the sleep mode, the power supply is turned off. The idle mode in TelosB is equivalent to the power down mode in CC2420 where the crystal oscillator, FIFO buffer and RAM access are disabled. During the transmission and reception mode, the node transmits and receives, respectively. The nodes are in sleep mode for 61.6% of the time. The transmitter sends the data every 3 s, with a payload of 8 bytes added to 6 bytes of header. The time in the transmitting mode, \(t_{tx}\), is calculated by the number of packets that are transmitted and retransmitted during the entire time of the experiments (i.e., 750 min), at the data rate of 250 kbit/s. Similarly, the time in reception mode, \(t_{rx}\), is calculated by the number of ACKs (i.e., 11 bytes long) that are received. The remaining time is spent idle. Therefore, the total energy spent is equal to: \(E=V\cdot(i_{tx}\cdot t_{tx}+i_{rx}\cdot t_{rx}+i_{idle}\cdot t_{idle}+i_{sleep }\cdot t_{sleep})\). The current draw per mode is given in Table 5. Specifically, the current draw in the transmission mode, \(i_{tx}\), is provided in the CC2420 datasheet for some transmission power levels, while the other values are evaluated through a quadratic fitting curve. In Table 5, the values of the current in the transmission mode are ordered accordingly to the list of power levels. The total energy consumption is equal to 1.78 Joule. Considering that TelosB nodes are supplied by 2 AA alkaline batteries, the capacity is twice 3000 mAh. Thus, the energy consumed by the transmitter over 750 min is 0.0027% of the available supply. In comparison, if we assume that the same amount of transmissions would have been executed by the highest transmission power level (i.e., 0 dBm), the energy consumption is 1.88 Joule, equal to 0.0029% of the energy supply.
# Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
###### Abstract
Finally, the RANSAC algorithm [12] is used to account for outliers, and the Umeyama algorithm [13] is employed to solve the rigid transformation, including translation, rotation, and the object's size. We refine the deformation network by using a multi-stage network structure, explicitly incorporating the deformed point cloud into the dense correspondence matrix regression network, enhancing the network's robustness. Additionally, we propose a new attention structure that focuses the network's attention on the differences between point cloud features, addressing the intra-class variation problem:  
* We propose a refined prior-guided category-based 6D pose estimation framework. Based on RGB and depth images, this framework effectively handles occlusion and lighting variations and can estimate the pose of unseen objects within the same category. * We refine high-dimensional feature information through a multi-stage structure and refine the deformation results of the point cloud. By explicitly incorporating the transformation of the point cloud, the learned features gain stronger semantic information. * We introduce a novel attention mechanism, enabling the network to allocate more focus on the differences between point clouds, thereby addressing the intra-class variation problem. * Extensive experiments on the CAMERA25 and REAL275 datasets demonstrate that our method outperforms existing approaches and is capable of estimating the 6D pose of objects under partial occlusion and varying lighting conditions. Furthermore, the effectiveness of our algorithm is also tested and validated in vision-based robotic arm-grasping experiments.
# On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network
## 3 Motivation
### Description of the Scenario and Tests of Communication Based on Mobile Network
In order to connect to the plane, the following steps had to be done: (1) Run the Birdie system and check the status of the multicolored diode on the modem (recommended green for LTE connection, allowed blue for 3G connection, red for no cellular network coverage); (2) connect the computer to the Internet (e.g., through a pre-set 3G/4G hotspot), and (3) after about 2 min from the powering of Birdie system, activate the connection from the desktop, open the Mission Manager application and select the connection option on port 14,550 from the Connection menu. The test results showed that in an urban environment, the average of the delay was around 200 ms, with possible delays of up to 500-800 ms. Values of delay higher than 1 s were already classified as temporary loss of communication. In case of temporary loss of communication, the Birdie system was equipped with \"Fail-Safe\" functionality, where the user defines (during the preparation the plane before flight) the maximum allowed time without communication and activates \"GCS Failsafe\" (by selecting RTL option). Figure 1 shows the delay results for the testing drone. These preliminary tests showed that latency is one of the main factors impacting the quality of the connection and poor quality connection risks the manageability of the UAVs. The throughput of the connection is, under normal conditions, low: MAVlink connection requires about 3 Kbps of capacity, however cyclic transmission of a flight plan may require up to 15-20 Kbps. The last important parameter impacting the communication with UAV is the reliability: Disconnecting and re-establishing connection through the mobile network on the fly involves downloading the full set of about 900 autopilot parameters. Therefore,possibility of additional and monitored routing is necessary for providing secure and reliable communication between the control station and the UAV.
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 4 Security Analysis
### Informal Security Analysis
#### 4.1.4 Resisting User Masquerading Attacks  
In the proposed protocol, legitimate user \(\mathcal{U}_{k}\) sends the login request message to \(\mathcal{FN}_{f}\), and \(\mathcal{FN}_{f}\) relays the message \(\{M_{1},M_{2},TID_{k},TID_{f},auth_{k},auth_{f}\}\) towards \(\mathcal{CSP}\). Suppose an adversary intercepts the login request message \(\{M_{1},TID_{k},auth_{k}\}\) initiated by \(\mathcal{U}_{k}\) and tries to impersonate the legal user \(\mathcal{U}_{k}\). In our proposed protocol, even after breaching the login request message \(\{M_{1},TID_{k},auth_{k}\}\), \(\mathcal{A}_{att}\) cannot compute a valid \(auth_{k}\), as the computation of an \(auth_{k}\) requires the \(ID_{k}\) of \(\mathcal{U}_{k}\) which is unknown to \(\mathcal{A}_{att}\). So, \(\mathcal{A}_{att}\) cannot masquerade as the legitimate user.
## 2 Materials and Methods
### Atmospheric Delay Model  
The atmospheric delay model is typically expressed as the product of the zenith atmospheric delay and the mapping function [54].  
\[\Delta R_{atm}=m(\varepsilon)\int_{z}^{\infty}{[n(z)-1]dz} \tag{10}\]  
where \(m(\varepsilon)\) is the mapping function, \(\varepsilon\) is the incidence angle, \(n(z)\) is the refractive index in the zenith direction, and \(\int_{z}^{\infty}{[n(z)-1]dz}\) is the atmospheric zenith delay. Atmospheric delay is the sum of ionospheric delay and neutral atmospheric delay.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 4 Experimental Evaluation
### Datasets
#### 4.1.2 MOTIF Datasets  
We created two datasets, namely MOTIF 1 and MOTIF 2 (MOTIF is the title of the project which funded this work). Both datasets were acquired by means of FMS-500 Light+ telematics device, manufactured by Xirgo technologies (former BCE) [27]. The device is equipped with accelerometers, a GPS receiver of 1 m resolution, and a GSM modem that works up to 4G cellular protocol. The raw accelerometer data were merged with the corresponding GPS coordinates to create vehicle routes. Three types of driving behaviour have been considered: normal, semi-aggressive, and aggressive. The MOTIF 1 dataset comprises of 11 route samples, whereas the MOTIF 2 comprises of 12 route samples. Six drivers have been involved (Table 3) in a range of six months, driving the same vehicles, following the same route but in different time periods. Each route sample has approximately a duration of 15 min, whereas the measurement vectors were acquired at 0.1 Hz and comprise 27 features: maximum positive acceleration, maximum negative acceleration, maximum transverse acceleration, a 21-bin histogram of acceleration values ranging from \(-0.5\) g to 0.5 g, latitude, longitude and speed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Driver** & **Genre** & **Age Range** & **Model** & **Fuel** \\ \hline D1 & Male & 40–50 & Audi Q5 & Diesel \\ D2 & Male & 20–30 & Mercedes B180 & Diesel \\ D3 & Male & 20–30 & Citrogen C4 & Diesel \\ D4 & Female & 30–40 & Kia Picanto & Gasoline \\ D5 & Male & 30–40 & Opel Astra & Gasoline \\ D6 & Male & 40–50 & Citrogen C-Zero & Electricity \\ \hline \hline \end{tabular}
\end{table}
Table 2: Drivers and vehicles in the UAH dataset [12].
# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 5 Result Evaluation and Discussion
### Linear Network of a Width of Two  
In the second trial, we evaluated the network performance at D by considering both cases as shown in Figures 6 and 7. To evaluate the performance of the network as shown in Figure 6, we sent 500 packets to D in the presence of all active relay nodes (R-1, R-2, R-3...R-6). We calculated the PRR at D with respect to all active relay nodes.  
From the experimental results, we can say that the net PRR at D was approximately 84.21%, as shown in Figure 11. To evaluate the network performance, as depicted in Figure 7, we transmitted 500 packets from S to D in the presence of all active relay nodes, considering the physical offset between relay nodes. The results showed that the PRR via all active relay nodes was about 98.15%, as shown in Figure 11. These results demonstrated the non-destructive characteristic of LoRa transmission. When the nodes at the station were colocated, this resulted in a high probability that both of these nodes would receive a message simultaneously and choose to retransmit it simultaneously, increasing the probability of collision at the next receiving station. Instead, when the nodes had a physical offset, the offset in the reception time was replicated in the subsequent retransmission time. This offset helped the next receiving station lock onto the first transmitter and ignore the collision.  
Figure 10: Overall performance using a single relay node at each hop.
## 2 Materials and Methods
### Estimation of Phenolic Content and Antioxidant Potentials
#### 2.4.1 Total Phenolic Content (TPC)  
The Folin-Ciocalteu method used for determining the total phenolic content in coffee beans was based on [PERSON], [PERSON], [PERSON], [PERSON], Sharifi-Rad, and Suleria [12] with some modifications. Briefly, 25 \(\upmu\)L of sample extract was first added to the 96-well plate and mixed with 25 \(\upmu\)L Milli-Q water-diluted Folin reagent and 200 \(\upmu\)L of Milli-Q water. This mixture was then subjected to a 5 min dark incubation at ambient temperature. Afterincubation, 25 \(\mu\)L of 10% (_w/w_) sodium carbonate solution was added, and the plate was further incubated for 1 h under the same dark conditions. Meanwhile, gallic acid in ethanol solution ranging from 0 to 200 mg/mL was prepared and used to generate calibration curve. To eliminate the sample's color interference on absorbance, a mixture of sample and water was used as blank. After incubation, the absorbance was measured at 765 nm using spectrophotometer (Multiskan(r) Go microplate photometer), and results were expressed as mg of gallic acid equivalents (GAE) per gram of coffee bean powder on a dry weight basis (mg GAE/g) \(\pm\) standard deviation (SD).
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 4 Experiments and Discussions
### Experimental Results
#### 4.4.2 Time Consumption  
The advantage of incremental learning is that it is able to significantly reduce the time consumption required for model training. The purpose of using incremental learning for SAR ATR is to occupy smaller memory space, obtain faster training speed, and consume less time. Non-incremental learning (one-time supervised learning or batch learning) means that the model training phase uses all data from each target class, which is the common joint training approach. The experiment is run under windows environment, the computer cpu is intel core i5-8500, the gpu is NVIDIA GeForce RTX 2070, the RAM is 16 g. We first start training on data containing 5 target classes, and then joint training obtains all data from each class each time. Incremental training adds 1 new class of data at a time, while only representative samples of the old data from the past are preserved. As shown in Figure 5, during the total of 6 phases, the incremental learning approach significantly reduces the time consumption. The time required for incremental training remains essentially in the same dimension as the new target arrives. However, the time consumption of non-incremental training continues to increase and exceeds the incremental methods. This shows that the incremental learning method proposed in this paper is able to obtain a better recognition rate and less time consumption.