esa-sceva/satcom-chunk-collection huggingface dataset:
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 4 Consecutive Error Prevention Performance: Evaluation
### Experiment Assumptions  
The open-source simulator LTEV2 Vsim [18] is used for the simulations, which implements an IBE model. Winner+ B1 is used as the channel model [22] and a 10 MHz band allocated for the sidelink channel is assumed. A 300-byte for the basic safety message (BSM) size is assumed, including the security credential as recommended for evaluation experiments by [PERSON] et al. [23]. Given the 2400-bit transport block size (TBS) that accommodates the 300-byte BSM in addition to the SCI overhead, the TBS index of \(I_{TBS}=7\) is used, which can carry 2472 bits with the coding rate of 0.55. With \(I_{TBS}=7\), the number of required physical resource blocks (PRBs) is 20 for the payload. Since 2 more PRBs are necessary for the SCI, a single BSM needs 22 PRBs. Since there are 50 PRBs, two subchannels can be allocated in a 10 MHz channel, so \(N_{subCH}^{SL}=2\). Therefore, the MCS level is set to 7. The SCI and TB of a single message are assumed to be adjacent, but the alternative non-adjacent configuration does not affect the proposed scheme. Vehicles transmit the BSM once every 100 ms, and the resource keep probability \(P_{k}\) used in the resource reselection process is set to 0.8 to give SPS the maximum resource use predictability. Vehicles are deployed according to the 1-D Poisson point process along a straight highway road of 3 km. Vehicle traffic density \(\rho\) is varied across 100, 150, 200, and 250 vehicles per km. These traffic densities were selected to load the wireless channel in the approximately 50-80% range as shown in Table 1 (center column), which the J2945/1 congestion control algorithm  
Figure 7: SCI Format 1 in cellular V2X: **(a)** in NR V2X [20]; **(b)** in LTE V2X [6].  
considers desirable as the channel load [24]. Note that adding a single blind retransmission per each transmission does not double the CBR due to increased packet collisions.  
The Tx power is set to 23 dBm, and the thresholds for SINR and RSSI conditions in the inequalities (1) and (3) used for feedback condition determination are 7.3 dB and \(-\)85.2 dBm, respectively. In particular, the SINR threshold is for the MCS level 7 [17]. Under these conditions, the rate \(\alpha\) of power leakage to other channels is 0.35%, which suffices to compute the IBE at \(\mathrm{N_{subCH}}=2\). Note that for a fair comparison, the BSM size is set at 300 bytes for all schemes whereas [PERSON] et al. [9] set it to 190 bytes. Additionally, the initial RSRP threshold is set to \(-\)110 dBm, used for the LTE/NR V2X SPS resource selection procedure as per the LTE-V2X and NR V2X standards [2,11] whereas [9] set it to \(-\)120 dBm. For the SPS resource reselection algorithm, the standard values for Sidelink Mode 4, as specified in TS 36.213 [11], is used. Table 2 summarizes the simulation settings.
## 2 Methods  
### The Theory of Probability Inversion  
Bayesian inversion is based on the Bayesian theorem, which is a statistical method used to update probability estimates based on prior knowledge. In the application of ground-penetrating radar, Bayesian inversion can help to more accurately estimate the characteristics of underground structures, such as soil moisture content, rock type, or other underground features. The Bayesian formula is as follows:  
\[P(H|D)=\frac{P(D|H)\times P(H)}{P(D)} \tag{1}\]  
Here, \(P(H|D)\) is the posterior probability of hypothesis \(H\) given data \(D\), \(P(D|H)\) is the likelihood of data \(D\) given hypothesis \(H\), \(P(H)\) is the prior probability of hypothesis \(H\), and \(P(D)\) is the marginal probability of the data.  
To solve geophysical inversion problems, considering a probabilistic inversion approach, the respective reverse equation is as follows [24]:  
\[\sigma_{M}(\mathbf{m})=k\rho_{M}(\mathbf{m})L(\mathbf{m}) \tag{2}\]  
Here, \(\sigma_{M}(\mathbf{m})\) represents a posteriori probability distribution, which is the solution of the inversion; \(\rho_{M}(\mathbf{m})\) represents the prior probability density, denoting the pre-existing knowledge about model attributes; \(L(\mathbf{m})\) stands for the likelihood function, which offers a metric illustrating the extent to which the simulated data align with the observed data; and \(k\) represents a normalization factor.  
The key in this method is to build an appropriate probability model that correlates GPR data with subsurface characteristics. In practical applications, this often involves complex numerical simulations and statistical analysis. For instance, model parameters can be adjusted by comparing actual observational data with model predictions to improve accuracy.
## 4 Discussion
### The Future Development Prospect of DA
#### 4.2.1 Intelligent and Integrated DA
Future developments should account for interactions and dependencies among modules, incorporating physical modeling with ML methods like ML-4D-Var, environmental stress indices, and crop variety traits, leveraging ML's capacity for nonlinear modeling to improve adaptability to varied conditions and crop types [297; 298]. While complex CGMs offer accuracy and performance advantages, their computational complexity and demands can hinder DA research. Thus, approximated models oremulators are proposed to simplify computational requirements while preserving model accuracy. Emulators simulate key growth parameters like LAI, biomass, and transpiration rates, combining these with observations and utilizing variational approximation or Gaussian process estimation for model parameterization, leading to more accurate crop growth status predictions and yield estimates [299; 300]. Such approaches propel DA towards greater intelligence and service-oriented applications.
## 3 Results
### In Vitro Antioxidant Activity  
In the present study, six different methods (DPPH, ABTS, CUPAC, FRAP, metal chelating, and phosphomolybdenum assays) were used to evaluate the antioxidant activity of the _R. angustifolius_ extracts. The methanolic extracts were noted to exhibit the highest antioxidant capacity, considering different methods of extraction. Indeed, the methanolic extracts have been noted to have the highest DPPH- (45.57-47.20 mg Trolox equivalent (TE)/g) and ABTS (94.68-104.11 mg TE/g)-scavenging properties (Table 1). Regarding other extracts, they were found to have a relatively moderate or lower scavenging ability, ranging from 10.29 to 39.96 mg TE/g and 12.71 to 69.69 mg TE/g in DPPH and ABTS assays, respectively. In particular, the HAE-EA extract demonstrated the lowest radical-scavenging potential in both assays (Table 1). Moreover, the extracts also showed notable reducing power in CUPAC and FRAP assays. Similarly, the methanolic extracts of _R. angustifolius_ were found to exhibit the most significant reducing activity in the CUPRAC assay (144.67-173.03 mg TE/g) and FRAP assay (91.35-103.09 mg TE/g), followed by ethanolic extracts ranging from 130.52 to 140.72 mg TE/g and 67.12 to 79.45 mg TE/g, respectively.  
Figure 3: Cumulative graphs of _R. angustifolius_ bioactive compounds classes. (**A–C**) reported the main classes of bioactive compounds, whereas (**D–F**) reported the main classes of polyphenols in _R. angustifolius_ extracts. The letters correspond to: (**A,D**) = homogenizer-assisted extraction (HAE), (**B,E**) = maceration (MAC), and (**C,F**) = Soxhet (SOX) methods of extraction.  
Concerning the metal-chelating activity, the water extracts obtained by MAC, HEA, and infusion showed the highest metal-chelating activity values ranging from 25.66 to 27.51 mg EDTA equivalent/g, especially compared to the ethanolic extracts (4.57-8.90 mg EDTAE/g) (Table 1). Nevertheless, the total antioxidant capacity assessed by the phosphomolybdenum assay highlighted that water extracts, obtained by HAE, MAC, and infusion methods, have the highest antioxidant activity (2.83-3.92 mmolTE/g), while the activity of the other extracts ranged from 1.55 to 2.98 mmol TE/g (Table 1).
## 2 Materials and Methods
### Double 3 Robot  
Double 3, developed by Double Robotics (Redwood City, CA, USA), is a telepresence robot that allows remote control by an operator via a standard computer. The operator can view the robot's environment through a camera mounted on the robot, while their face is displayed on the robot's screen (see Figure 2). Double 3 is equipped with an audio system that enables the operator to hear and speak through the robot's loudspeakers, facilitating two-way communication. Its two-wheeled, self-balancing design contributes to stability  
Figure 1: A Pepper robot interacting with one of the participants in the test.  
and ease of navigation. The robot's 3D sensors support navigation and obstacle avoidance, allowing the operator to perceive a physical presence in various settings such as offices, educational institutions, and healthcare facilities [19].  
The Double 3 has been tested in several different contexts. In education, it has been utilized to facilitate remote classroom participation, enabling students who cannot be physically present for learning activities. Research by [PERSON] and [PERSON] [15] indicates that Double 3 can enhance the learning experience by providing accessible educational opportunities, though its effectiveness may vary based on classroom dynamics and student needs.  
In corporate settings, Double 3 has been applied for remote work and teleconferencing. Studies by [PERSON] et al. [16] highlight how it may improve communication and collaboration within distributed teams, although its impact varies with the workplace contexts and the team dynamics.  
In healthcare, Double 3 has been used for remote patient consultations. As noted by [PERSON] [26], this application represents a novel approach to telemedicine, improving access to medical consultations for patients in remote or underserved areas. However, the appropriateness and efficacy of such applications depend on the specific medical context.  
Overall, existing research presents Double 3 as a versatile tool for enhancing remote interaction in several sectors. However, the degree to which the robot can replace physical presence remains an area for further exploration.
# Effect of Temperature, pH, and Reaction Duration on Microbially Induced Calcite Precipitation
## Materials and Methods
### Experimental Procedures  
Figure 1 shows the experimental process to measure the amount of calcite precipitation. The urea-CaCl\({}_{2}\) medium was prepared using 500 mL of distilled water, 1.5 g nutrient broth (Difco), 5 g NH\({}_{4}\)Cl, and 1.06 g NaHCO\({}_{3}\) (equivalent to 25.2 mM). The initial pH of the medium was varied between 6-10 with hydrochloric acid and buffer solution. After autoclaving at 121 \({}^{\circ}\)C for 15 min, the medium was cooled and 14 g CaCl\({}_{2}\cdot\)2H\({}_{2}\)O and 1 g urea were added.  
The measurement of calcite precipitation was performed according to the Standard Test Method for Rapid Determination of Carbonate Content of Soils [42]. A calibration curve was plotted using 1 N hydrochloric acid (80 mL HCl and 720 mL distilled water) and 0.2, 0.4, 0.6, 0.8, and 1.0 g of calcite powder (CaCO\({}_{3}\): Showa Chemicals Inc. (Tokyo, Japan)). The calcite powder in the reaction cylinder reacted with HCl, and the carbon dioxide (CO\({}_{2}\)) pressure generated by this reaction was measured to plot the calibration curve. It was assumed that the pressure of CO\({}_{2}\) was proportional to the amount of calcite precipitated. Using this method, calcite precipitation was measured and the optimal conditions for precipitation were determined by varying different parameters like pH, temperature, and reaction duration.  
The testing conditions included different pHs (6, 7, 8, 9, and 10) of the medium, temperatures (20, 30, 40, and 50 \({}^{\circ}\)C), and reaction durations (2, 3, and 4 days). First, the two species under investigation were activated by culturing in 35 mL of tryptic soy broth (TSB; Difco Laboratories, Detroit, MI, USA, pH 7.2) for 2-3 days at 28 \({}^{\circ}\)C (Figure 2a)). The microbes were then incubated for 3 days at 30 \({}^{\circ}\)C before injecting them into the urea-CaCl\({}_{2}\) medium (Figure 2b). For this, 50 mL of urea-CaCl\({}_{2}\) medium with a pH ranging from 6-10 was prepared in Teflon beakers, and inoculated with 2 mL of 108-109 CFU/mL microbes. Then the beaker was covered and placed in the incubators, temperatures of which were set to 20, 30, 40, and 50\({}^{\circ}\)C. For twenty different sets of pH and temperatures, the tests were performed for different reaction durations of 2, 3, and 4 days. Subsequently, the beaker containing the medium was oven-dried at 105 \({}^{\circ}\)C for 2 days (Figure 2c). It was placed in the reaction cylinder as in Figure 1, and 20 mL of 1 N HCl was poured into it. The calcite that had precipitated at the bottom of the beaker chemically reacted with HCl, emitting CO\({}_{2}\) gas. According to the ASTM D4373 [42], the pressure of the CO\({}_{2}\) gas was measured to determine the amount of calcite precipitation.  
Figure 1: Experimental process for measuring the calcite precipitation.
## 4 System Realization
### Results of the Experiment  
We develop and deploy the industrial product information traceability system under the environment built above, take the quality and safety information on the critical parts of a specially shaped beam pumping machine produced by a Xinjiang petroleum machinery manufacturing enterprise as a sample to test, carry out the uplink operation for the product's information, execute the same endorsement strategy, and synchronize the storage among the nodes, and the product quality and safety information of the industrial product can be traced through the QR code of the associated identification information. Figure 7 shows the general consumer traceability query page; by entering the product's unique identification code for a product traceability query, you can query information on the product from its production to its sale in the intermediate stage from various links in the information. In terms of the state of the product, the department/point inspector to which it belongs, and its production date, the product is associated with the code ID, Fabric hash sequence value, and other information, breaking the traditional information exchange barriers in the traceability process between enterprises to improve the efficiency of product traceability; when there are product quality problems, we can quickly locate the responsible body and pursue those responsible for remediation. An example of partial traceability data validation for industrial products is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Industrial** & \multirow{2}{*}{**Identification Number**} & \multicolumn{2}{c}{**Fabric Transaction Sequence**} \\
**Product** & & **Number (Data Hash)** \\ \hline \multirow{2}{*}{The donkey head} & XX/60131125860 & 1d3 be7c7c51039 aa73 de42 ec337a32b3c6 \\  & 30 cyj0400201 & e6918 ae2c3e36c7759 ca29c43c596 bd \\ \hline \multirow{2}{*}{Beam} & XX/60131125860 & a50s5 dk385d0d072 cda25a43f23905 \\  & 30 cyj0400203 & e447a7a5a8c7 ce556 af60 cf5d121299 \\ \hline \multirow{2}{*}{Gearbox output shaft} & XX/60131125860 & f7570 ae43e7b802335866 befeded9c5f06 \\  & 30 cyj0400501 & d68 bf8 bc2a48f6 bdede8d3f115100f2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example of partial traceability data validation for industrial products.  
Figure 6: Large screen for monitoring industrial product quality information at the manufacturing stage.
## 3 Results
### Cell Culture Optimization into the EUs
#### 3.1.4 Cell Density to Plate  
Considering that the period from handover to the installation into the KUBIK could last 5 days (worst-case scenario) without any possibility to change the culture medium, we seeded huMPCs on ThermanoxTM coverslips at different densities to establish the appropriate one to be used to study cell proliferation or cell differentiation avoiding scarce or excessive cell confluence. We seeded huMPCs at 1.0-5.0 \(\times\) 10\({}^{4}\) or 2.5-7.5 \(\times\) 10\({}^{4}\) cells per coverslip for the proliferation and differentiation condition, respectively After 3 days in GM, cells were cultured for a further 5 days at T mimicking the T profile reported in Figure 2, and then cultivated for additional 3 days in GM or in DM to induce differentiation. We found that 2.0 \(\times\) 10\({}^{4}\) and 4.0 \(\times\) 10\({}^{4}\) cells per coverslip were the best plating densities for cell proliferation and cell differentiation purposes on board the ISS, respectively (data not shown).  
Therefore, we evaluated huMPCs seeded at the identified densities and cultured in the above reported conditions on plates or in EUs and found that they displayed similar proliferation and differentiation capabilities (Figure 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **25** & **27** & **30** \\ \hline Cell survival (\% of control) & 30.5 \(\pm\) 4.2 & 98.2 \(\pm\) 6.2 & 98.6 \(\pm\) 6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cell resilience to temperature. Cell survival percentage of huMPCs cultured in the EUs at the indicated T for 5 days with respect to huMPCs cultured in parallel at 37 \({}^{\circ}\)C in plates (control).  
Figure 3: Myoblasts and myotubes after 3 days of culture in proliferation (GM) or differentiation (DM) conditions as viewed by phase-contrast microscopy. Magnification, 20\(\times\).  
We calculated the proliferation rate expressed as population doubling level (PDL) after 10 days of culture in GM, and the capability to differentiate expressed as ability to fuse (Fusion Index, FI%) after 7 days in DM (Table 3). We found that huMPCs showed similar PDL (4.0 vs. 3.8) and FI (55.2% vs. 57.0%) when cultured on plates or in EUs.
## 4 Experimental Results and Discussion
### IoT Sensor Node Application  
An IoT application was implemented to test the capabilities of the proposed PMS for a power demanding extraction conditions. A Wi-Fi-enabled temperature smart node was designed and built for this purpose. This sensor node was based on the IoT platform Photon (Particle(r), San Francisco, CA, USA) [50]. As seen in Figure 10a, the IoT circuit board was wirelessly connected to a router to send the collected data over the internet, and the information gathered was retrieved using a handheld iPhone(r) device. Figure 10b shows that the 5 F output supercapacitor was charged from 0 V to 3.3 V with an initial charging time (\(t_{i}\)) of 11.3 h; at 3.3 V the internet-enabled smart node was enabled, and the data was transmitted wirelessly to a remote server. After the data packet was received in the smartphone, the sensor was disconnected and the output capacitor voltage presented an output voltage of 2.8 V. It took an average recharging time (\(t_{r}\)) of three hours to fully recharge the supercapacitor from 2.8 V to 3.3 V. The process was repeated six times in the span of 24 h. The collected temperature readings are shown in Figure 10c.  
Figure 10: \(Cont\).  
Figure 9: MFC interleaved power extraction measurements: (**a**) Phase one and (**b**) phase two.  
By effectively powering the IoT application with the MFC array, the system robustness was confirmed after providing continuous power extraction in the span of 24 h, in such period the MFC did not show any abnormal stress or failure, validating in this way the potential use of MFC technology for power-hungry systems, such as IoT-applications.
## 6 Improvements Derived from the Developed Questionnaire
### Smart Flooding with MAP-Me and Part Routing (SF MM PR)  
Frequently used names in ICN are suggested to be formatted in a hierarchical manner to be able to aggregate the names when used in the FIB and PIT structures. Leveraging this information, one can further strengthen the naming rules to fit a hierarchy with \(\varnothing\) as the root vertex of a tree graph and each edge being traversed adding a '/'. For this article, each vertex in the resulting ICN name graph has a string of any type. An example of the resulting graph is shown in Figure 4. From graph theory, it is known that one can define a graph distance \(d(u,v)\) where \(u,v\) are vertices of the graph and the distance is defined as the minimum number of edges needed to be traversed to get from vertex \(u\) to \(v\). This definition permits the definition of a metric space.  
Although ICN names can be easily mapped to a graph using this strategy, within an Information-Centric Network, a particular ICN name would be divided into chunks to transmit the content in pieces throughout the network as mentioned in Section 4. Leveraging this characteristic of ICNs, a numbered label is added to identify the chunks. These identified chunks of information related to an ICN name are called Parts. We assign numbers to the Parts to create another metric space  
Figure 4: A graph for ICN names.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategy** & **Question 2** & **Question 3** & **Question 4** \\ \hline SF & \(\bigcirc\) & \(\times\) & \(\times\) \\ SF MM & \(\bigtriangleup\) & \(\times\) & \(\times\) \\ SF 3N & \(\bigcirc\) & \(\times\) & \(\triangleup\) \\ SF MM PR & \(\bigcirc\) & \(\bigcirc\) & \(\times\) \\ SF MM PR 3N & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) \\ \hline \(\bigcirc=\)**Satisfied**, \(\triangleup=\)**Partially satisfied**, \(\times=\)**Unsatisfied**. \\ \end{tabular}
\end{table}
Table 3: Questionnaire evaluation results for ICN forwarding strategies.  
utilizing the one-dimensional Euclidean distance to define the distance between Parts. The definitions of '\(\geq\)' and '\(<\)' for natural numbers can then be used to establish intervals and aggregate the Parts present at any node.  
This new information can be easily leveraged by the MAP-Me enhancements. In the MAP-Me-IU, the producer would add the next ICN name Part \(p_{j}\) for information transmission. When a ICN CR receives this information on a given interface, it only changes the state of the interface to green for Parts \(\geq p_{j}\). The modification of the MAP-Me update system to utilize Part information is called Part Routing. When Smart-Flooding, MAP-Me enhancements and Part Routing are used, the resulting forwarding strategy is summarized as SF MM PR. This forwarding strategy would satisfy Question 2 as the MAP-Me enhancements would update the location of Parts directly. Question 3 is also satisfied as the ICN namespace is a metric space.
## 2 Notation and Background
### Lie Groups
: _A few examples of Lie groups include the Euclidean space \((\mathbb{R}^{n},+)\) with the additive group structure, \((\mathbb{R}_{+},\cdot)\) the positive real line with a multiplicative group structure, the space of invertible real matrices \(\mathrm{GL}(n)\) equipped with a multiplication of matrices forming a Lie group, and the rotation group \(\mathrm{O}(n)\), consisting of real orthogonal matrices with determinant one or minus one forming a subgroup of \(\mathrm{GL}(n)\)._  
The identification of the space of left-invariant vector fields with the Lie algebra \(\mathfrak{g}\) allows for a global description of \(\Delta_{G}\). Indeed, let \(\{v_{1},\ldots v_{d}\}\) be an orthonormal basis of \(T_{e}G\). Then, \(V_{i}(g)=(dL_{g})_{e}v_{i}\) defines left-invariant vector fields on \(G\) and the Laplace-Beltrami operator can be written as (cf. [24], Proposition 2.5)  
\[\Delta_{G}f(e)=\sum_{i=1}^{d}V_{i}^{2}f(e)-V_{0}f(e),\]  
where \(V_{0}=\sum_{i,j=1}^{d}C_{ij}^{j}V_{j}\) and \(C_{ij}^{k}\) denote the structure coefficients given by  
\[[V_{i},V_{j}]=C_{ij}^{k}V_{k}. \tag{4}\]  
By left-invariance, \(\Delta_{G}f(g)=\Delta_{G}f\circ L_{g}(e)=\big{(}dL_{g}\big{)}_{e}\Delta_{G}f(e)\).