esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Epithelial Homeostasis and Perturbation in Three-Dimensional Human Esophageal Organoids
## 3 Results
### HOME0 Permits Normal Esophageal Organoid Formation from Patient Biopsies  
Given improved OFR and the differentiation gradient formation for EPC1 and EPC2 cells in HOME0 (Figures 1 and 2), we further tested HOME0 on PDO formation with normal esophageal endoscopic biopsies. Following dissociation, biopsy-derived cells were processed for organoid culture, either immediately (EN1) or following cryopreservation (EN2 and EN3). Testing HOME0 and three other media, HOME50, ADF3+, and KSFMC (Table 3), all the biopsy samples (EN1-3) gave rise to spherical structures. When these normal PDOs were subjected to morphological analyses, an inward differentiation gradient was observed in structures formed in KSFMC or HOME0 (Figure 4). Cornification, normally absent in normal human esophageal squamous epithelium, was present in the organoids grown in KSFMC, but limited, if any, in HOME0. Organoids grown in HOME50 or ADF3+ displayed either nuclear atypia or degeneration, suggesting that cells may be under a certain stress in these medium conditions.  
Figure 4: Normal esophageal PDOs recapitulate squamous epithelial differentiation gradient when grown in KSFMC or HOME0. Normal esophageal PDOs (EN1-3) grown with indicated media were subjected to H&E staining at the end of passage 1. Note that structures grown in KSFMC and HOME0 had concentric layers of spinous-like cells with a differentiation gradient with a keratinized material to a partial (HOME0) or a total extent (KSFMC). Structures grown in HOME50 or ADF3+ displayed no apparent differentiation gradient with involutional changes or mild atypia. Scale bars, 100 \(\upmu\)m.  
While none of these media permitted indefinite growth, replicative lifespan or the total number of cell division measured as population doubling level (PDL) appeared to be higher in KSFMC and HOME0 than HOME50 and ADF3+ (Figure S2A-C). The maximum PDL of these normal mucosa-derived organoids was 40-60 when grown in KSFMC and HOME0 (Figure S2A-C), compatible with the PDL for normal human esophageal epithelial primary monolayer culture where cells cease proliferation and undergo senescence at the end of their replicative lifespan aka the Hayflick limit [31; 33]. The PDL was less than 20 when grown in HOME50 or ADF3+ (Figure S2A-C). Interestingly, OFR was found to be the highest at passage 1 for all the clinical samples, especially in HOME0 and KSFMC, although OFR decreased as passaged in subculture (Figure S2D-F). While initial organoid culture may contain biopsy-derived non-epithelial cells that may account for a lower OFR, the transient elevation of OFR at the first passage (P1) suggests that esophageal organoids contain a subset of epithelial cells that underwent renewal to initiate organoids in subsequent passages.
## 2 Taxonomy for the Threat Model
### Threats against Integrity  
We categorized these threats into five types: message append threats, message alteration threats, tampering threats, session hijack and data diddling threats. These attacks can be categorized in other hierarchies; however, the presented taxonomy is most appropriate as per the prevailing security paradigm in 6G communication. Integrity-related threats have severe implications in the 6G network [21]. Several risks are related to the unconventional implementation of the Internet of Things (IoTs), such as intelligent water quality management for a healthy ecosystem [42]. Various techniques, especially blockchain, are widely considered for integrity measures in 6G-enabled IoTs.  
Similarly, unconventional approaches such as machine learning-based blockchain techniques can cover several security prospects, especially data integrity [43]. Implementing various cryptographic techniques to counter message append, message alteration and message tampering attacks may result in massive processing overheads in ultra-dense communication, which will become a challenge for resource constraints of 6G-enabled IoTs [44]. Time-specific session hijack attacks compromise the integrity of whole data communication. Similarly, session hijacking is considered a broad attack that may result in several simultaneous security incidents in a 6G-enabled fog computing environment [45]. Likewise, data diddling also transforms into many attack formats such as infecting malicious data devices to create integrity issues in massive communication. In [46], the authors suggest QR code-based secret sharing in 6G-enabled industrial IoTs to prevent attackers from data diddling.
# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 9 Challenges and Future Directions
Multi-agent reinforcement learning (MARL) models with multiple affordances from smart intersections cooperate to perform traffic optimization collectively over an urban network [246; 247]. Neural network-based deep learning methods forecast congestion to enable proactive intersection control. Digital twins will enable critical real-time traffic monitoring, simulation, and predictive traffic modeling [248], leading to increased resilience in an intersection model [249; 250]. Cloud digital twin platforms enable real-time synchronization between physical intersections and virtual simulation models for live decision-making [251; 252]. Fifth-generation-based V2X communication can improve vehicle-to-vehicle communication, pedestrian safety systems, or traffic-responsive control in real time [253; 254]. Edge AI enables real-time, decentralized decision-making at intersections, reducing cloud dependence and latency [255; 256]. Sixth-generation-enabled mobility ecosystems can be used for fast data transfer at ultra-high speeds, and AI can be used for next-level traffic control at intersections for ground transport [257; 258; 259; 260; 261; 262; 263]. Standardized AI governance models can be developed for process-level-wise applications in traffic management [264; 265]. Cross-jurisdiction interoperability agreements: ensure common data protocols for intelligent intersections between states and transportation sectors [266]. Autonomous vehicle interaction regulations: developing policies for smart intersections to address CAVs, liability, quantum risk assessments, and procedures for emergencies [267]. Commutable and self-regulating smart intersections: these intersections that use vehicle and queue detectors, smart cameras, LED road markers, and Variable Message Signs (VMSs) allow vehicles to be channeled appropriately into specific lanes, depending on the traffic demand level and distribution of traffic flows, as is the case with the novel COM-Roundabout (Figure 5) [268]. In this and in other smart intersection types, a traffic control area could be implemented where CAVs can start to communicate with the physical infrastructure. Within this management area, CAVs could provide kinematic information (e.g., instantaneous position, speed, and acceleration) to the smart intersection manager and gather optimized trajectories to maximize traffic performance indicators, such as the LOS [269; 270]. The smart intersection manager should properly channel the CAVs between the lanes, coordinatemanoeuvres, and control the traffic lights and adapt them to the traffic conditions in real time [269]. In brief, research priorities and future work will be directed towards AI-assisted optimization, security cybersecurity solutions, digital twin integration for intelligent intersections, and policy-kind frameworks to scale sophisticated junctions easily and allow them to remain scalable.
## 4 Materials and Methods
### Cytotoxic Activity  
#### 4.2.1 Collection and Preparation of Cells  
The cytotoxicity of the 3,4,5-trimethoxybenzoic acid derivatives was evaluated in LLC-MK2 cells with the aim of analyzing their selectivity for _T. cruzi_ in relation to host cells. LLC-MK2 cells (ATCC CCL-7) are a lineage of epithelial cells obtained from the renal tubules of monkeys (_Macaca mulatta_). The cells were obtained from the Rio de Janeiro Cell Bank (BCRJ) and cultivated in DMEM medium (Dulbeccoo's Modified Eagle's Medium, pH 7.4)--supplemented with 10% FBS (fetal bovine serum) and the antibiotics (penicillin--200 UI-mL\({}^{-1}\) and streptomycin--130 mg-mL\({}^{-1}\)). The epimastigote forms (Y strain) of _T. cruzi_ were provided by the Parasite Biochemistry Laboratory of the University of Sao Paulo (USP) and cultivated in LIT medium (_Liver Infusion Trypose_, with NaCl 4 gL\({}^{-1}\); Na\({}_{2}\)HPO\({}_{4}\).12H\({}_{2}\)O 11.6 gL\({}^{-1}\); KCl 0.4 gL\({}^{-1}\); glucose 2.2 gL\({}^{-1}\); trypose 5 gL\({}^{-1}\); liver infusion 5 gL\({}^{-1}\); and bovine hemin 25 mg\(\cdot\)L\({}^{-1}\) at pH 7.4), supplemented with 10% FBS, and the antibiotics (penicillin--200 IU\(\cdot\)mL\({}^{-1}\) and streptomycin--50 mg\(\cdot\)L\({}^{-1}\)). The cultures were maintained at 28 \(\pm\) 1 \({}^{\circ}\)C in a BOD (_Biochemical Oxygen Demand_) oven in sterile bottles [32]. The trypomastigote forms of _T. cruzi_ were obtained from host cell infections [33]. For this, LLC-MK2 cells were cultured in sterile 25 cm\({}^{2}\) bottles at a concentration of 1 \(\times\) 10\({}^{5}\) cells-mL\({}^{-1}\) in DMEM 10% FBS medium. After 48 h of incubation in a CO\({}_{2}\) oven, the medium was replaced by DMEM 2% FBS without antibiotics, and the cells were infected with trypomastigotes at a rate of 20 parasites per cell. To evaluate the effect on intracellular amastigotes, LLC-MK2 cells (10\({}^{5}\) cells-mL\({}^{-1}\)) were cultured in 24-well plates on 13 mm diameter sterile coverslips for 24 h incubated in a CO\({}_{2}\) oven. The cells were then infected with trypomastigotes (2 \(\times\) 10\({}^{6}\) cells-mL\({}^{-1}\)) and incubated for 48 h. They were then further treated with Ch-DiCl (150, 75, 37.5, and 18.75 uM) and kept in a CO\({}_{2}\) oven for 24 h.
## 4 Results and Analysis
### Price Comparison and Average Savings of Participants  
Figure 14 shows the comparison of the average P2P price versus the RTP and FIT. As is evident from the figure, the participants were offered a price that was competitive to the market values. The customers were offered a price that was lower than the retail tariff, and the prosumers sold their energy at a higher price than the FIT. This resulted in additional saving/earning for the market participants.  
Note that both of the strategies offered an average P2P price that was competitive worth respect to the market values. Table 1 shows the percentage of P2P trading for the different intervals using each trading strategy. The percentage shows the amount of PV energy traded via the P2P market. Table 1 also shows the average monthly savings of the participants. Each customer saved around $3.50, and each prosumer earned an additional $5.80 for the ZI strategy. Similarly, the average savings per participant for the GT approach were $3.25 (per customer) and $5.50 (per prosumer). The monetary advantages for the market participants were established without impairing their comfort factor (shifting of loads). This shows  
Figure 14: Comparison of average P2P price with retail and FIT during the solar hours for each trading strategy.  
Figure 13: Comparison of P2P traded energy over the solar hours for each trading strategy. The results are plotted as double axis graph. Dotted plot shows the prosumers’ excess PV power with respect to the second axis.  
that the presented framework improves the welfare of the P2P community by utilizing the excess PV power of the prosumers in an efficient manner.
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 4 Joint Iterative Detection and SCL Decoding Receiver  
In this section, the extrinsic message of the coupled polar decoder is constructed and the details of JIDS algorithm are presented.  
In [17], the authors proposed a JIDD receiver for the uplink PC-SCMA system. The MPA detector outputs its extrinsic messages within one inner-loop iteration and directly as the input prior messages of SCAN decoder, and vice versa. The receiver only needs outer iterations and no inner iterations of constituent SCMA detector and SCAN decoder are needed. Since the performance of SCAN decoder is not ideal, the performance of the PC-SCMA system is limited. Therefore, this subsection proposes a JIDS algorithm for the PIC PC-SCMA system.  
To better describe the algorithm, we define some notations as follows:  
* \(M_{gi\to qj}\): The messages passing from the \(i\)-th function node \(g_{i}\) to the \(j\)-th variable node \(q_{j}\).
* \(M_{qj\to g_{i}}\): The messages passing from the \(j\)-th variable node \(q_{j}\) to the \(i\)-th function node \(g_{i}\).
* \(\mathcal{G}_{j}\): The set of function nodes that connect to variable node \(q_{j}\).
* \(\mathcal{G}_{i}\): The set of variable nodes that connect to function node \(g_{i}\).
* \(\left\{Q_{j}\backslash i\right\}\): Excluding function node \(g_{i}\) from the set of \(\mathcal{Q}_{j}\).
* \(\left\{\mathcal{G}_{i}\backslash i\right\}\): Excluding variable node \(q_{j}\) from the set of \(\mathcal{G}_{i}\).
* \(P\left(s_{v}^{l}\right)\): The prior information of the \(l\)-th SCMA codeword for user v.
# Study on Microseismic Monitoring of Landslide Induced by Blasting Caving
## 2 Engineering Background  
### Mining Operation Context  
The Shizhuyuan polymetallic mine is a large skarn deposit, primarily containing tungsten, molybdenum, bismuth, and tin. Early operations focused on the III-1-1 rich ore zone, which spans 16-24 horizontal lines and VII-XI vertical lines, covering an area of 315 \(\times\) 313 m\({}^{2}\) at elevations between 490 and 558 m. The mining area was divided into four block caving sections from west to east, with each section further subdivided into nine ore blocks from north to south, totaling 36 ore blocks. Each stope measures 64 m in length, 20 m in width, and 68 m in height. Continuous pillars (P1-P5 and C1-C8) with 15 m spacing were retained between stopes and block caving sections (see Figure 1). Due to historical reasons, the underground stopes were not backfilled, leaving approximately 60% of the ore pillars and over 3 million m\({}^{3}\) of void space in the mining segment. To ensure continuous mining operations and effectively manage the voids while recovering ore pillars and hanging wall-rich ore, the mine adopted the caving method for pillar and hanging wall recovery, starting in 2006. This process involved first creating a pressure-relief shaft in the northwest corner of the mining area to connect with the surface, followed by progressive caving of the pillars and hanging wall ore using the shaft as a free face [22; 23], as shown in Figure 2.
## 5 Methodology
### Spatial Statistics  
In this research, we used spatial statistics techniques such as density-based clustering and hotspot analysis, supported by ArcGIS Pro, to analyze mobility data from SLMA and gain insights into the spatial patterns, processes, and relationships. These techniques allowed us to visualize spatial patterns and understand underlying processes, as spatial statistics is an interdisciplinary field that uses concepts and methods from statistics, geography, computer science, and other fields [46; 47]. By utilizing these tools and techniques, we took full advantage of the spatial component of our data and gained a deeper understanding of the patterns and relationships present in our study area (Figure 4).  
Using GeoPandas and the h3 package in Python [48], we tracked individuals in key locations like residential areas, restaurants and bars, hotels, liquor stores, and Cardinals Stadium (Figure 4). To avoid clutter and noise in the visualization, we removed other data points such as people in transit whose GPS pings were located on roads. We then used polylines to connect the first and last location of unique individuals and employed the shapely package in GeoPandas to create arcs to visualize any return movements. To further aid in visualization, we aggregated the movements within 6-mile hexagons and calculated the flow intensity of individuals moving between these hexagons. The resulting map generated in ArcGIS Pro revealed that the intensity of people moving towards the city was higher before the game and that this intensity decreased as people moved away from the city area after the game. While the two graphs may appear similar at first glance, a closer examination reveals that the flow intensity increases around specific areas, particularly near the stadium, after the game. This post-game clustering indicates a significant movement of people towards local businesses, which corresponds to the increased economic activity observed in those areas. The increased flow intensity around the stadium and key points of interest post-game suggests a boost in local spending.  
Figure 3: Visualized workflow and methodology.
## 4 Modification of the Peck Formula  
Given that [PERSON]'s empirical formula only has two characteristic parameters, \(S_{\max}\) and \(i\), it cannot accurately reflect the relationship between surface settlement and the distance \(L\) between the monitoring section and the composite plane in the settlement change range of the composite strata. Moreover, the values of Smax and \(i\) are significantly correlated with \(L\). Therefore, parameters \(\alpha_{\rm L}\) and \(\beta_{\rm L}\) are introduced to modify the values of \(S_{\max}\) and \(i\) in [PERSON]'s empirical formula.  
\[S_{x}={S^{\prime}}_{\max}{\rm exp}\biggl{[}-\frac{x^{2}}{2{i^{\prime}}^{2}} \biggr{]} \tag{13}\]  
\[{S^{\prime}}_{\max}=\alpha_{\rm L}\times S_{\max} \tag{14}\]  
\[i^{\prime}=\beta_{L}\times i \tag{15}\]  
Here, \({S^{\prime}}_{\max}\) and \(i^{\prime}\) represent the modified values of \(S_{\max}\) and \(i\), respectively, and \(\alpha_{\rm L}\) and \(\beta_{\rm L}\) are the modification parameters for \(S_{\max}\) and \(i\). Relationships between various strata's \(L\) and the modification parameters \(\alpha_{\rm L}\) and \(\beta_{\rm L}\) were obtained through computation and fitting analysis using field measurement data and numerical simulation results.  
\[\alpha_{\rm L}=\left\{\begin{array}{ll}\mbox{Stratum 6: }0.65+0.01\times L,&L\leq 35\\ \mbox{Stratum 7: }1.6-0.03\times L,&L\leq 25\end{array}\right.\]  
\[\beta_{L}=\left\{\begin{array}{ll}Stratum 6:&1.32-0.007\times L,&L\leq 50\\ \mbox{Stratum 7: }0.81+0.004\times L,&L\leq 30\end{array}\right.\]  
To validate the applicability of the modified Peck formula in the settlement change range of composite strata, a comparison was made between the numerical simulation results and the calculation results obtained using the modified Peck formula. Three examples were considered, with corresponding values of \(L\) being 5, 15, and 25 meters, as shown in Figures 13 and 14. It can be observed that the predicted maximum settlement values using the Peck empirical formula are significantly larger than the fitted values based on the measured data for the silt-clay interlayer segment, while the settlement values on both sides of the axis are slightly smaller than the fitted values. At \(L=15\) m in the limestone stratum, the predicted results from the Peck empirical formula are slightly smaller than the fitted values, but significant discrepancies still exist at \(L=5\) and 25 m. The predicted values using the modified Peck formula show good agreement with the measured data for both types of strata, achieving satisfactory prediction accuracy for all three example cases. The comparison between the Peck empirical formula and the modified Peck formula indicates that the latter provides more accurate predictions for surface settlement in composite strata.  
Figure 12: Comparison of simulated and measured values of \(i\) for various monitoring cross-sections.
# Adaptive Clustering of Users in Power Domain NOMA
###### Abstract
While the adaptive step attempts to fix clusters that do not reach a minimum SINR difference constraint between users by splitting, regrouping or transmitting by OMA. As result, higher user data rates were achieved. It was also noted that an increase in the number of users in a cluster does not always lead to higher NOMA rates. It is possible to observe some common aspects to these works. They do not consider the temporal variation of the users' channel gain. In addition, the proposed clustering techniques are generally non-adaptive since the investigated systems' intrinsic dynamics are commonly not considered. Even works that propose an adaptation of clusters attempt to correct groupings that were initially made erroneously, still require initial knowledge of how many clusters will be formed.
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
## 2 Materials and Methods  
### Wheat Sample  
A wheat spike is a symmetrical-like structure, where spikelets are arranged interactively on the spike. At the end of the spikelets, wheat awns are distributed outwards. In Figure 1a,b, one of the spikelets is marked by a red bounding box, and Figure 1c shows a single instance of a wheat spikelet. Every spikelet has the possibility of possessing about 0-3 grains, which is highly related to the yield of wheat. For the sake of more precise phenotyping, this study analyzed the frontal view image of wheat spikes based on three steps: spikelet instance segmentation, grain number classification, and total grain count.  
The experiment was conducted in the field at the Agricultural Science Changxing Test Station of Zhejiang University (30\({}^{\circ}\)15\({}^{\prime}\) N, 120\({}^{\circ}\)13\({}^{\prime}\) E). The wheat varieties involved in this study were \"Zhengmai 618\" (Z618), \"Yannong 19\" (Y19), and \"Sumai 8\" (S8). As shown in Figure 2, the three varieties had obvious differences in appearance. Z618 had plump grains, short awns, and spikelets with clear boundaries. Y19 had moderate awns, and its spikelets were closely distributed on the rachis, resulting in spikelets that occluded each other. S8 had relatively plump grains, long awns, and sparse spikelets. From the perspective of the growth period, the spikes were green at the early filling period, the starch accumulation of wheat grains was completed, and the shape and quantity of grains could be distinguished from the appearance. At the late filling period, the spikes turned from green to yellow and the grains became plump. The spikes turned yellow completely at the mature period. The grains were dehydrated, and their recognizability decreased compared to the filling period.