esa-sceva/satcom-chunk-collection huggingface dataset:
# Production of Alternative Fat from Adipose-Derived Stem Cell from Bovine in 3D Culture
## 2 Materials and Methods
### Adipogenic Induction  
For 2D culture, bADSCs were seeded at a density of 3 \(\times\) 10\({}^{5}\) cells per well in 6-well plates. Once the cells reached approximately 80% confluency, the medium was replaced with adipogenic induction medium (AIM), consisting of DMEM supplemented with 1% Antibiotic-Antimycotic (A/A), 5% fetal bovine serum (FBS), and the following adipogenic factors: 10 uM human insulin (Sigma-Aldrich, I2643, St. Louis, MO, USA), 1 uM dexamethasone (Sigma-Aldrich, D4902, St. Louis, MO, USA), 5 uM troglitazone (Sigma-Aldrich, R2408, St. Louis, MO, USA), and 500 uM isobutyl methylxanthine (IBMX; Sigma-Aldrich, I7018, St. Louis, MO, USA). After 3 days of induction with AIM, the medium was replaced with adipogenic differentiation medium (ADM), composed of DMEM supplemented with 1% A/A, 2% FBS, 10 uM human insulin, and 5 uM troglitazone. Differentiation was continued for 18 days in ADM. The control group consisted of cultured cells with complete culture medium for 21 days. All mediums used during differentiation were changed every 3 days.  
For 3D culture, autoclaved TVPs (3 cm\({}^{2}\)) were first immersed in a 0.2% gelatin solution (Sigma-Aldrich, G1890, St. Louis, MO, USA) and incubated overnight at 4 \({}^{\circ}\)C for coating. The coated TVPs were then washed twice with 5 mL of DPBS and placed in a 12-well plate. Subsequently, 50 uL of DMEM containing 6 \(\times\) 10\({}^{6}\) bADSCs was seeded onto the gelatin-coated TVPs (Hokyung-Tech, Anseong, Gyeonggi-do, Republic of Korea), and the cells were allowed to attach for 30 min in a 37 \({}^{\circ}\)C incubator. After cell attachment, 3 mL of complete culture medium was added to each well containing the bADSC-seeded TVPs. The constructs were cultured for 2 days to allow cell proliferation. After 2 days, the bADSC-seeded TVPs were transferred to a 100 mL sterile glass bottle containing 30 mL of AIM for adipogenic induction. The TVPs floated in the medium but remained fully submerged to prevent drying. After 3 days of induction, the medium was replaced with ADM and maintained for an additional 18 days, with medium changes every 3 days. Adipogenic differentiation was assessed over 21 days following the initiation of suspension culture.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 5 Conclusions  
In our previous studies on the application of compressive sensing to gravitational microlensing, we showed that CS is able to reconstruct the light curve without compromising the scientific parameters of interest. However, our study did not incorporate the effect of noise. In this study, we show the effect of source noise as well as measurement noise. For source noise, we note that choosing a measurement matrix with low mutual coherence can improve results, as expected by CS theory. Our analytical and numerical results show that for a measurement matrix with lower mutual coherence, CS reconstruction works better. We compared the Gaussian measurement matrix with 0 mean, and 0.25 units of variance, with a binomial measurement matrix with 0.25 units of variance as well. The binomial measurement matrix has a higher mutual coherence as compared to a Gaussian measurement, as also shown through numerical simulations. Through our simulations, we can conclude that matrices with lower mutual coherence provide better CS results when source noise is present. For the measurement noise analysis, we applied Poisson noise to depict shot noise. As Poisson noise is dependent on the signal itself, we note that for high-magnification events, where the flux of the microlensing source star pixels are comparatively higher, Poisson noise during CS measurements acquisition can lead to a lower % error. In addition, we show through our analytical and numerical results that by reducing the value of \(p\), where \(p\) represents the expected value of the binomial distribution of \(A\), we can achieve lower noise variance, thereby decreasing the average % error in CS reconstruction. Our results demonstrate effectiveness using the OMP algorithm. Various other algorithms, such as convex optimization, can provide better sensitivity and reconstruction accuracy with a trade-off of computational complexity.  
Conceptualization, A.K.-P.; Investigation, A.K.-P.; Software, A.K.-P.; Supervision, R.K.B. and T.M.; Writing--original draft, A.K.-P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Figure 13: Average % error for an image with applied Poisson noise to CS measurements for a single-lensed microlensing event with \(\mu_{0}=0.01\) using binomial measurement matrix with \(p=0.5\) and \(p=0.25\).  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 5 Experiment
### Experiment on Synthetic Objects
#### 5.1.3 Metrics  
We evaluate the results of the synthetic experiments in terms of rendering quality and geometric quality. For rendering quality, we use the peak signal-to-noise ratio (PSNR) and the learned perceptual image patch similarity (LPIPS) metric. The PSNR measures the fidelity of the rendered 2D images by calculating the pixel-wise difference between the images rendered from the trained model and the ground truth images, with higher PSNR values indicating better image quality. LPIPS, on the other hand, evaluates perceptual similarity using a deep learning-based model, where lower LPIPS values indicate greater visual similarity to the ground truth images.  
For geometric quality, we use the F-score and Chamfer distance metrics to assess the difference between the reconstructed 3D mesh and the ground truth mesh. The F-score is calculated by sampling points from each mesh and setting a threshold of 1 cm to evaluate completeness and accuracy, where higher F-score values indicate better alignment between the reconstructed and ground truth meshes. The Chamfer distance measures the average distance between points on the reconstructed mesh and their closest counterparts on the ground truth mesh, with smaller values indicating a closer match and thus higher geometric fidelity.
## 4 Simulation Experiments: Security Analysis and Comparison
### Correlation Analysis  
The aim of conducting a correlation analysis is to evaluate how much of a linear connection exists between the plaintext \(P\) and the ciphertext \(C\). The correlation coefficient \(R\) is computed using the formula given below [45]:  
Figure 3: An illustrative example of the proposed decryption algorithm.  
\[R=\frac{n\sum\limits_{i=1}^{n}(P_{i}C_{i})-\sum\limits_{i=1}^{n}P_{i}\sum\limits_ {i=1}^{n}C_{i}}{\sqrt{\left(n\sum\limits_{i=1}^{n}P_{i}^{2}-(\sum\limits_{i=1}^ {n}P_{i})^{2}\right)\left(n\sum\limits_{i=1}^{n}C_{i}^{2}-(\sum\limits_{i=1}^{n }C_{i})^{2}\right)}}, \tag{7}\]  
where \(n\) represents the length of the text, and \(P_{i}\) and \(C_{i}\) represent the ASCII codes for the \(i\)-th character of the plaintext and ciphertext, respectively. When the correlation coefficient is near 1 or \(-1\), there is a strong linear relationship between the ciphertext and plaintext. Conversely, when the correlation coefficient is close to 0, the ciphertext and plaintext have a weak linear relationship. A good encryption algorithm should have a weak linear relationship between the ciphertext and plaintext.  
We conducted experiments on different texts to evaluate the performance of the proposed scheme and summarized the results in Table 1, which shows that the correlation coefficient ranges from 0.001 to 0.08. Additionally, we compared the correlation analysis of our scheme with several state-of-the-art schemes [10; 11; 12; 13; 14; 15; 16; 17; 18]. As presented in Table 2, the proposed scheme exhibits a weaker linear association than the schemes [10; 11; 12; 13; 14; 15; 16; 17; 18].  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Number of Characters in \(P\)** & \(R\) & \
\begin{tabular}{c} \(EQ\) \\ **(\%)** \\ \end{tabular} & \(H(P)\) & \(H(C)\) &
\begin{tabular}{c} **Plaintext** \\ **Sensitivity** \\ \end{tabular} \\ \hline
30 & 0.0515 & 90.00 & 3.5232 & 4.2817 & 100.000 \\
300 & 0.0774 & 77.33 & 4.8759 & 5.6367 & 99.3333 \\
999 & 0.0244 & 76.18 & 4.4759 & 5.4658 & 98.6987 \\
3000 & 0.0012 & 81.20 & 4.5578 & 5.5356 & 98.5333 \\
5995 & 0.0021 & 82.60 & 4.5573 & 5.5114 & 98.8490 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Security analysis of the proposed text encryption scheme.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Number of Characters in \(P\)** & \(R\) & \
\begin{tabular}{c} \(EQ\) \\ **(\%)** \\ \end{tabular} & \(H(P)\) & \(H(C)\) &
\begin{tabular}{c} **Plaintext** \\ **Sensitivity** \\ \end{tabular} \\ \hline
30 & 0.0515 & 90.00 & 3.5232 & 4.2817 & 100.000 \\
300 & 0.0774 & 77.33 & 4.8759 & 5.6367 & 99.3333 \\
999 & 0.0244 & 76.18 & 4.4759 & 5.4658 & 98.6987 \\
3000 & 0.0012 & 81.20 & 4.5578 & 5.5356 & 98.5333 \\
5995 & 0.0021 & 82.60 & 4.5573 & 5.5114 & 98.8490 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed text encryption scheme with the state-of-the-art schemes.
## 3 Materials and Methods
### Study Area and Data
#### 3.1.2 Meteorological Data  
The study analyzed the meteorological data obtained from the network of agrometeorological stations of Prefecture Florina, Western Macedonia. Meteorological variables were collected from the station of Aminteo, while precipitation data were also included from a station near lake Zazari. The meteorological variables include air temperature and rainfall, while wind speed solar radiation and relative humidity were also processed to calculate evapotranspiration. Figure 3 depicts temperature and precipitation data used in this study. The availability of high-resolution radiation and sunshine data in the area enabled improved modeling of evapotranspiration and energy balance [41; 42]. For this study, reference crop evapotranspiration was calculated using the Penman-Monteith formula [43] (Figure 4).  
Figure 3: Time series of meteorological conditions (air temperature (**top**) and precipitation (**bottom**)) in the study area (2018–2024).  
Figure 2: Ombrothermic diagram for Lake Zazari (1967–2024), illustrating the monthly mean temperature (red line) and half the monthly precipitation (blue line). The summer months show a pronounced climatic water deficit (deficit: 46.90), indicating a dry period with high evapotranspiration demand relative to rainfall—a key consideration for irrigation management in the study area. Note: Rainfall values are divided by 2 to match scale with temperature (as per Gaussens’s method).
## 3 Results
### Characteristics of Spatial and Temporal Evolution of Vegetation GPP on the Qinghai-Tibet Plateau
#### 3.2.2 Model Parameter Optimization Process
Overall, the difference maps reveal a spatial trend, with positive values in the southeastern region transitioning to negative values in the central and western regions. As shown in Figure 7, the distribution of the annual average rate of change in GPP on the Qinghai-Tibet Plateau from 2001 to 2022 illustrates the dynamic changes in ecosystem productivity. Overall, GPP change exhibits significant spatial heterogeneity: the rate of change in the humid regions of the northeast and southeast is significantly positive, with 96% of the area showing a positive correlation. These regions, primarily wetlands and forest ecosystems, may have been influenced by climate warming and ecological restoration policies. The maximum annual average rate of change reached 12.40 gC/m\({}^{2}\)/year. Figure 5: Spatial distribution of interannual average GPP on the Qinghai-Tibet Plateau from 2001 to 2022: **(a)** Spatial distribution of MODIS data; **(b)** MODIS-PSN model simulates GPP spatial distribution. In contrast, the central and southern regions exhibit a decreasing trend, with the lowest rate of change at \(-\)1.61 gC/m\({}^{2}\)/year and the negative correlation area accounting for 4%, mainly concentrated in alpine grasslands and desert regions. This decrease could be linked to climate aridification and land degradation. The area showing significant change (\(p<0.05\)) accounts for 68.7%, indicating a substantial increase in GPP across the Qinghai-Tibet Plateau. The overall pattern of change reflects the complex response of the Qinghai-Tibet Plateau's ecosystems to both climate change and human activities, providing important insights for regional ecological conservation and management.
## 4 Agent-Based Simulation
### Simulation Characteristics
#### 4.2.2 Agent Navigation and Obstacle Avoidance  
Agents need to navigate the environment in order to provide effective transportation of material. Many approaches exist for the control and coordination of AMR systems. The most distinctive characteristic is that of _centralised_ versus _decentralised_ coordination or control [8]. While the central control server that coordinates the entire fleet of the facility was the predominant form of implementation, recent works increasingly seem to embrace the decentralised approach due to expected improvements regarding scalability, flexibility, and robustness [8, 52, 53].  
In our implementation, agent motion is simulated using a non-holonomic, differential drive kinematics model. In order to move towards their goals, they can adapt their velocity. Pose updates are calculated accordingly and communicated to the centralised environment representation.  
When an agent receives a new move request it will first look up the pose of the corresponding navigation goal. Then it will find the required immediate movement direction using the environment's navigation graph and the static navigation obstacles defined in the DT. The current agent pose and the goal pose are matched onto the navigation graph and the shortest path is calculated using a graph search algorithm. For this purpose, we use an implementation of [PERSON]'s algorithm. The agent will then use the roadmap nodes along the path as waypoints towards the goal. If the goal pose is found in direct line of sight, the agent will head directly towards the actual goal pose.  
We integrated the collision-free agent navigation scheme Optimal Reciprocal Collision Avoidance (ORCA) provided by the RVO2 library for collision avoidance and conflict resolution [54]. The theoretical background is described in [PERSON] et al. [55] and [PERSON] et al. [56]. The basic principle is the continuous adaptation of the agent's linear and angular velocity in order to avoid collisions with mobile and stationary obstacles along their predicted relative trajectories. Thereby each agent slightly adjusts their own speed and heading towards a value outside the so called _Velocity Obstacles_[57] the other entities represent. Depending on the parameterisation of the time step, temporal horizons, and safety margins, we can rely on a collision-free multi-agent navigation within the simulation.  
The selected approach provides a sufficient baseline operation in configurations with little congestion or navigation constraints. However, during the experiments it became obvious, that these circumstances can lead to deadlocks in crowded environments. Other approaches include priority-based schemes or swarm behaviours to mitigate this [53; 58].
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 4 Network Planning and Optimisation
### Study Metrics  
In order to evaluate the network solution, we define the following simulation metrics.  
* _Coverage_, refers to the percentage of served CPEs compared to all the CPEs in the network.
* _Serving Distance_, determines the usable distance between node either CPE-EN or between ENs.
* _Backhaul Capacity_ is divided in two parts, the _supported capacity_, referring to the maximum bit rate the links in the backhaul network could provide, while the _served capacity_ is the actual throughput used in those links.
* _Link Count_ establishes the number of hops needed for a CPE to connect to the PoP.
# Smart Low-Cost Control System for Fish Farm Facilities
## 2 Identification of Critical Factors for Control Systems for Aquaculture
### Factors Affecting Fish Wellbeing and Performance
#### 2.1.1 Factors of the Production Domain  
In this Domain, we can identify factors relative to the location in which the fish are kept, which can be in tanks or cages. For tanks, the tank dimensions (shape, diameter, and depth) [27], the tank material (plastic, fiberglass, geomembrane, concrete, and ground) [28], and the tank color [29] are the most relevant factors. Other factors, such as the aerial protection of the tank, are not included since their use is minimal in tanks. Regarding the cage, its dimensions affect the fish and water quality parameters [30]. The net material (generally synthetic materials such as nylon or polyethene) and the net support are extremely important due to their close relation with fish escapes, predation, and biofouling control [31; 32; 33]. Aerial protection, also called bird nets, is an important factor in preventing predation [34]. In the case of cages, the used ships are also an important factor in terms of the produced sound and its effect [35; 36].  
On the other hand, the aspects relative to the feeding greatly impact the fish's performance. Two issues can be identified, the feed and the feeding procedure. The feed quality, with different additives and probiotics [37; 38; 39], and the adjustment of the feed dose [40; 41], have a huge impact on fish performance. The feeding procedure includes the feed schedule [42; 43], the number of feed shoots per day, feeds per day or feeding frequency [44], and feeder characteristics. Considering that feed supposes 70% of the costs of fish farm facilities [45], it is not strange that several efforts have been made to manage the feed in aquaculture. The amount of feed is estimated by existing models based on fish  
Figure 1: Included Domains in aquaculture. Different Domains that affect fish performance and well-being that are considered in this paper. The following subsections analyze each one of the Domains detailing the most important factors for the Control of Aquaculture.  
size and fish population. Sensors can be used to estimate these variables or production models based on fish growth under given conditions are able to provide these data in the absence of sensors. Feeding rate and feed discharge frequency will be adapted to the fish species and size, and to the feeder equipment and facilities. Figure 2 summarizes all the production factors identified in this paper for the Smart Low-Cost Control Systems for Aquaculture Facilities.
# An IoT System for Remote Monitoring of Patients at Home
## 5 Protocol Conversion in the IoT System  
### Protocol Conversion Modules  
As mentioned earlier, in this study protocol conversion is performed between the ISO/IEEE 11073 protocol and the oneM2M protocol. Most PHDs use ISO/IEEE 11073 protocol for their communication  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**QTable** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
**1** & & & & & & & & & \\... & & & & & & & & & \\ **5** & 5.11 & 0.23 & 4.67 & 9.06 & 9.26 & 6.25 & 2.50 & 2.32 & 9.46 & 6.86 \\... & & & & & & & & & \\ **10** & & & & & & & & & \\ \hline \hline
**Message Queues** & **Events** & **1** & **2** & **3** & **...** & & & & \\ \hline
**UrgentQ** & & & & M17/5.0 & M01/4.9 & M02/3.81 &... &... &... &... &... \\
**NonUrgentQ[1]** & & & & & & & & & \\... & & & & & & & & & \\ \hline
**NonUrgentQ[9]** & Before the arrival of M18 & M09/2.0 &... &... &... &... &... &... &... \\  & After the arrival of M18 & M18/3.0 & M09/2.9 &... &... &... &... &... &... \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contents of QTable and message queues at unit time 5.  
because the protocol is a standard communication protocol for PHDs. In addition, most IoT servers and IoT gateways support oneM2M communication protocol for their communication because the protocol is a standard communication protocol for IoT environments. Therefore, protocol conversion between the ISO/IEEE 11073 protocol and the oneM2M protocol is required for interoperability.  
Figure 4 shows the program modules for this study that were newly added to the original oneM2M system for the protocol conversion process. Protocol conversion between the ISO/IEEE 11073 protocol and the oneM2M protocol is performed in gateways (MN-CSEs) in the system. The protocol conversion module consists of three classes programmed in C# under a Windows 7 environment.  
* PHDMessageManager class: Controls the entire flow of the protocol conversion process. It also receives incoming messages to the protocol conversion module and delivers the messages to the PHDMessageHandler.
* PHDMessageHandler class: Performs protocol conversion and sends its results to the ResourceTreeManager, the responsibility of which is to build resource trees for the gateway.  
Figure 4: Program modules for the protocol conversion process.  
* PHDMessageTemplate class: Handles the templates of various ISO/IEEE 11073 messages. With the templates, the related messages can be built quickly and protocol conversion time can be shortened.
# Fast and Effective Tag Searching for Multi-Group RFID Systems
## 3 Preliminary
### System Model  
Normally, a multi-group RFID system [14; 16] is composed of a back-end server, several readers, and lots of tags, which are divided into multiple groups in the interrogation region. Because readers are connected to the back-end server, we consider the readers and the back-end server as a whole and refer to it generically as the reader, which has great computing power and storage capacity. For the case of multiple readers, we can employ the existing reader scheduling algorithms to schedule readers without conflicts. Therefore, in our paper, we consider the case of a single reader.  
The communication between the reader and tags complies with Class 1 Generation 2 (C1G2) standard and adopts listen-before-talk communication mode [21]: firstly, the reader initiates communication by broadcasting query command and related parameters, including the frame length \(f\) and random seed \(R\), and then tags perform the hash function \(\left(H(ID,R)\mod f\right)\) to select a time slot and respond in the slot, where the \(ID\) represents a 96-bit unique identification for each tag. Because of the randomness of slot selection, we usually divide the time slots into empty slots, singleton slots and collision slots, according to the number of tags responding in a slot. Only in a singleton slot, the tag can send the message to the reader successfully. There are usually three types of messages sent by tags: 1-bit short response, 16-bit long response, and 96-bit tag ID. We denote the time duration for transmitting a 96-bit tag ID as \(t_{id}\) and the time duration for transmitting a 1-bit short response as \(t_{s}\).  
In practice, products are often located together with the same category in a warehouse. For the convenience of management, the tag ID is required to express the group information, and it contains two components: the group ID denoted by \(ID_{s}\) and the member ID denoted by \(ID_{m}\). The group ID indicates the group to which the tag belongs, and the member ID is the unique identification sequence of the tag in a group. Tags with the same group ID share the same group information, which may be the category, the brand, or the manufacturer of the tagged products [22]. The group information is deterministic and preloaded into tags to wait for query by readers after tags are put into service. The length of the group ID can be adjusted appropriately according to the practical requirements. In this paper, we assume that the category ID has 16 bits.