esa-sceva/satcom-chunk-collection huggingface dataset:
# Method for Handling Massive IoT Traffic in 5G Networks
## 5 Data Aggregation and Scheme for Multiplexing  
In this work, the design of a fixed RN is presented for 5G networks. IoT data traffic from devices located near RN is aggregated at the RN. In this paper, it is supposed that the RN antennas for the access link (interface with devices) and the backhaul link (interface with BS) are well separated and that self-interference can be totally avoided. For example, consider the case of deploying the access antenna inside some building while the backhaul antenna is deployed outdoors. In such cases, the inband operation of RN can be ensured without any time division mechanism. At the MAC layer of the BS, a channel and service aware scheduler [31] is implemented for scheduling the RN as well as mobile user devices with regular data traffic. The scheduler consists of time and frequency domain uplink scheduling schemes.  
An aggregation buffer is used at the RN for aggregating IoT data having a size that matches the size of instantaneously achievable Transport Block Size (TBS) offered by the air interface. The maximum buffer size denoted by \(n_{max}\) corresponds to instantaneously available TBS, which depends on channel conditions. However, the multiplexed data forming a large packet can have a maximum size of \(n_{max}-overhead\) (from lower layers). The overhead of LTE-A lower layers is assumed to be 352 bits [32]. The multiplexed large packet is transmitted to the BS via the backhaul link. This approach can bring significant improvement in the radio resource utilization, as discussed later. Conversely, this approach also can introduce problems due to constraints related to latency requirements associated with high priority data traffic, for example the delay sensitive emergency warnings. The small packets from IoT devices must wait until the buffer size reaches \(n_{max}-overhead\). In case of a highly loaded traffic scenarios, this issue may not occur, as the arrival rate is high and filling the buffer with packets would not take long. The issue arises in scenarios having low traffic load, where large interarrival times can cause longer waiting times greater delays in stuffing the buffer. As a result, the performance of delay sensitive IoT applications is compromised. This issue is dealt with by deploying a timer in the scheme. The timer is set to a certain time \(T_{max}\), which is the maximum waiting time of the first packet arriving since the empty buffer (Figure 3). Thus, the buffer multiplexes the aggregated packets until a duration \(T_{max}\) of the first arrival. Once waiting time of a packet reaches the timer expiry duration \(T_{max}\), the process of packet multiplexing is triggered, and the large multiplexed packet is sent to the BS. The original small packets from the IoT devices are then sent from the BS to the core network.
## 1 Introduction  
In recent decades, materials engineers and scientists have spent considerable effort and time in conducting research in biodegradable materials. Some may argue that the field of biomaterials possibly began 4 millennia ago when the Egyptians and Romans utilized gold and iron for applications in dental issues and performed toe replacement surgeries with wood without considering the problem of corrosion. The significance of this field has gained prominence after the world wars as the demand for biomaterials was rising [1]. An estimated 8 million fracture-related injuries are reported in the United States annually and a rising cost of US$116 billion has been forecast for the bio-implants market by 2020 [2]. There is no doubt that the field of biomaterials plays a significant role in increasing the mobility and lifestyle of a significant portion of the world's population.  
Study on biodegradable materials can generally be divided into two main categories i.e., (1) biodegradable metal implants and (2) biodegradable non-metal implants. The example of the biodegradable metal implant is discussed in this paper whereas an example of biodegradable non-metal implants (e.g., biocearamic and polymers) has been discussed elsewhere [3]. The concept of a biodegradable metal implant is that it should be able to degrade in an in-vivo (human/animal body fluid) setting at an appropriate rate without any toxic effects to the host body. In addition, the metal implant must be able to uphold its durability while aiding the patient's recovery. Once the healing process is completed, the implant should then ideally dissolve as soon as new tissues are developed [4].  
There are currently three well-studied materials utilized in biodegradable implants: magnesium, iron, and zinc. These biodegradable implants have been utilized in useful medical applications such as coronary stents, bone screws, pins, plates, and such (Figure 1) due to their ability to integrate with human cells and tissues [5].  
Although the biodegradable non-metal implant has shown to be more appropriate in practice however, clinical trials are needed before this material can be applied for human use. In contrast with the non-metal option, the biodegradable metal implant is more applicable in a shorter period of time for human applications. Meanwhile, the real-time or rapid corrosion monitoring has remained unsolved. The current method of corrosion monitoring involves performing a subsequent surgical procedure to observe whether there are any chemical reactions between human tissue and the biodegradable metal implant. This risky and potentially fatal issue has motivated the authors to write a review paper regarding existing real-time corrosion monitoring methods. In addition, this is a preliminary study of the development of real-time corrosion monitoring for biodegradable metal implants.
## 2 Methods and Data  
### Monitored Households and Mechanical Ventilation System  
The climate zone distributions of China [23] and the location and quantity distribution of the monitored households are shown in Figure 1. These households were in the cold region of northern China. They were in Beijing, Tianjin and six cities of Hebei province (Shijiazhuang, Baoding, Langfang, Cangzhou, Tangshan and Handan). All these households were using a mechanical ventilation system of the same brand. There were two types of systems, either split or centralized, with schematics provided in Figure 2 and relevant parameters listed in Table 1.  
The split system is a supply ventilation system with one air inlet [9]. Each room uses it independently. In this study, most split systems were installed in bedrooms. It uses a fan to force outdoor fresh air into the room through the filter element, while the indoor exhausted air leaks out through the cracks and holes of the building. The centralized system is an energy recovery ventilation system with ducts. It can generally be used in the entire house. The fresh air is introduced and sent to each room through the air inlet duct, and the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Split** & **Centralized** \\ \hline Air flow (m\({}^{3}\)/h) & 60–130 & 100–500 \\ Usage area (m\({}^{2}\)) & 30 & 120–180 \\ Rated power (W) & 22 & 165 \\ Noise (dB) & 32–49 & 29–56 \\ Product size (mm) & 840 \(\times\) 300 \(\times\) 185 & 1541 \(\times\) 600 \(\times\) 405 \\ Net weight (kg) & 13.8 & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of mechanical ventilation system.  
Figure 1: The climate zone distribution of China and location and quantity distribution of the monitored households. Beijing, Tianjin and Hebei were the cities and province investigated. SS is the split system and CS is the centralized system; C: cold region; SC: severe cold region; M: mild region; HSCW: hot summer and cold winter region; HSWW: hot summer and warm winter region.  
Figure 2: Schematics of the (**a**) split and (**b**) centralized mechanical ventilation systems in buildings.  
exhausted air is discharged outdoors through the exhaust duct. Meanwhile, the operation of the heat exchange unit makes the occupant feel more thermally comfortable and saves heating loads in the winter season and cooling loads in the summer season.
# Dynamic Controllability of Processes without Surprises
## 4 Sudden Termination and Sudden Start Problems, and Semi-Dynamic Controllability
### Semi-Dynamic Controllability  
The constellation of constraints leading to an STP or to an SSP does not violate dynamic controllability of processes. Indeed, we have shown that there exist dynamically controllable processes exhibiting an \(STP\) or in an \(SSP\). Therefore, the notion of dynamic controllability turns out to be not sufficiently adequate: it allows semi-contingent activities to start without knowing yet when they must end, or it allows activities to start without prior notice. In [11], we defined semi-dynamic controllability as a specialization of dynamic controllability that recognizes the need to know the required end time for a semi-contingent activity at its start time. Here, we extend that definition, including the requirement that no activity may be forced to suddenly start, if it demands prior notice of start.  
**Definition 6** (Semi-Dynamic Controllability): _Let \(P\) be a process. \(P\) is semi-dynamically controllable (sdc) iff \(P\) is dynamically controllable (dc), and \(\
exists S,C\in P.N\): \(STP_{S,C}\) or \(SSP_{S,C}\)._  
The notion of semi-dynamic controllability introduced here is stricter than the notion of dynamic controllability, since it requires (1) that a process is dynamically controllable, (2) that no semi-contingent activity is involved in a sudden termination, (3) that no activity is involved in a sudden start pattern.
## 2 Deformation Allowance of Loess Tunnel and Statistical Monitoring Samples
### Basic Information of Monitoring Samples
Compared to other weak surrounding rocks, loess stands out due to its distinctive engineering features, including vertical joints, macropores, and complex structure. This results in rapid initial-stage deformation characterized by significant, asymmetric, and spatially uneven changes in deformation speed, magnitude, and duration. The low bearing capacity of loess in the rock foundation leads to poor tunnel stability at the junctions of the arch foot, inverted arch, and side walls. If support is not applied in a timely manner or if the support stiffness is insufficient, the support structure may experience excessive settlement, which could ultimately result in tunnel collapse [66; 67; 68]. The low bearing capacity and weak self-stability of loess tunnel surrounding rock result in significant initial stage deformation rates during excavation. For instance, the Dayoushan and Qindong loess tunnels exhibit high initial deformation rates of 77 mm/d and 40 mm/d, respectively. The loess surrounding rock experiences prolonged deformation, marked by prominent creep characteristics. Analyzing arch crown settlement and horizontal convergence data from the Dayoushan tunnel, a significant deformation increase is observed 7-15 days after tunnel face excavation. Between days 10 and 15, the deformation rate slows down, eventually stabilizing approximately 30 days after excavation. Proper deformation allowance settings can enable the tunnel's surrounding rock to undergo controlled deformation after excavation and support, thus releasing energy and reducing deformation pressure on the post-support structure. Deformation allowance is tied to final settlement, particularly in the case of 148 sections with grade IV and V surrounding rocks where crown settlement  
Figure 4: Distribution of loess soil types in China. exceeds surrounding convergence. Therefore, the deformation allowance setting should prioritize arch crown settlement.
# BEST--Building Energy-Saving Tool for Sustainable Residential Buildings
## 5 Discussion  
### Strengths and Practical Applications  
The result described enables the achievement of significant benefits for the national electrical system and its users, such as:  
* Reduction in energy consumption: The support provided for optimizing energy efficiency interventions in national residential buildings leads to a decrease in energy demand at the national level, contributing to the overall improvement of the electrical system's efficiency.
* Improvement of system stability: The widespread implementation of energy efficiency interventions positively impacts the stability of the national electrical system by mitigating energy demand peaks and promoting a more rational and sustainable use of available resources.
* Economic benefits for users: The cost-benefit analysis provided by the tool ensures the identification of targeted and optimized solutions, capable of maximizing the cost-benefit ratio. This leads to a significant reduction in energy costs for citizens and families, improving the economic sustainability of the interventions.
* Promotion of environmental sustainability: The large-scale implementation of energy efficiency interventions contributes to reducing CO\({}_{2}\) emissions, supporting the achievement of sustainability goals set at both European and national levels. This approach produces positive effects on both the environment and the quality of life within communities.
* Support for the spread of innovative technologies: The tool promotes prefabricated and optimized technological solutions, encouraging the growth of the advanced and sustainable technology market. This dynamic stimulates innovation in the construction sector and strengthens the competitiveness of the businesses involved.
* Strategic planning tool for institutions: The tool enables the creation of priority rankings for interventions, providing a valuable resource for energy and urban planning at the territorial level. This allows institutions to plan targeted actions, ensuring efficient resource allocation and a positive impact on the management of building stock.  
To ensure broad accessibility and ease of use, a structured web-based application was developed.  
The interface is organized into clearly defined sections that, in a sequential manner, describe the building-plant system based on its key characteristics. Particular emphasis was placed on minimizing the need for technical input from users. Where input is required, data entry is facilitated through guided, multiple-choice menus designed to streamline the process and support non-expert users.
## 3 Results and Discussion
### Adsorption Experiments  
#### 3.2.1 Influence of pH  
The pH of a solution may change the form and chemistry of the target dye ions and the binding sites on the adsorbent. The effect of pH on the removal of IV2R ions by McNNTs was performed in a pH range of 2-10, the results of which are shown in Figure 6. These results show that when the pH of the solution was augmented from 2 to 4, the adsorption of dye increased to reach its maximum for the McNNTs of 92.98% at pH 4; then, it slightly decreased. Therefore, pH 4 was selected as a compromise value. This can be explained by the adsorbent containing several functional groups that are affected by the pH of the solution. Moreover, an acidic solution increases in the percentage of anionic dye removal because of the electrostatic attraction between anionic dye and the positive charge of the surface of the adsorbent [50]. At a higher pH, electrostatic repulsion occurs among dye molecules and the negatively charged surface, therefore reducing the percentage elimination of anionic dyes and the sorption capacity [11]. Additionally, [PERSON] et al. [29] concluded that, at a higher pH, elimination is low compared to the maximum condition. This can be clarified as the binding site not being able to activate under basic conditions.  
Figure 5: Raman spectrum for the MWCNTs.
## 2 Method
### The Design of the Dual Receivers Tracking System  
The dual-receiver tracking system is mainly composed of two vertically distributed receiving coils, as shown in Figure 1. In the figure, there is a PIG with a transmitter (the red part) in a pipeline (the black part), while the gray part outside the pipeline is soil. Furthermore, the dual tracking receivers (the blue part) are above the ground.  
The tracking system works mainly as follows. First, the transmitter carried by the PIG constantly radiates an ELF magnetic field in the pipeline. The ELF magnetic field reaches the ground through the attenuation of the oil and gas, the pipeline wall, the soil, and the  
Figure 1: The architectural overview of the dual receivers tracking system. The red cylinder represents Pipeline inspection gauge (PIG) transmitter, while blue ones are tracking receivers.  
air. Then, the vertically distributed dual tracking receivers detect the magnetic field values at different heights on the \(y\)-axis when PIG passes. Last, the tracking system can use the characteristics of the radiation field to track the PIG.
## 3 Results
### Argon Plasma Induced Apoptosis in Patient-Derived BC and OSCC Cancer Tissues  
We next examined the effects of cold physical plasma on ex vivo-generated punch biopsies of BC and OSCC tissue derived from a total of 40 cancer patients. The question of our study in this part was whether plasma treatment induced cytotoxic responses in cancer tissues investigated 24 h after treatment (Figure 2). After the tissue was sectioned, stained, and imaged, apoptotic cells (TUNEL positive, green) were quantified, and the ratio of green nuclei (apoptotic cells) over the total number of nuclei (DAPI\({}^{+}\) objects) was calculated. Our quantitative analysis revealed a significant increase in apoptosis in the cold physical plasma-treated BC (Figure 3a) and OSCC (Figure 3b) samples, particularly at the borders of the tissues where the cold physical plasma treatment was applied. The results showed that  
Figure 3: Immunofluorescence analysis. (**a**) representative images and quantitative imaging data of ex vivo generated patient-derived tumor tissue samples for stained with DAPI to indicate nuclei, TUNEL to indicate apoptotic cells, CD45 to label leukocytes, CD163 to label M2 macrophages, and FoxP3 for labeling regulatory T-cells in gas plasma-treated and untreated samples; (**b**) DAPI, TUNEL, and CD45 staining in ex vivo gas plasma-treated and untreated OSCC patient-derived tissues. Data are representative or mean from 20 BC and OSCC patient samples, respectively. Statistical analysis was performed using paired _t_-test. Levels of significance were indicated as follows: \(\alpha\) = 0.05 (*) and \(\alpha\) = 0.01 (**). ns = not significant.  
physical plasma treatment led to an on-average three-fold increase in apoptotic cells in BC tissue samples and a two-fold increase in OSCC samples compared to untreated samples. Apart from apoptosis, we also investigated the impact of cold physical plasma treatment on the number of stainable immune cells (CD45-staining), regulatory T cells (Foxp3-staining), and tumor-associated macrophages (TAM, CD163\({}^{+}\)-staining). Using immunofluorescence staining and analysis on tissue slides (Figure 3), we counted the number of these immune cells in both cold physical plasma-treated and untreated samples. The results indicated a significant decrease in the number of TAM in plasma-treated BC tissue samples. However, cold plasma exposure did not cause any significant effects on total leukocyte counts and regulatory T cells in BC tumor samples. Taken together, our findings suggest that cold physical plasma treatment induces apoptosis in cancer cells in vitro and in patient-derived tumor tissue ex vivo. At the same time, genotoxic effects were not observed in vitro. These results may have important implications for the potential use of cold physical plasma as a novel adjuvant treatment approach for cancer.
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## 2 AUV Model
### Tidal Current Dynamics  
In this work, the vehicle is subject to underwater currents that need countering. Considering the rigid-body AUV model of Equation (1), expressed in terms of the vehicle instantaneous velocities vector v, the tidal current is introduced in the model by considering the linear speed components of this disturbance with respect to the body-fixed reference frame \(v_{\rm T}=[u_{T},v_{T},w_{T},p_{T},q_{T},r_{T}]^{T}\). The relative velocity of the vehicle with respect to the tidal current can be computed as:  
\[v_{R}=v-v_{T} \tag{2}\]  
The effect of the tidal current can be introduced in the rigid body dynamics of Equation (1) by replacing \(v\) with \(v_{R}\) so that:  
\[M\dot{v}_{R}+C(v_{R})v_{R}+D(v_{R})v_{R}+g(\eta)=\tau_{A} \tag{3}\]  
**Remark 1**.: _The tidal current \(v_{T}\) is constant in direction and velocity (\(\dot{u}_{T}=\dot{v}_{T}=\dot{w}_{T}=0\ m/s^{2}\)), irrotational (\(p_{T}=q_{T}=r_{T}=0\ m/s\)) and the vertical component of the current is neglectable (\(w_{T}=0\ m/s\))._  
**Remark 2**.: _The tidal current speed \(V_{T}=\sqrt{u_{T}^{2}+v_{T}^{2}}\) is bounded such that \(V_{T}\in[0,\ 0.514]\ m/s\)._  
**Remark 3**.: _The constant tidal current heading orientation is \(\psi_{T}=\psi_{ROV}+\beta\) where \(\psi_{ROV}\) is the docking station heading and \(\beta\in[-0.1745,+0.1745]\ rad\) is constant._
# Monitoring Safron Crops with UAVs
## 4 Results
### Pixel Based Image Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Estimation** & **Accuracy** & **Precision** & **Recall** \\  & **model** & **Accuracy** & **Precision** & **Recall** \\ \hline \multirow{3}{*}{**RF**} & **Animal** & 100\% & 99\% & 68\% \\  & **Weed** & 100\% & 99\% & 73\% \\  & **Saffron flowers** & 100\% & 92\% & 80\% \\ \hline \multirow{3}{*}{**MLP**} & **Animal** & 97\% & 50\% & 37\% \\  & **Weed** & 94\% & 35\% & 52\% \\ \cline{1-1}  & **Saffron** & 94\% & 55\% & 42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for RF and MLP per category. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** \\ \hline
**RF** & 100\% & 97\% & 74\% \\ \hline
**MLP** & 95\% & 47\% & 44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall results of pixel-based image analysis. In addition, Table 5 lists the rate of existence of each type of pixel represented in the image of Field 1 (this field represents a flourished instance of a field; therefore, it is ideal to use it as a benchmark for the level of production in saffron). Figure 8 presents magnified and clearer pictures of the part of the original cultivation with the predicted values of the models. Through these pictures, it is possible to estimate the position of every category in the field and the amount of production. The results in Figure 8 show that the number of saffron flowers is 8% within the field, the weed is at 0.4%, and the mouse trace rate is at 8% based on RF classifier, which was the most accurate model.
# Decision Framework for Predictive Maintenance Method Selection
## 5 Evaluation of the Proposed Framework
### Case 2: Nuclear Reactor  
_Case description:_ This department conducts maintenance on the equipment of a nuclear facility. Due to the strict regulations within this sector, detailed analyses are conducted for critical equipment. Moreover, a high level of redundancy is present to guarantee safety in case of equipment or process failures. Due to the high amount of redundancy, little similar equipment is available. This reduces the applicability of reliability statistics approaches. A high level of knowledge and historical data on the usage and loading of the reactor is available. In process logs, every activity in the reactor is logged. However, since modifications on the reactor have taken place over the years, not all data are equally useful for maintenance analyses.  
_Challenge:_ The company wants to check whether the present predictive maintenance approach is appropriate. Table 4 shows the analysis using the prosed framework.  
_Conclusion:_ For this nuclear reactor case, there is a clear match between the ambition level and the data available. The framework correctly advises a model-based approach, which is also the approach currently followed by the company.