esa-sceva/satcom-chunk-collection huggingface dataset:
# Folding Every Point on a Polygon Boundary to a Point
## 3 The Algorithm
Additionally, we know that \(a_{2}\cup a_{3}\) intersects \(p_{1}\) at only \(q_{i1}\in a_{1}^{\ell}\), implying that \(p_{i}\) does not intersect \(a_{1}^{r}\cup a_{1}^{c}\). We can conclude that \(p_{i}\) does not intersect with \(R_{i-1}\) because \(R_{i-1}\) lies between the unbounded curves \(a_{k}^{c}\cup a_{k}^{\ell}\) and \(a_{1}^{r}\cup a_{1}^{c}\). We also have a simple contraposition. **Corollary 1**. : _If \(R_{i-1}\
ot\subseteq H(p_{i})\), then \(q_{k1}\
ot\in H(p_{i})\)._  
Let \(\hat{R}=\bigcap_{j=1}^{i}H(p_{j})\) be the correctly updated solution, we would like to show that \(R_{i}\) constructed above equals \(\hat{R}\). We remark that the \(i\)-th parabola \(p_{i}\) corresponds to edge \(e_{i}\) that comes counterclockwise after all other edges that contribute to \(R_{i-1}\). **Lemma 5**. : _If \(R_{i-1}\
ot\subseteq H(p_{i})\), the arcs on \(R_{i-1}\)'s boundary which do not belong to \(\hat{R}\) form a consecutive sequence:_  
\[R_{i}=\bigcap_{j=1}^{i}H(p_{j}),\]1  
Proof. : Lemma 3 ensures that if \(p_{i}\) intersects the boundary of \(R_{i-1}\), \(p_{i}\) touches at most one arc of \(\hat{R}\), the result of the intersection of \(H(p_{i})\) and \(R_{i-1}\). This implies that the arcs of \(R_{i-1}\) do not belong to form a (circular) consecutive sequence. To see this, assume otherwise and note that in that case, \(p_{i}\) would touch more than one arc of \(\hat{R}\). Our procedure finds the consecutive sequence starting at \(q_{k1}\), the intersection of \(p_{k}\) and \(p_{1}\), then iterates through other consecutive points. Thus, the procedure is correct if the starting point is correct, i.e., we start at some intersection point outside \(\hat{R}\). This is indeed the case because Corollary 1 guarantees that when \(R_{i-1}\
ot\subseteq H(p_{i})\), \(q_{k1}\) is outside \(\hat{R}\). We conclude with our main correctness theorem. **Theorem 1**. : _Our updating procedure is correct, i.e., \(R_{i+1}=\hat{R}\), and the algorithm computes the safe region in linear time._  
Proof. : Regarding the updating procedure, we deal with three possible cases. Lemma 4 ensures that our condition for Case 1 is correct. In other cases, Lemma 5 shows that the procedure for deleting arcs is correct. By induction on \(n\), the algorithm thus produces the required safe region. To analyze the running time, we first note that, except the two inner while loops, for each \(i\), the algorithm runs in \(O(1)\) time. To account for the running time of the inner while loops, observe that each iteration of the loop removes one parabola from the list. Since at most \(n\) parabolas are inserted in the list, the deletion can take place at most \(n\) times, implying the total running time of \(O(n)\) for the loops.
## 3 Experimental Facility
A safety valve is also installed upstream the test sample, in order to limit the maximum feeding pressure to the plenum. During the test rig operation, firstly, the shutter valve located upstream of the inlet plenum (see Figure 4) was regulated in order to set the desired Reynolds number (i.e., the mass flow rate across the seal). Then, the discharge pressure is controlled by means of the a ball valve (see \"regulation valve\" in Figure 4). A steady-state condition was reached after a time of 30 s and data were acquired for the next 10 s.  
Figure 3: Assembly of a test sample representative of the tested labyrinth seal. The pressures and the air temperatures are measured downstream of each tooth of the test articles in order to retrieve the pressure distribution across the seal and to evaluate the contribution of each tooth to the total pressure drop. Blind holes are also employed to measure the metal temperature in the proximity of the flow path. In addition, both the inlet plenum and the discharge plenum are equipped with pressure taps in order to check the global pressure ratio across the seal. All the temperatures are measured by T-type thermocouples (\(\pm\)0.5 K accuracy), while static pressures are retrieved by means of a ScaniValve DSA3217 (\(\pm\)52 Pa accuracy), with temperature-compensated piezo-resistive relative pressure sensors. The experimental campaign consisted of five test series with different values of clearance. For each test series, a double-regulation strategy was adopted. First of all, in order to provide a proper flow check curve around the nominal operating point of each seal, the conditions were changed by keeping the discharge pressure unaltered and varying the \(PR\). In this way, a variation of both the Reynolds number and the pressure ratio occurred. Then, in order to evaluate the separated effect of the two parameters, a second regulation strategy was adopted by keeping the mass flow rate (i.e., the Reynolds number) unaltered to fixed values, and by varying the \(PR\) together with the rig-pressure level. It was therefore possible to achieve flow check curves with fixed Reynolds number (ranging between 60% and 140% of the nominal value) and varying PR. The whole test matrix is reported in Table 2.  
Figure 4: P&ID scheme of the test facility. Figure 5: Sketch of the stepped test article installed inside the test rig.
## 6 Results  
Section 5 describes the three variants of the EOICHD protocol. This section compares the EOICHD variants and the legacy routing protocols--LEACH and LEACH-C. Several comparative parameters are used to understand the EOICHD variants' efficiency, and briefly, the comparative parameters are described as follows.  
* _Energy consumption of the network._ From this parameter, the entire network's energy depletion can be observed.
* _Number of alive nodes_: The number of operational nodes, i.e., the nodes that are not dead, is measured using the parameter number of alive nodes.
* _Data received by BS_: The data sensed by nodes will be transmitted to BS as packets. BS keeps track of the number of data packets received by all the nodes over a period of time. A larger number of data indicates better performance of the routing protocol.
* _Stability period_: Based on the number of dead nodes, the stability of the network can be verified. In this context, the time instance at which the first node that becomes dead (FND), half of the nodes of the network becoming non-operational (HND), and the last node dead (LND) will help in understanding network lifetime.  
All the simulations were conducted using Network Simulator (NS) version 2.34 [29], and simulation parameters are mentioned in Table 2. We considered 100 nodes randomly dispersed over \(100\times 100\) m\({}^{2}\) simulation area. Figure 6 shows the location of all nodes. Tiny circles in Figure 6 represent nodes of the network. The simulations were carried out over two scenarios, as mentioned below.  
* **Scenario #1**: Given the simulation area \(100\times 100\) m\({}^{2}\), the position of the base station is located at (50, 50) of simulation area (the base station is at the center of simulation area).
* **Scenario #2**: Given the simulation area \(100\times 100\) m\({}^{2}\), the position of the base station is located at (50, 175) of simulation area (the base station is located out of the simulation area).
## 3 Results
### Nanoparticles for Pancreatic Cancer Treatment
A study carried out by [PERSON] et al. (2019) employed PEGylated liposomes to negatively regulate Bcl-2 expression. Cell viability was reduced by 35%, and the cell invasion capacity was reduced by 15% in comparison with the treatment with paclitaxel alone. Tumor volume barely increased, with a reduction difference of 75%, compared to the treatment without siRNA, paralleled by increased expression of proapoptotic caspase-3 [60]. On the other hand, [PERSON] et al. (2023), employed a combination of 5-FU and oxaliplatin encapsulated in an amphiphilic polymer (PMBOP). It was coated with a mixture of chondro sulphate (natural ligand for CD44) and PEG (polyethylene glycol) that co-delivers an siRNA against Xkr8, which is associated with tumor immunosuppression. This strategy achieved a 17 and 20% increase in caspase-3 and annexin V levels with the combined treatment compared to the use of the free drugs, respectively. Also, the study obtained good results in vivo, with a 15% extra inhibition of tumoral growth with a 15% decrease in tumor volume compared to the use of siRNA alone [37]. Finally, it is noteworthy that a study that used a TGF-\(\beta\) receptor inhibitor, called LY2157299, encapsulated in a nanoplatform with a hydrophobic core, and a surface adsorbing siPD-L1 that acts by increasing CD8 T-cell infiltration. This combined NP produced an increased cytotoxicity (26%) compared to the use of a free inhibitor. Furthermore, the tumor volume in vivo decreased by \(\approx\)73% with the synergic full treatment compared to the chemotherapeutic alone and had a 90% reduction in tumor weight between the synergic therapy and LY2157299 alone [61]. In summary, in pancreatic cancer, we highlight the use of conventional chemotherapeutic agents, such as oxaliplatin or gemcitabine, or novel therapies, such as TGF-\(\beta\) receptor inhibitors. These therapies were combined with gene silencing of important targets such as PD-L1 or Xkr8, increasing the chemosensitivity of these cells to drug treatments administered in conjunction with siRNA therapy (Table 3). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Nanoformulation** & **Chemotherapeutic** & **siRNA** & **Target** & **Targeting Ability** & **In Vitro Results** & **In Vivo Results** & **Ref.** \\
**(Loading Method)** & **Agent** & & & & & \\ \hline PCL-PEG tri-layer & & & & & Reduced cell migration & \\ micelles (solvent evaporation method) & & & & structure & and increased apoptotic & \\  & & & & (passive) & rate compared to chemotherapy alone. & \\ \hline \end{tabular}
\end{table} 
Table 3: Nanoparticle-siRNA-drug strategies developed for pancreatic cancer treatment.
# A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G
## 4 Framework Proposal
### UPF Placement Stages
#### 4.2.1 Optimal UPF Placement
Thus, the distinction of the objective function in terms of mobility requirements simplifies the problem formulation considerably when their effects on UPF relocations can be diminished. The constraints of the system are described from (3) to (14). Expression (3) ensures that only a main or backup UPF is placed at a specific candidate location, thus guaranteeing only one UPF type for a given service category. Thus, in case of a failure in the infrastructure hosting a main UPF, its backup UPF can be instantiated in a safe location. Moreover, constraints (4) and (5) indicate that an access node must be assigned only to a candidate location where there is a main or backup UPF in place. The constraint (6) guarantees that the demands of an access node are served by only a main UPF at a given time. In addition, restriction (7) forces the assignment of the access nodes to at least the minimum number of backup UPFs (\(K_{u}-1\)) necessary to satisfy the service reliability requirements. Thus, each access node will be assigned to a total of \(K_{u}\) UPFs (i.e., one main UPF and \(K_{u}-1\) backup UPs). Additionally, Inequalities (8) and (9) ensure that the capacity of the main and backup UPFs is not exceeded by the underlying service demand. The factor \(\alpha\) defines the maximum capacity to be occupied in the main UPFs by the access nodes to avoid slowing their performance. The Expression (10) ensures that the access nodes cannot be assigned to a main or backup UPF if the latency requirement (\(L_{req}\)) is not satisfied. The \(L_{req}\) between (R)AN nodes and UPFs is determined by taking into account their processing time (\(L_{proc}\)) and the service latency requirement (\(L_{serv}\)), \(L_{req}=L_{serv}-L_{proc}\). The effects of packet transmission and queuing delays in the overall latency are considered negligible. Moreover, constraint (11) restricts the assignment of an access node to a specific main UPF if this UPF has been placed at its location. Otherwise, it can be assigned to a UPF in any other location (12). Additionally, constraint (13) is an extra restriction to take into consideration for UPF placement with mobility requirements, Expression (2a). It expresses the relationship between two access nodes and their assignment to a main UPF. Since the Expression (13) is non-linear, it must be replaced by the following inequalities: \(a_{ijc}\leq p_{ic}+p_{jc}\), \(a_{ijc}\geq p_{ic}-p_{jc}\), \(a_{ijc}\geq p_{jc}-p_{ic}\) and \(a_{ijc}\leq 2-p_{ic}-p_{jc}\). Constraint (14) indicates that \(x_{c},y_{c},p_{rc},b_{rc}\) and \(a_{ijc}\) are binary variables.
## 2 The Transmitting and Receiving System of the Composite Signal  
In this section, we will give a signal model of the composite signal, and introduce the structure of the transmitting and receiving system.
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 2 Data and Methods
### Observed Data  
We selected BDS-3 observations from 25 stations in the MGEX network over 30 consecutive days from 1 July 2022 (DOY 182) to 30 July 2022 (DOY 211); the data sample interval was 30 s. All 25 stations could receive BDS-3 B1I, B1C, B2a, B2b and B3I signals. The distribution of stations is shown in Figure 3.
# Development of a Novel De-NOX Technology for the Aftertreatment of Ship Exhaust Gases
## 4 Conclusions  
The conclusions that derive from the present work can be considered of significant industrial importance, since the three different technologies used in this research are combined and implemented in a pilot unit for the first time on board a cruise ship. In particular:  
* The Sea Water Absorption technology was found to be capable of practically eliminating sulphur dioxide from the exhaust gas stream. SO\({}_{2}\) was almost completely removed from the ships exhaust stream (more than 99% conversion), which is in full accordance with the new IMO MARPOL regulations [2].  
Figure 6: Steady-State concentrations of NO\({}_{\chi}\), SO\({}_{2}\) and PM before and after the operation of the Integrated Prototype Unit (SWA unit, Oxidative Catalyst and De-NO\({}_{\chi}\) unit).  
Figure 7: Final conversion (%) values of examined pollutants (NO\({}_{\chi}\), CO, PM and SO\({}_{2}\)), as achieved by the operation of the present Integrated Prototype Unit.  
* Moreover, a significant reduction in PM was also achieved with the use of an oxidizing catalytic converter. In the framework of the present study, it has been found that more than 70% of the PM in the exhaust gas stream of the ship was successfully removed with the use a commercial oxidation catalyst.
* Most importantly, within the present work, the H\({}_{2}\)-SCR de-NO\({}_{\text{x}}\) technology was examined, for the first time ever, in a real application, as a candidate technology for the reduction of NO\({}_{\text{x}}\) present in ships exhaust gasses. It was proven that the Selective Catalytic Reduction of NO\({}_{\text{x}}\) with the use of H\({}_{2}\) as a reducing agent in combination with a suitable catalyst, can be considered an effective NO\({}_{\text{x}}\)-pollution control technology for ships. In particular, it was found that more than 80% of NO\({}_{\text{x}}\) can be successfully reduced to N\({}_{2}\) with the use of the novel Pt/MgO-CeO\({}_{2}\) supported catalyst.  
The results obtained by the present work are quite promising and can lead to a real, practical application for the complete removal of major pollutants present in ships exhaust gaseous streams, i.e., NO\({}_{\text{x}}\), SO\({}_{2}\) and PM.  
[PERSON] proposed the idea behind the research and had the main responsibility for the preparation of the manuscript; [PERSON] supervised the project and the experiments conducted; [PERSON] had the responsibility for the construction of the pilot unit and the performance of the pilot experiments; and [PERSON] contributed to the design of the pilot unit and the interpretation of the results obtained. All authors have read and agreed to the published version of the manuscript.  
The work reported herein was conducted within the research project \"Development of a Novel De-NOx Technology for an Integrated Aftertreatment of Ship Engine Flue Gas Stream\" that was funded by the Cyprus Research Promotion Foundation (NEMO--TEXNO/MHXAN/0308(BIE)01).
## 2 Materials and Methods
### The proposed Motion Analysis System
#### 2.1.1 Data Collection  
This module enables the analysis of the frames from the camera(s) to detect and track people presented in the frame. It uses models and techniques of deep learning and computer vision.  
The deep learning model used to track the skeleton joints (i.e., key points) is based on the open-source project Tf-pose-estimation. This algorithm has been implemented using the C++ language and the Tensorflow framework. Tf-pose-estimation provides several models trained on many samples: CMU, mobilenet_thin, mobilenet_v2_thin, and mobilenet_v2_small. Several tests were done, and the CMU model was chosen as the optimal model for this project, looking for a compromise between accuracy and image processing time. It allows the identification of a total of 18 points of the body (Figure 2). When a video is analyzed, for any person detected in each processed frame, the system associates a skeleton and returns the following three data, for each single landmark:  
* x: horizontal coordinate.
* y: vertical coordinate.
* c: confidence index in the range [0; 1].  
For the subsequent parameter calculation, the system considers only the landmarks with a confidence index higher than 0.6.  
To ensure univocal identification of the subjects, as long as they remain and move in the visual field of the camera(s), the system also associates a proper index to each person. A proper algorithm has been implemented to distinguish the key points belonging to different subjects that could eventually overlap each other when passing in front of the camera. It considers a spatial neighborhood for each detected subject through the key points detection model: if the subject maintains its position within that neighborhood in the subsequent frame, the identifier associated with it at the first recognition will remain the same. Collected data is then saved in a high-performance in-memory datastore (Redis), acting as a communication queue between the Data Collection and the Parameter Calculation modules.
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 3 Propagation Modeling
### Artificial Neural Network  
The feedforward neural network model consists of an input layer, two hidden layers, and an output layer. The five inputs of the network are:  
* Total number of walls blocking the LoS (\(\sum k_{j}\))
* Total attenuation caused by the blocking walls (\(\sum\alpha_{i}k_{j}\))
* Attenuation caused by distance and loss exponents (\(n_{A231}\times L_{FS}(d)\))
* Set of (\(x_{i},y_{i}\)) coordinates to specify the measurement location inside the building  
The first and second hidden layers consist of seven and three neurons, respectively. The number of layers and the size of each layer were heuristically found to avoid overfitting, while generalizing the problem. The only output of the network is the error between the optimized or best estimate of the adjusted COST231 model (\(F_{A231}\)) and the measured RSSI. The transfer functions are linear for the first hidden layer, and hyperbolic tangent sigmoid [34] for the second and the output layer. Also, the Levenberg-Marquardt backpropagation algorithm [34] is used for training. An overall schematic of this implemented network in MATLAB R2016b is depicted in Figure 3, where \"w\" and \"b\" are indicating the weights and biases of each layer of neurons, respectively.
# Ribosomal Hibernation-Associated Factors in _Escherichia coli_
## 3 Preparation of Ribosomal Hibernation  
### Synthesis of (p)ppGpp on the Ribosome by RelA  
Expression of the _rmf_ gene is known to be controlled by guanosine tetraphosphate and pentaphospheate, collectively known as (p)ppGpp [35]. Depletion of essential nutrients in the environment is among the most serious threats to bacteria. A central component of adaptation to this stress is the stringent response [36] driven by (p)ppGpp, which plays numerous roles in regulating cell growth rates and adapting to the environment [37]. (p)ppGpp regulates transcriptional activity and decreases ribosome biosynthesis for environmental adaptation by interacting with RNA polymerase [38; 39]. Intracellular (p)ppGpp levels are regulated by enzymes belonging to the RelA/SpoT homolog family [40]. SpoT is a bifunctional enzyme with strong hydrolytic activity and weak (p)ppGpp synthesis activity (<8> in Figure 2) [41], whereas RelA is a ribosomal factor with (p)ppGpp synthesis activity (see Table 1) [42]. When the supply of amino acids is limited by nutrient starvation, deacylated tRNA binds to the A site of the 70S ribosome (<6> in Figure 2). RelA is activated by binding to deacylated tRNA-bound 70S ribosomes [43]. Activated RelA transfers the pyrophosphoryl group from ATP to GTP or GDP on the ribosome, synthesizing pppGpp or ppGpp, respectively (<7> in Figure 2). To understand how RelA synthesizes (p)ppGpp on the deacylated tRNA-bound 70S ribosome, the high-resolution structure of the entire 70S RelA deacyl-tRNA complex was analyzed [44; 45; 46]. The structural analysis results showed that the interaction between RelA and the ribosome caused conformational changes in both the 30S and 50S subunits during (p)ppGpp synthesis. (p)ppGpp is involved in promoting transcription of the _rmf_ gene and strongly affects 100S ribosome formation (<9> in Figure 2) [35]. This sequence of events (amino acid starvation \(\rightarrow\) synthesis of (p)ppGpp by RelA \(\rightarrow\) expression of RMF by (p)ppGpp \(\rightarrow\) formation of 100S ribosome by RMF \(\rightarrow\) reduction of translational activity by 100S ribosome) is congruent with the response to starvation stress.
## 2 System Architecture and Design Principles
### Optimization of OOK Demodulation Design
This process effectively filters out outliers, such as spiky noise, thereby smoothing the signal and improving its quality. To ensure accurate signal reconstruction, an adaptive joint decision threshold is employed for signal judgment. This threshold is initially configured according to the channel conditions and dynamically adjusted in real time using a 255-bit shift register to compute the mean value. The calculated average is then weighted and integrated with the initial threshold, ensuring reliable evaluation even when the initial sample size is limited. The judged signal is subsequently processed by BEDF. The implementation of the BEDF involves the detection of jumps in the input signal \"_Judge_data_\", which is performed by a jump detection unit to identify the rising and falling edges of the signal. For each detected transition (rising edge \(t_{1}\) and falling edge \(t_{2}\)), the time interval \(\Delta t\) between them is measured by count, and this time interval \(\Delta t\) is compared with a preset threshold value \(T\) to determine the validity of the signal change. If \(\Delta t\) is less than \(T\), the signal change may be caused by noise; if \(\Delta t\) is greater than or equal to \(T\), the signal change is considered valid. Figure 5: Framework of AGC design. Based on this judgment, an output signal is controlled by a switching circuit to ensure that the signal is allowed to pass only when \(\Delta t\geq T\). This filters out burrs that do not satisfy the bit duration requirement and ensures the accuracy and reliability of the signal. Finally, the demodulated signal is output under the control of the \"_Bit_sync_\" clock generated by the Bitsync module.