esa-sceva/satcom-chunk-collection huggingface dataset:
# Consequences of Glucose Enriched Diet on Oncologic Patients
### Role of Sugar in Cancer Metabolism  
Due to enhanced activation of various metabolic pathways, cancer cells create a high quantity of reactive oxygen species (ROS) with respect to normal cells [19]. The metabolism of cancer cells is directly tied to ROS homeostasis; they generate ROS detoxifications by utilizing a number of metabolic intermediates and substrates in metabolic pathways, the most famous of which being glycolysis via the Warburg effect and pentose phosphate pathway (PPP) [20]. The Warburg effect preserves the redox equilibrium by being independent of mitochondrial OXPHOS, which produces a massive amount of ROS [21], and PPP, which produces the ROS-detoxifying molecule NADPH via G6 PD and 6-Phosphogluconate dehydrogenase (6 PGDH) [22].  
Oncogenes, such as MYC, Src, and Ras, and transcription factors, for instance, signaling pathways such as phosphoinositide 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR), hypoxia-inducible factor-1 (HIF-1), and tumor suppressors such as p53 are all implicated in the modulation of glucose metabolism in cancer. Ras, Src, and MYC oncogenes improve HIF-1 expression, which raises the production of several glycolytic enzymes, and MYC, KRAS and HIF-1 oncogenes worsen glucose absorption by enhancing GLUT expression. Furthermore, the PI3K/Akt/mTOR pathway increases the synthesis of GLUT and glycolytic enzymes, whereas p53 regulates GLUT and glycolysis via AMP-activated protein kinase (AMPK) and mTOR [23; 24].  
The metabolism of tumors are very specific. Among the many contrasts with healthy cells, possibly the most crucial is that cancer tissues depend exclusively on glucose as a source of carbon and energy. Because of how rapidly cancer cells seek glucose, they can be spotted by positron emission [25].  
Cancer cells have been recognized for decades to demonstrate higher metabolic rate and glucose use rather than cellular respiration. The glycolytic change was considered to be produced by abnormalities in mitochondrial respiration because of mutations in mitochondrial DNA. Yet, it is currently argued that tumor cells require glucose because they make use of it to build metabolites required for proliferation, notably, fatty acids and nucleic acids. Instead of employing the bulk of the glucose to create ATP through respiration, alterations in metabolism enable cells to grow more quickly [26; 27].
## 7 Proposed Extensions to Bio-2 FA-IoD
### Sybil-Free Pseudonym Management for Enhanced Anonymity
#### 7.2.1 Conceptual Integration  
This extension would primarily impact the registration phase and the format of \(ID_{O}\) in the authentication request:  
1. **TA's** role (initial identifier issuance): * During initial registration, the trusted authority (TA) acts as a trusted issuer, providing each legitimate operator (\(U_{O}\)) with a unique, unforgeable \"initial identifier\" (\(u\)). This \(u\) would be akin to a non-transferable e-token dispenser, incorporating cryptographic elements like a Camenisch and Lysyanskaya (CL) signature. This process would involve algorithms like _IKg_, _UKg_, _Obtain_, and _Issue_. * This initial identifier \(u\) is securely provisioned to the operator's device (OD).
2. **OD's** role (pseudonym generation and storage): * The OD, using the initial identifier \(u\), generates a new pseudonym \(p_{u_{O}}^{C}\) for each specific IoD \"service context\" (\(\mathcal{C}\)) (e.g., a drone mission type, a geographical area) it participates in. This involves a 'Sign' algorithm that binds a freshly generated public key (\(pk_{u,\mathcal{C}}\)) to the pseudonym, producing a unique serial number (\(S\)) and a pseudonym certificate (\(\tau\)). * The OD uses \(S\) and \(\tau\) instead of \(ID_{O}\) in the authentication message \(M_{1}\). The new public key \(pk_{u,\mathcal{C}}\) can be used for signing messages originating from the pseudonym.
3. **GCS's** role (pseudonym verification and Sybil detection): * Upon receiving an authentication request with a pseudonym (\(S,\tau\)), the GCS uses a 'Verify' algorithm to check its validity and authenticity against the TA's public key. * The 'Identify' algorithm [29] could be invoked by the GCS (or a central auditing entity) if two pseudonyms from the same service context are observed to be potentially linked, allowing the detection of Sybil attacks (i.e., if an operator attempts to create multiple identities within the same context). This can compute the initial identifier \(u\) of the owner, enabling accountability for misbehavior (e.g., revocation).
# Static and Moving Target Imaging Using Harmonic Radar
## 4 Synthetic Aperture Radar (SAR) Imaging
### SAR Image Processing Approach  
SAR images can be created using data collected in many different configurations. For airborne applications, SAR systems can fly in a straight line to image a large area or they can fly in a circular path to image a desired target [65]. Ground vehicle based SAR systems can be configured in a forward looking orientation [66; 67; 68] or a side looking configuration [69; 70]. Regardless of the configuration and orientation, the basic imaging algorithms remain the same. The SAR data collected here use a back-projection algorithm to form the SAR images [66]. To perform the back-projection algorithm, a coordinate system, image area, and antenna positions need to be defined. The imaging area consists of \(N\) pixels with coordinates \((x_{p}(i),y_{p}(i),z_{p}(i))\) and amplitudes \(P(i)\), where \(1\leq i\leq N\). Data are collected over an aperture consisting of \(K\) positions. The coordinates of the transmit and receive antennas need to be known at each of the \(K\) positions. The location of the receive and the transmit antennas at each aperture position are denoted as \((x_{R}(k),y_{R}(k),z_{R}(k))\) and \((x_{T}(k),y_{T}(k),z_{T}(k))\), respectively, where \(1\leq k\leq K\). An illustration of the back-projection algorithm is presented in Figure 10.  
The coordinate system, images area and antenna position define the geometry of the scene. In addition to the geometric configuration, the range profiles at each aperture position are needed to create the image. The range profile at each aperture position is defined as \(s_{k}(t)\), where \(t\) is the delay and \(k\) is the index of the aperture position.  
The amplitude of each pixel in the image, \(P(i)\), is calculated using  
\[P(i)=\sum_{i=1}^{N}\sum_{k=1}^{K}s_{k}(f(i,k)) \tag{10}\]  
Figure 10: Geometry of synthetic aperture radar (SAR) imaging scene.  
where \(f(i,k)\), is the time delay from the transmit antenna at aperture location \(k\) to the pixel at location \(i\) plus the time delay from the pixel to the receive antenna, also at location \(k\). The delay from the transmit antenna to each pixel location is calculated using  
\[d_{T}(i,k)=\frac{\sqrt{\left[x_{T}(k)-x_{p}(i)\right]^{2}+\left[y_{T}(k)-y_{p}( i)\right]^{2}+\left[z_{T}(k)-z_{p}(i)\right]^{2}}}{c} \tag{11}\]  
where \(d_{T}(i,k)\) is the delay from the transmit antenna to the pixel and \(c\) is the speed of light. Similarly, the delay from the image pixel to the receive antenna is calculated using  
\[d_{R}(i,k)=\frac{\sqrt{\left[x_{R}(k)-x_{p}(i)\right]^{2}+\left[y_{R}(k)-y_{p} (i)\right]^{2}+\left[z_{R}(k)-z_{p}(i)\right]^{2}}}{c} \tag{12}\]  
where \(d_{R}(i,k)\) is the delay from the transmit antenna to the pixel. The two-way delay, \(f(i,k)\), is then given by  
\[f(i,k)=d_{T}(i,k)+d_{R}(i,k) \tag{13}\]  
Now that the mathematics for SAR imaging have been established, SAR images can be created.
## 5 Challenges for ML-Based Routing Algorithms in WSNs  
Despite their great promise, there are many challenges to implementing ML-based routing algorithms in WSNs, which should be addressed in future research. The key challenges come from the requirements around learning performance, energy efficiency, real-time performance, and maintaining the security of the application and privacy of the users.  
Most current studies consider routing transmission and decision making in a single ML framework to obtain the best policy. However, from a real-world perspective, it is extremely costly for the network structure to collect information, which may be due to high energy consumption, long delay, asynchronous information preprocessing, and reduced learning speed. Therefore, the open problem is finding the best balance between learning performance and information quality. This prevents agents from consuming excessive resources just to achieve a minimal, negligible increase in learning performance.  
As highlighted earlier, sensor nodes cannot replace their batteries after deployment, and the lifetime of the sensors determines the lifetime of WSNs, which places a demand on the energy efficiency of the routing protocol. However, ML algorithms, especially the deep neural network, need a large amount of computation to ensure the accuracy of the algorithm, which will undoubtedly consume a lot of energy, making the needs of the two contrary. Determining how to identify a point where the superiority of ML algorithms is balanced with energy efficiency is one of the core challenges for future applications of ML algorithms in WSNs.  
In some application scenarios, WSNs require a real-time response after monitoring events. Some ML algorithms take the approach of collecting network conditions periodically before recalculating and distributing the updated network dispatch information, which cannot meet a real-time performance in WSNs. Therefore, it is necessary to adopt efficient solutions for specific applications to ensure that relevant information is communicated quickly and accurately.  
In addition to the requirements for energy efficiency and real-time performance, security is also a key issue for WSNs, since WSN is a resource-constrained communication network, and communication between sensors mainly relies on low-bandwidth channels.  
Hence, it is too complicated for traditional protection mechanisms to operate on WSNs to ensure their security. Identifying how to ensure the security as well as privacy of sensing data through ML algorithms in WSNs is one of the most challenging research topics.  
Another challenge is most existing ML-based routing algorithms are based on simulation, which is far away from the actual environments of WSNs. Building large-scale test beds to evaluate the performance of the existing algorithms and refine the ML-based routing schemes is an exciting research area in WSN.
# Flooding Message Mitigation of Wireless Content Centric Networking for Last-Mile Smart-Grid +
## 4 Performance Evaluation
### Evaluation of the Flooding Range Control Scheme  
In multi-hop wireless LMN environment, control messages can be flooded to entire LMN. This flooding message will increase network congestion because collision detection channel access scheme cannot be effectively adapted to wireless environment. Thus, wireless CCN have to reduce the number of flooding messages and its flooding range. Figure 7 shows the flow snapshot of flooding packets in each scheme, which are pure CCN, TOP-CCN without flooding range control and TOP-CCN with flooding control. A red circle represents a CCN node and a green arrow shows a packet flow. In Figure 7a, packet flows of pure CCN should fill out entire network to find the content provider and to reply the requested content. However, TOP-CCN as Figure 7b dramatically reduces the number of flow by exploiting MPR and PMPR strategy. In this figure, the almost edge nodes in the network do not rebroadcast interest and content packet. In addition, flooding range is effectively limited through the expected hop count measured by CA messages from the all nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline MAC/PHY protocol & IEEE 802.15.4 SUN non-beacon mode \\ PHY rate/max PPDU size & 250 Kb/s (DSSS)/2047 octets (SUN) \\ \# of MAC retransmissions & 3 \\ \# of AMI devices & 100–900 \\ Device density (\# of Devices per 100 m\({}^{2}\)) & 0.1–0.9 \\ Transmission/Interference range & 100 m/140 m \\ Network size/topology & 1000 by 1000 m/Grid topology \\ MPRcost parameters & \#: 10 s, \(\alpha\): 0.5 \\ CA interval & 2 s \\ Simulation Time & 1 h \\ \hline RPL parameter (DIO interval) & 1 s (evaluation for CCN and IP LMN) \\ \hline
**Application** & **Data Size (Bytes)** & **Delay** \\  & **Requirement** & **Interval** \\ \hline Time synchronization & 64 & 2 s \\ Price alert & 60 & 5 s \\ Firmware update & 50,000 & Best effort \\ Load control & 60 & 5 s \\ Smart meter (on-demand) & 64 & Best effort \\ Smart meter (Periodic) & 64 & Best effort \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters and Smart Grid (SG) application set.  
Figure 7: The number of flooding messages (interest/content data) comparison in single consumer scenario using ns-3 visualization tool (Consumer: DCU/Gwateay, Content provider: Smart Meter device).
## 4 Case Study
### Selection of Replacement Plan
#### 4.3.3 Performance Coefficient Determination  
Referring to Equation (24) and gained hierarchical weights, the performance coefficient of each replacement plan can be determined and illustrated, as Table 14 shows.  
Ultimately, plan A with the higher performance coefficient of 0.854 will be adopted to guide the BECI replacement, which implies that the MA modular type BECI will be selected instead of the SC comb plate type.
## 3 Experiment Workflow
### Clustering  
One effective approach for differentiating traffic traces is to categorize them into groups based on a specific similarity measure. A straightforward method for assessing the similarity between two time series involves comparing each value in the first time series with its corresponding value in the second time series. This direct comparison allows for a clear evaluation of how closely the two series align over time. Nonetheless, this approach is often impractical due to factors such as noise, varying lengths of the series, and differences in dynamics and scales. Therefore, it is more advantageous to utilize a clustering algorithm on the interpretable feature vectors that have been extracted. These vectors effectively summarize the transient characteristics of the time series making this approach more resilient to noisy data. We applied the features identified and filtered in Section 3.2.  
Figure 5: Finding similarities in a univariate time series. Features are computed from the consecutive sliding windows of length \(w=5\), represented as the vectors \(\{X_{1},X_{2},\ldots,X_{n}\}\). Then, the distance among the features is computed. The computed distance is used to find the clusters of the features.  
In addition to the similarity metrics, the choice of clustering method also influences the outcomes. Therefore, we utilized three algorithms, namely KMeans, OPTICS, and AutoClass, with each representing a distinct clustering approach. The KMeans algorithm is a centroid-based clustering algorithm that partitions data into \(k\) distinct clusters. In our case, \(k\) was equal to the number of distinct types of traces we wanted to identify: \(k=2\) for non-video and video identification and \(k=6\) for identification of all traces listed in Table 1. It is simple to implement, is efficient for large datasets, and works best when clusters are spherical and evenly sized. Its disadvantages are sensitivity to the initial placement of centroids and poorer results, with clusters of varying sizes, densities, and non-spherical shapes. OPTICS creates a reachability graph to identify clusters of varying densities. It can identify clusters of varying densities and shapes, making it more flexible than KMeans, but at the cost of higher computational intensity. The last algorithm, AutoClass, uses Bayesian methods to identify clusters in data. It models the data as a mixture of probability distributions and uses a Bayesian approach to determine the best clustering. It can model clusters with different shapes and sizes due to its probabilistic nature. Similarly to OPTICS, it is computationally intensive and can be slower than KMeans, especially for large datasets. With this selection, we aim to compare the algorithms' classes instead of the algorithms themselves.
# Effect of Biochar Application Depth on Crop Productivity Under Tropical Rainfed Conditions
## 4 Discussions
### Field Contingency Effect on Yield  
To further understand the effect of the contingent field conditions on the performance of the measured yield variables, a table of the best- and worst-performing treatments was constructed (Table 2). Note that plant weight (PW) and height (PH) constituted of the sum of the values of all the measured yield components (root, shoot, panicle and grain), and therefore not separately given. Thus, from Table 2, it was apparent that under high soil moisture (SM) content, water was not a factor for the performance of the measured yield variables (MYV), except plant length (PL). Conversely under low soil moisture content, water became critical for the performance of all the measured yield variables, except crop water content (CW). This suggested that rice plant always adjusted its own tillering (TN), growth (measured as PL) and dry matter accumulation (measured as PW) to maintain a certain level of water to keep alive. Soil moisture content was lowest under TC because of mounding (raised bed), implying that most of the soil moisture was absorbed to sustain growth under TC. Furthermore, large plant length was influenced only by soil moisture, whereas small plant length was a function of soil moisture, tiller number, and plant weight. While low tiller number was affected by all the measured yield variables, except crop water content, high tiller number was a function of both crop water content and plant weight.  
When related with the contingent field conditions, it was clear that TC (the 30-cm depth treatment with mounding runoff and high pest damage) had the lowest value for the measured yield variables, except crop water content (Table 2). Also, while low crop water content was not really a growth and yield factor, high crop water content influenced both tiller number and plant weight, but not plant length. Here again, soil moisture content had no effect on crop water content. Table 2 also showed that while TA (10-cm depth treatment with high water logging and limited pest damage) was lowest only for crop water content, TB (20-cm depth treatment with no water logging or pest damage) was highest for all the measured yield components, except plant length. This reaffirmed the point that the contingent field conditions affected the results of the experiment. Deductively therefore, it was possible that biochar application depth had a boosting effect on the performance of NERICA-4 rice cultivar. This was because for most of the time, TB (20-cm biochar depth treatment with no pest damage) performed better than CK (control treatment with water logging and no biochar or pest damage).
## 4 Construction of a Control System Model and Test Results
### Simulation Tests of a Control System without Disturbances and Inertial Block
As can be seen from the graph in Figure 4, the stabilisation level was below 40% of the \(\varphi_{min}\) value. This means that setting \(\varphi_{min}\) below the measurement accuracy or expected control accuracy should not affect the control accuracy if the measurement error is not constant but oscillates around the correct value. An interesting result was obtained for \(\varphi_{min}=0\), and despite the threat of dividing by zero, such a phenomenon did not occur. This means that the measured value never reached zero. Thus, a preliminary conclusion can be drawn that the smaller the value of the \(\varphi_{min}\) parameter, the more accurate the control process is. However, in practice, when setting the value of \(\varphi_{min}\), the measurement accuracy and the accuracy of the digital recording of the calculation value should be taken into account, and \(\varphi_{min}\) should be set on this basis.
## 3 Results and Discussion
### The Effect of the Moisture Content on the Pore Pressure  
#### 3.6.1 Concrete Slabs Exposed to a Target Temperature of 600 \({}^{\circ}\)C  
The moisture content of a concrete specimen represents the mass percentage of the moisture it contains, which is closely related to the increase in the pore pressure at high temperatures [63]. The evolution of the pore pressure at a depth of 10 mm from the heated surface of the concrete slab, at a target temperature of 600 \({}^{\circ}\)C and a heating rate of 5 \({}^{\circ}\)C/min, is shown in Figure 24a. The LWAC slabs had similar pressure curves in the oven-dried and air-dried states. The maximum pore pressures of the LWAC slabs in oven-dried and air-dried states were 102 and 96 KPa, respectively. However, the NWC slab had a higher pore pressure in the air-dried state, and there was an obvious peak value (214 KPa) and a steep pressure rise phenomenon after being heated for 2 h. This was due to the rapid rise in the pore pressure, which caused the internal fractures resulting in the gas escaping, causing the pressure value to drop rapidly. At 30 mm from the heated surface (as shown in Figure 24b), the pore pressure of the LWAC slab in the air-dried state was slightly higher than that in the oven-dried state, indicating that a higher water content led to a higher steam pressure. This is consistent with the data in the literature [62; 64]. However, the NWC slab showed the opposite result at this depth. The reason for this might be that a large crack occurred at 10 mm and extended and expanded, which made it easier for the steam to escape here, resulting in a lower pore pressure. At 50 mm from the heated surface (as shown in Figure 24c), the pore pressure of the LWAC slab in the air-dried state was higher than that in the oven-dried state. In addition, Figure 24c shows that the high water content caused the pressure curve to be more unstable. The NWC slab also had the same trend.  
Figure 23: Effect of the heating rate on the pore pressure of the air-dried specimens performed at a target temperature of 800 \({}^{\circ}\)C: (**a**) 10; (**b**) 30; (**c**) 50 mm depths.  
The evolution of the pore pressure at a depth of 10 mm from the heated surface of the concrete slab, at a target temperature of 600 \({}^{\circ}\)C and a heating rate of 10 \({}^{\circ}\)C/min, is shown in Figure 25a. The LWAC slabs had similar pressure curves in the oven drying and air-dried states. The maximum pore pressures of LWAC slabs in oven-dried and air-dried states were 102 and 96 KPa, respectively. However, the NWC slab had a higher pore pressure in the air-dried state and showed a pressure rise, which was the same as that at a heating rate of 5 \({}^{\circ}\)C/min at the same target temperature. At 30 mm from the heated surface (as shown in Figure 25b), the pore pressure difference of the LWAC slab under different moisture states was very small, and this phenomenon was also observed for the NWC slab.
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 2 Food Quality Indicators
### Humidity  
Constant humidity values are essential for preserving the condition and texture of food. Indeed, low humidity values can dry the edibles, while an excess of moisture can provide the ideal environment to foster microbic and fungi growth [42; 43]. Common packaging technologies are supposed to not be permeable to moisture [44; 45; 46]. As a matter of fact, the level of humidity inside food packages can undergo significant variations due to accidental breakage of the package as a consequence of improper manipulation and prolonged exposure to significant temperature fluctuations. Therefore, humidity is a key factor to be considered in food quality assessment as it can provide useful information as regards improper storage conditions.