esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Radiological and Chemical Risk for Public Health from Flour Sample Investigation
## 4 Conclusions  
In this article, radiation levels, metals contamination, and inorganic mineral content in flours were measured; this is extremely important, due to their high consumption by the population for nutritional use.  
The specific activity of the main natural radionuclide present in flour, \({}^{40}\)K, was measured using HpGe gamma spectrometry, with the aim of estimating the health risk for the age category higher than 17 years by the assessment of the effective dose due to flour ingestion. The coefficient of the effective dose for ingestion reported by Italian Legislative Decree 101/20 was employed. The estimation of the annual effective dose was found to be in the range 0.04-0.23 mSv y\({}^{-1}\), about 1.6-9.6% of the total natural radioactivity value for humans (2.4 mSv y\({}^{-1}\)) and under the threshold level (1 mSv y\({}^{-1}\)), thus excluding the risk of ionizing radiation effects on humans. The \({}^{137}\)Cs activity concentration was lower than the minimum detectable activity value for all analyzed samples, thus excluding its presence as pollutant.  
As far as the heavy metal (Cd and Pb) concentration is concerned, it was lower than the contamination threshold value, thus excluding their presence as contaminants.  
The PCA multivariate statistical analysis, performed starting from the inorganic minerals content in the investigated samples, showed how it is possible to discriminate among them, with a strict dependance on the compositional features of the plant or fruit from which the analyzed flours derive, thus representing a powerful tool to guarantee the product authenticity to the consumers.  
Conceptualization, F.C. and V.V.; methodology, F.C. and V.C.; validation, D.M.; formal analysis, G.A., M.D., A.B., S.M. and M.M.; investigation, F.C., G.A., G.P. and V.V.; resources, F.C., V.C. and D.M.; data curation, F.C.; writing--original draft preparation, F.C.; supervision, D.M., V.C. and V.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# A Brief Review of Mueller Matrix Calculations Associated with Oceanic Particles
## 2 Fundamental Concepts for Mueller Matrix Calculations
### [PERSON]'s Equations and the Volume/Surface-Integral Equations
In the far-field regime, they can be expressed in the form  
\[\left.\mathbf{E}^{\mathrm{sca}}\left(\stackrel{{\rightarrow}}{{r}} \right)\right|_{r\rightarrow\infty}=\frac{\exp(ikr)}{-ikr}\frac{ik^{3}}{4\pi }\int\limits_{V}d^{3}\stackrel{{\rightarrow}}{{r}}\left\{(m^{2} -1)\left[\hat{r}\times\hat{r}\times\mathbf{E}(\stackrel{{ \rightarrow}}{{r}}^{\prime})\exp(-ik\hat{r}\cdot\stackrel{{ \rightarrow}}{{r}}^{\prime})\right]\right\}, \tag{7}\]\[\mathbf{E}^{\text{sca}}\left.\left(\overrightarrow{r}\right)\right|_{r\to \infty}=\frac{\exp(ikr)}{-ikr}\frac{k^{2}}{4\pi}\hat{r}\times\oint_{S}d^{2} \overrightarrow{r}^{\prime}\Big{\{}\left[\hat{n}_{s}\times\mathbf{E}( \overrightarrow{r}^{\prime})\right]-\frac{\omega\mu_{0}}{k}\hat{r}\times \left[\hat{n}_{s}\times\mathbf{H}(\overrightarrow{r}^{\prime})\right]\Big{\}}, \tag{8}\]  
where the parameters are given in Figure 1; \(\hat{n}_{s}\) is the outward normal to the surface. It is evident that the scattered far field only depends on the scattered directions with an outgoing spherical wave factor \(\exp(ikr)/kr\).
# Cooperative Buffering Schemes for Time-Shifted Live Streaming of Distributed Appliances
## 3 Cooperative Buffering Schemes for Time-Shifted Live Streaming
### Overall System Architecture  
Our P2P time-shifted live streaming system consists of streaming servers, a tracker server, and PVRs, as shown in Figure 2. The streaming servers store all TV programs after having received them directly through broadcasting channels. They also have sufficient network bandwidth to transmit a large number of programs to PVRs simultaneously. When a PVR cannot receive necessary data from other PVRs due to lack of available chunks, the streaming servers immediately transmit them to avoid service interruption.  
The tracker server coordinates our time-shifted live streaming services by managing the buffering and transmission of broadcast programs among PVRs. First of all, to schedule buffering live programs in the system, it periodically collects the current status information including the current playback position of each PVR and the degree of deficiency and the number of playback requests of each chunk. Based on this information, the tracker server requests the PVRs playing back at the broadcast time to buffer the chunks of broadcast programs assigned to themselves. To support the playback of buffered programs, it also maintains necessary information including a list of available TV programs and the current connection status of each PVR. When a PVR moves to the desired playback position using time-shifting functions such as skip-forward/rewind and fast-forward/rewind, the tracker server gives the PVR a list of neighbor PVRs playing back around the position. Afterreceiving a certain number of chunks from neighbor PVRs, the PVR then starts to play them back. The tracker server also maintains a skip list capable of keeping a list of PVRs according to playback positions to quickly retrieve the PVRs playing back around the desired position.  
Note that we employ the mesh-pull based P2P networks to provide the streaming services and the combined storage spaces reserved for time-shifted live streaming among PVRs form a virtual storage space for cooperating to buffer and exchange live programs.
# Improved Hybrid A* Algorithm Based on Lemming Optimization for Path Planning of Autonomous Vehicles
## 5 Simulation Experiments
### Simulation Result Analysis
The simulations show that, in different scenarios and processes, there are some parameters that need to be adjusted numerically according to the scenario's conditions in order to make the algorithm more stable, as shown in Table 11.  
By comparing simple and complex scenarios and adjusting the parameters accordingly, it is found that the algorithm can balance efficiency and robustness in different scenarios. In practical applications, it is recommended to first use a grid search to determine the parameter range and then fine tune it using an optimization algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Suitable Value for Simple Scenario** & **Suitable Value for Complex Scenario** & **Adjustment Logic** \\ \hline Population size (N) & Minimum value (10–20) & Maximum value (20–30) & Complex scenarios require more diverse exploration. \\ Maximum number of iterations 0 & Minimum value (20–30) & Maximum value (40–60) & Complex scenarios require longer convergence times. \\ Lévy flight intensity & Lower value (levy\_strength = 0.1) & Higher value (levy\_strength = 0.3) & Complex scenarios require stronger global leap capabilities. \\ Local search weight & Higher value (local\_weight = 0.8) & Lower value (local\_weight = 0.5) & Simple scenarios converge quickly, while complex scenarios require balance. \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of LOA parameters in different scenarios.
## 2 Volterra Model and FSI Method  
### Volterra Series Theory  
The Volterra series theory was first proposed by [PERSON] in 1880 (Volterra [23]). According to the Volterra series theory, for any continuous-time nonlinear and time-invariant system, the relationship between the system input \(u(t)\) and the system output \(y(t)\) can be expressed as follows,  
\[\begin{split} y(t)&=h_{0}+\int\limits_{0}^{t}h_{1}( \tau_{1})u(t-\tau_{1})d\tau_{1}+\int\limits_{0}^{t}\int\limits_{0}^{t}h_{2}( \tau_{1},\tau_{2})u(t-\tau_{1})u(t-\tau_{2})d\tau_{1}d\tau_{2}\\ &\quad+\cdots+\int\limits_{0}^{t}\cdots\int\limits_{0}^{t}h_{n}( \tau_{1},\cdots,\tau_{n})u(t-\tau_{1})\cdots u(t-\tau_{n})d\tau_{1}\cdots d\tau_ {n}\end{split} \tag{1}\]  
where \(u(t-\tau_{k})\) denotes the system input; \(y(t)\) denotes the system output; \(h_{0}\) represents the steady-state term; \(h_{1}\) represents the first-order kernel; \(h_{n}\) represents the higher-order term which describes nonlinear features of the system.
# A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment
## 4 Simulations and Results Analysis
### Performance Metrics  
For each simulation, we computed the following metrics:  
* A-PDR: the aggregated packet delivery ratio by considering all deployed gateways. By aggregated, we mean that a packet was received by at least one GW.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of EDs & from 100 to 1000 by step of 100 \\ Payload length & from 10 to 100 bytes by step of 10 \\ Radius & 7.5 km \\ Simulation Time & 60 min \\ Packet generation period & from 1 pkt/min to 1 pkt/17 min by step of 2 \\ Number of gateways & from 1 to 5 by step of 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
Pkt-Sent: the number of packets sent by all EDs in the network. We note that this number also included the retransmitted packets in the case of confirmed traffic.
* LPI: refers to the number of lost packets at the gateway due to interference. The value of this metric was the average for all used gateways.
* LPTX: refers to the number of lost packets at the gateway since it was in the transmitting phase (TX), typically, when the gateway sent the ACK to the ED. The value of this metric was the average for all used gateways.  
We note that we only show a part of the obtained results to avoid redundancy. Generally, we do not display all the results for other generation period traffic since our goal was to study multi-gateway behaviour in high-traffic conditions. However, we can confirm that we still observed the same trend in those results with a frequency of 1 pkt every 3, 5, 7, and 9 min but with slight differences.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 3 Design and Implementation
### System Implementation
#### 3.2.1 Device Gateway  
This component is responsible for collecting and gathering data from clients' devices, such as sensors or smart wearable devices, and then transferring them to the fog server in a static frequency. The device gateway receives IoT data from clients who are not able to make connections with the fog server directly. It is important to note that IP protocol  
Figure 3: Eliminating the single point of failure risk.  
may not be supported by all clients' devices. IP-enabled devices can communicate with the fog directly allowing them to transfer data in an IoT context, whereas other devices that support other protocols, such as ZigBee, have to communicate with the fog through the device gateway. The device gateway plays a key role in exchanging data between different protocols. MPIGate [24] (i.e., it stands for a multi-protocol-interface gateway) that integrates actuators and sensors that are supporting different network protocols (e.g., ZigBee, EIB/KNX, Bluetooth, WiFi) is used as a device gateway.  
In fog computing, nodes are placed close to clients' devices (IoT source nodes) allowing them to upload their data to the nearest fog node (i.e., latency will be noticeably reduced). Clients who upload their data via the device gateway have to share their location coordinate allowing it to upload the received IoT data to the nearest fog node.
## 4 Discussion
### Techniques for Measuring the Changes in the Residual Lower Limb
#### 4.1.4 Bioimpedance  
There is quite a high number of publications on bioimpedance measuring techniques. This technique is among the best technique that can be applied to measure the residual limb volume fluctuation due to its sensitivity. In the early stage of bioimpedance analyser instrument development, [PERSON], [PERSON], and [PERSON] [48] used a commercial bioimpedance analyser (Xitron Hydra 4200) that can measure impedance at 50 frequencies between 5 kHz and 1 MHz to determine the fluid resistance. The data collected were then used to estimate the extracellular fluid volume. The researchers used four strip electrodes, namely the outer pair (injected current) and the inner pair (sensed voltage) to collect the impedance data. Then, [PERSON] et al. [50] enhanced the design of their device by changing the dimensions of the current injecting electrodes (proximal: 15 \(\times\) 2 cm; distal: 3.5 cm diameter) and voltage sensing electrodes (7.5 \(\times\) 2 cm). Other than that, previous studies customised the electrodes using conductive tape (0.09 mm thickness), custom multi-stranded silver-plated copper wire, and polyvinyl chloride (PVC) insulation. The wire was attached to the electrode by splaying its ends and sandwiching it between two pieces of conductive tape (the underside of the conductive tape was covered with hydrogel). A uniform-thickness hydrogel patch was also used in most studies. Meanwhile, the outside of the conductive tape was covered with Tegaderm. By using this measuring technique, researchers may determine the residual limb volume gain or loss while the residual limb is in the prosthetics socket. Thus, the analysis of the residual limb volume fluctuation in a special region can be conducted in real time. This technique can also be simultaneously conducted while the amputee is sitting, standing, or walking. Moreover, with this technique, researchers may also determine the possible causes of residual limb volume fluctuation, such as the health status of the subject [12], the prosthetic socket suspension system [49; 56], the socket design or socket size [52; 57], activity level [6; 7; 8; 50], and the post-doffing effect [51]. [PERSON] et al. [47] also reported that this bioimpedance technique is highly correlated (r > 0.9) with the extracellular fluid volume measured using the MRI technique. However, the publication was only limited to the level of transtibial amputation. Hence, more research needs to be conducted on the transfemoral amputation level since there is more rapid volume loss from the huge number of soft tissue areas compared to transtibial amputation. The use of bioimpedance on transfemoral amputees also needs to be validated before it is implemented clinically.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 5 Discussion  
This study employs a patch-wise diffusion model to enhance low-light images, introducing uncertainty guidance within selected patch areas. Notably, the authors prioritize randomness in patch selection, as opposed to traditional feature-based approaches. This shift is based on the observation that feature-based patch selection proves unreliable in low-light conditions, where noise and insufficient lighting can distort feature representation. The results presented suggest that this approach not only improves the diffusion model's structure-awareness but also enhances its regression performance. This indicates that integrating randomness into patch selection could be crucial for handling low-light environments, where traditional methods may struggle. Overall, this approach provides valuable insights into the use of patch-wise methods for more robust low-light image enhancement.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Pharmacological Strategies
#### 5.3.3 Bradykinin (BK), BK Mediators, Nitric Oxide (NO), and NO Mediators  
Bradykinin (BK) and its receptors, B1 and B2, play a significant role in regulating vascular permeability. The activation of these receptors promotes vasodilation, disrupts endothelial cell junctions, and contracts the endothelial cell cytoskeleton, thereby increasing vascular permeability. Many tumors overexpress BK receptors and contain high levels of bradykinin, which enhances tumor vascular permeability and facilitates the EPR effect [193].  
Researchers have explored the use of angiotensin-converting enzyme (ACE) inhibitors, such as enalapril, to prevent the degradation of bradykinin. By inhibiting bradykinin degradation, these ACE inhibitors enhance tumor vascular permeability and improve the accumulation of nanomedicine. For instance, enalapril has been shown to increase the delivery of bacterial agents to tumors, while captopril improves nanoparticle accumulation in glioma tumors. These findings highlight the potential of ACE inhibitors in enhancing the EPR effect and improving therapeutic outcomes [194,195].  
Nitric oxide (NO) is another key mediator of vascular permeability. NO enhances tumor vessel permeability by binding to soluble guanylate cyclase in smooth muscle cells, leading to vasodilation. In cancer, the overexpression of endothelial nitric oxide synthases (eNOS) and inducible nitric oxide synthases (iNOS) in tumors allows for selective NO production, which can be exploited to improve drug delivery [196].  
Studies have investigated the use of NO-generating agents, such as L-arginine and hydroxyurea, to increase NO levels in tumors. These agents have been shown to enhance the accumulation of therapeutic agents in tumors, inhibit tumor growth, and reduce metastasis. By leveraging the role of NO in vascular permeability, researchers aim to develop more effective strategies for enhancing the EPR effect and improving cancer treatment outcomes [197].
# Research on Shear Behavior of Sand-Structure Interface Based on Monotonic and Cyclic Tests
## 4 Cyclic Shear Tests
### Test Results
#### 4.1.2 Shear Displacement-Normal Displacement Curve  
The changes in shear stiffness and residual deformation in the above cycles will be discussed later in this article. In order to compare with the previous monotonic shear test research, Figure 7d-f show the shear displacement-normal displacement curve corresponding to the test results above. It can be seen from the test results that the loose sand is located on a relatively smooth interface. During the initial loading process, the curve is similar to the static loading situation described above, generally showing volume shrinkage. In the later dynamic loading process, the normal displacement of each cycle is much smaller than that of the initial loading stage, and it continues to develop. It is found that the final deformation accumulated during the dynamic loading phase exceeds that produced by the above-mentioned static test. The higher the stress level amplitude, the greater the accumulated normal deformation. In addition, the normal deformation is mainly affected by the normal stress, and the interface roughness has little effect on its deformation. As the normal stress increases, the total amount of normal deformation accumulated during dynamic loading decreases. Taking the stress level amplitude of 90% loose sand on the relatively smooth interface as an example, when the normal stress is 200 kPa, the cumulative normal deformation is 0.108 mm. When the normal stress increases to 400 kPa, the accumulated normal deformation is reduced by 35%. It can be seen that the normal displacement of the loose sand interface gradually decreases in each cycle. This corresponds to the tightness between the loading and unloading curves of the aforementioned shear displacement-shear stress ratio. The loading and unloading curve tend to be loose, the normal displacement produced by each cycle is larger, and the final cumulative normal deformation is larger. Additionally, notice that the normal displacement curve has a tendency to move continuously to the right, which means that residual deformation has occurred in the cycle. This is also consistent with the above analysis of residual deformation caused by inconsistent loading and unloading stiffness.  
The performance of dense sand in circulation is similar, except that the accumulated normal deformation is smaller than that of loose sand.
## 4 Anaerobic Digestion Process
### Hydrolysis  
The hydrolysis phase of anaerobic digestion is where complex biopolymeric compounds (lipids, carbohydrates, and proteins) are converted to water-soluble compounds by degradation through Bacteroides, Clostridia, and Bifidobacterium and sometimes Streptococcus and Enterobacteriaceae [52]. This step is relatively slow and can limit the rate of the overall digestion, mainly when solid material is used as a substrate. As seen in Equation (1), cellulose (C\({}_{6}\)H\({}_{10}\)O\({}_{5}\)) is hydrolyzed to generate glucose (C\({}_{6}\)H\({}_{12}\)O\({}_{6}\)) and hydrogen (H\({}_{2}\)). This reaction is catalyzed by homogeneous/heterogeneous acids yielding the fermentable monosaccharide (C\({}_{6}\)H\({}_{12}\)O\({}_{6}\)). The products (C\({}_{6}\)H\({}_{12}\)O\({}_{6}\) and H\({}_{2}\)) are used by the fermentative microorganisms in the next phase to form higher-chain organic compounds, such as volatile fatty acids [53; 54].  
\[\left(\text{C}_{6}\text{H}_{10}\text{O}\text{S}_{\text{n}}\right)_{\text{n}}+ \text{nH}_{2}\text{O}\ \rightarrow\text{n}(\text{C}_{6}\text{H}_{12})+\text{nH}_{2} \tag{1}\]
## 4 Simulations and Results Analysis
### Security Degree
#### 4.6.1 Behavior Analysis of \(g\)  
To show the behavior of the security degree of a node, which results from the product of the security metrics computed by SDSE, we use the following configuration parameter values:  
1. Cryptography: RC5 Algorithm. * \(f=394,254,429,396\) keys/s; * \(t=45\) days; * \(s=64\) bits.
2. Key Management: q-composite scheme. * \(k=200\) keys; * \(q=2\) keys; * \(p=0.33\).
3. IDS: for the case when a global alarm is not raised by the IDS (\(\mathbf{A-}\)). * \(m=5\) nodes; * \(N=10\) nodes; * \(P_{tn}=0.7\); * \(P_{fn}=0.3\).  
Figure 7: Accuracy of the legitimacy probability when the IDS raises a global alarm, \(P_{L}(alarm)\), _vs._ consensus parameter \(m\). For different values of \(P_{M}\), \(N=10\) neighbors, \(P_{tp}=0.6\) and \(P_{fp}=0.4\).  
Figure 8 presents the behavior of the security degree of a node as a function of the number \(x\) of captured nodes, for different values of base rate (\(P_{M}\)) in a network with \(1000\) nodes. Note that the security degree decreases as \(x\) increases and that it decreases more significantly for values of \(x\) greater than 50. This behavior is expected, since it is the same behavior presented by the resilience probability \(P_{R}(x)\). Notice, also, that the security degree is smaller for larger values of \(P_{M}\). Similarly, this behavior is inherited from the metric \(P_{L}(\
eg alarm)\), in which the greater the number of malicious nodes in the network, the smaller the probability that a node is legitimate when an alarm is not raised against it.