esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Electrochemical Sensors for the Detection of Glyated Hemoglobin
## 4 Comparison of the Characteristics of Direct and Indirect Electrochemical HbA1c Sensors  
In direct type sensors, HbA1c binds to the electrode surface through the bio-affinity of antibodies, boric acid, etc., and then, the appropriate signal transduction mode is used to directly detect HbA1c [23]. The selective binding of HbA1c to the electrode surface produces electrochemical signals in terms of current, voltammetry, impedance, or potential patterns. Such fabricated sensors have a wide detection range, but the specificity is poor. Generally, whole blood samples need to be pretreated to obtain HbA1c for further determination. These sensors are suitable for long-term HbA1c level monitoring.  
Indirect type HbA1c sensors work based on the glycated amino acid FV or FVH produced through the hydrolysis of HbA1c protein as detected by FAO, FPOX, or MIC. HbA1c must be predigested by proteolytic enzymes to produce FV fragments. Therefore, these sensors involve much more complicated pretreatment and post-detection conversion processes than their direct type counterparts. The FV sensors mainly modified by nanomaterials offer a wide detection range in line with clinical requirements and good stability, and they generally do not suffer from specificity problems. However, the distribution of nanoparticles on the modified interface is usually not uniform, which results in poor repeatability. Moreover, FV sensors built based on imprinting technology usually have a relatively narrow detection range. Due to the selectivity of specific polymers to the template FV molecules, this type of sensor has good specificity and repeatability, as well as a longer stability period. The fabricated FVH sensors combined with the FIA system offer a wide detection range in line with clinical requirements, good stability, and multi-sample continuous testing. In addition, the advantages and disadvantages of different types of electrochemical HbA1c sensors were compiled and are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Electrode Type/Interface** & **Detection Range (FV)** & **Detection Limit** & **Potential** & **Sample** & **References** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} **Type** \\  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Type** & **Advantages** & **Disadvantages** & **References** \\ \hline \multirow{4}{*}{Boric acid} & Easy to be chemically modified & Poor specificity & \multirow{2}{*}{[32; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]} & \\  & Short response time & Needs pretreatment & \\  & Wide detection range & of blood samples & \\  & Simple fabrication process & & \\ \hline \multirow{4}{*}{Ferrocene} & Good specificity & Iron ion is easy to & \multirow{4}{*}{[32; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47;
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 2 Theoretical Background of TD-NIRS
### The \(P_{1}\) Approximation  
In the \(P_{1}\) approximation, four unknowns, \(i_{00}(\mathbf{r},t)\), \(i_{1-1}(\mathbf{r},t)\), \(i_{10}(\mathbf{r},t)\), and \(i_{11}(\mathbf{r},t)\) are related to the fluence rate, \(\phi(\mathbf{r},t)\), and to the flux vector of the fluence rate, \(\mathbf{F}(\mathbf{r},t)\), expressed as,  
\[\phi(\mathbf{r},t)=\int_{4\pi}I(r,\hat{s},t)d\Omega,\;\mathbf{F}(\mathbf{r},t )=\int_{4\pi}I(r,\hat{s},t)\hat{s}d\Omega\;, \tag{3}\]  
and the \(P_{1}\) approximation is expressed by two coupled equations for \(\phi(\mathbf{r},t)\) and \(\mathbf{F}(\mathbf{r},t)\), including the reduced scattering coefficient, \(\mu_{s}^{\prime}(\mathbf{r})\) = [\(1-g\)]\(\mu_{s}(\mathbf{r})\), and the anisotropy parameter, \(g(\mathbf{r})\), defined as the average cosine of the phase function as,  
\[g=\frac{\int_{4\pi}(\hat{s}s)p(\hat{s},\hat{s}t)d\Omega}{\int_{4\pi}p(\hat{s}, \hat{s}t)d\Omega}=2\pi\int_{0}^{\pi}p(\theta)\cos\theta d\theta \tag{4}\]  
Figure 1: Definition of the radiance.  
where \(\theta\) is the angle between the directions \(\hat{s}\) and \(\hat{s}^{\prime}\), and the phase function \(p(\hat{s},\hat{s}t)\) is assumed symmetric to the azimuthal angle, \(\phi\), and dependent on the polar angle, \(\theta\), only. Here, the phase function is normalized as \((1/4\pi)\int_{4\pi}p(\hat{s},\hat{s}^{\prime})d\Omega=1\).  
From the two coupled equations, the equation for \(\phi(\textbf{r},t)\) is derived as,  
\[\begin{array}{l}\frac{3D(\textbf{r})}{c^{2}}\frac{\partial^{2}}{\partial t^{2 }}\phi(\textbf{r},t)+[1+3D(\textbf{r})\mu_{a}(\textbf{r})]\frac{1}{c}\frac{ \partial}{\partial t}\phi(\textbf{r},t)+\mu_{a}(\textbf{r})\phi(\textbf{r},t) -\
abla\cdot[D(\textbf{r})\
abla\phi(\textbf{r},t)]\\ =\frac{3D(\textbf{r})}{c}\frac{\partial}{\partial t}q_{0}(\textbf{r},t)+q_{0} (\textbf{r},t)-3\
abla\cdot[D(\textbf{r})\textbf{q}_{1}(\textbf{r},t)]\end{array} \tag{5}\]  
where \(D(\textbf{r})=1/[3(\mu_{s}{{}^{\prime}}(\textbf{r})+\mu_{a}(\textbf{r}))]\) is the diffusion coefficient, \(q_{0}(\textbf{r},t)\) and \(\textbf{q}_{1}(\textbf{r},t)\) are related to \(q_{lm}(\textbf{r},t)\) in Equation (2) describing the isotropic and anisotropic components of the light source, \(q(\textbf{r},\hat{s},t)\). Equation (5) is the equation for \(\phi(\textbf{r},t)\) in the \(P_{1}\) approximation having a form of the telegraph equation (TE) which is an elliptic type of partial differential equation including the second derivatives with respect to both time and space indicating a phenomenon of wave propagation in the medium.
# Dynamic Balance Simulation and Optimization of Electric Vehicle Scroll Compressor Rotor System
## 2 Dynamic Balance Principle of Rotor System  
There are two main factors that cause the dynamic imbalance of the rotor system of the scroll compressor. On the one hand, there is the effect of gas forces on the compression chamber of the vortex disk. During the operation of the scroll compressor, the rotating and stationary scroll discs are subjected to the same gas force. Since the stationary scroll and compressor housing are directly connected together, the pneumatic force acting on the orbiting and stationary scroll will inevitably cause the vibration and noise of the scroll compressor to increase. On the other hand, the centrifugal force is caused by the eccentric structure of the rotor system. The scroll compressor belongs to the high-speed rotary machinery, and the main shaft of the rotor system is eccentric due to the working characteristics of the scroll compressor. In addition, according to the need for involute meshing of the scroll disk, the orbiting scroll does not rotate around the center of the stationary scroll, but rather moves in a flat motion around the stationary scroll in a metric rotation under the constraints of the anti-rotation mechanism, which results in the radius of rotation of the center of mass of the orbiting scroll disk relative to the center of the spindle to vary with the rotation angle of the spindle. Therefore, during the operation of the rotor system, the centrifugal force generated by the unbalanced mass acting on the eccentric spindle will result in an unbalanced spindle motion.  
For the above reasons, two balancing methods are adopted: one balancing in order to determine the basic parameters of the balancing block, but due to the influence of the eccentric structure, the rotor system still retains a certain value of centrifugal force; the second balancing adopts a parametric optimization simulation of ADAMS/View 2020 to solve the problem of excessive change in the centrifugal force cycle. According to the principle of vibration, the transmission system of the scroll compressor belongs to periodic forced vibration, that is, the vibration generated by the system under periodic continuous excitation. For the transmission system of the scroll compressor, the source of periodic excitation is mainly the centrifugal force caused by the deviation of the center of mass of the transmission system from the main axis. The eccentric structure principle is shown in Figure 1.  
Figure 1: Schematic diagram of eccentric spindle structure.
## 6 Key Challenges and Mitigation Strategies
### Privacy and Security Risks  
Ensuring robust privacy and security is perhaps the most critical challenge, given the sensitive nature of IoT data and the distributed nature of FL. Key concerns involve balancing model utility against privacy guarantees, protecting against various attacks such as data leakage from model updates [110; 114], data or model poisoning by malicious clients [109; 115], Byzantine failures [116], and backdoor attacks targeting the models [117; 118; 119], all while complying with regulatory mandates like GDPR or HIPAA.  
A variety of techniques, often referred to as PETs and robust mechanisms, are used to mitigate these risks, each with distinct trade-offs in aspects like overhead (conceptually compared in Figure 6) and utility. DP offers strong, mathematical guarantees against inference attacks by adding calibrated noise. While generally having lower computational overhead than cryptographic methods, it introduces a direct privacy-utility trade-off, where increasing noise to enhance privacy typically degrades model accuracy [49], as illustrated conceptually in Figure 6. Cryptographic approaches like HE allow computations (like aggregation) on encrypted data, providing strong confidentiality against the server without accuracy loss, but their extremely high computational and communication overhead makes them largely impractical for direct use on most IoT clients [3; 53]. Similarly, SMPC enables joint computations without revealing private inputs, offering strong security through distributed trust with no accuracy loss, but typically requires complex, multi-round interactions unsuitable for dynamic IoT environments [55]. Secure aggregation protocols are optimized specifically for the FL summation task, offering much better efficiency than general HE/SMPC and protecting individual updates from the server during aggregation, but they do not protect the final model from inference or updates during transmission without additional measures [56].  
To defend against malicious clients sending faulty updates (poisoning or Byzantine attacks), Robust Aggregation methods like Krum [116], Bulyan [126], coordinate-wise median, or trimmed mean are employed to filter outlier updates. However, their effectiveness can decrease with sophisticated attacks or high Non-IID levels [127; 128]. Recent advancements show promise, such as the PEAR mechanism using cosine similarity and trust scores for better robustness in Non-IID settings [120], or techniques like ByzSFL that integrate Byzantine robustness with secure computation using zero-knowledge proofs (ZKPs) for efficient verification without revealing private data [124]. Complementary strategies include explicit attack detection and verification mechanisms [121; 122] and leveraging hardware security through Trusted Execution Environments (TEEs) to provide protected enclaves for computation [123].
## 3 Results: Network Performance
### Queue Lengths  
From the implementation point of view, it is very interesting to estimate the queue size needed to store packets in a node. The mean queue length \(\overline{Q}\) is calculated as the overall sum of packets stored in the queues in all the time slots divided by the simulation time and the number of nodes (excluding the Gateway), that is:  
\[\overline{Q}=\frac{1}{(N-1)\cdot t_{sim}}\left(\sum_{j=2}^{N}\sum_{t=1}^{t_{ sim}}r_{j,t}\right)\,, \tag{19}\]  
with \(r_{j,t}\) being the number of packets stored in the queue of node \(j\) after time slot \(t\), and \(t_{sim}\) the duration of the simulation. The results for both cases of Round-Robin and FIFO policies yield \(\overline{Q}<1\) independently of \(N\) and the operation time \(t_{sim}\) considered.  
Regarding the maximum length of the queue, it is easy to see that there is an upper bound equal to the number of time slots that the node remains in a non-transmission state (i.e., receiving or idle) plus one (own packet generated). For the scheduling matrix \(Q^{(\mathbf{4})}_{opt,\mathbf{4}\times N}\) in Equation (11), the maximum queue length is three packets because the maximum time between transmission slots is 2.
## 4 Discussion
The reason is that as the plasma temperature increases, the level of thermal ionization (freeing electrons from atoms under the influence of high temperatures) of the seeded alkali-metal element (such as potassium) increases rapidly, following a nonlinear dependence on the temperature [48]. The nonlinear increase in the ionization level (fraction of the seeded alkali-metal atoms that are ionized) with the temperature includes the temperature's exponential term [49; 50], such that the strengthening of the electric conductivity due to the improved ionization at elevated temperatures overpowers the opposite effect of weakening the electric conductivity due to the enlarged electron-neutral collision cross sections at elevated temperatures. Thus, the plasma electric conductivity is always boosted at elevated temperatures, even if the average electron-neutral collision cross sections increase with the temperature for one or more gaseous species contained within the plasma gas mixture. Third, the six gaseous species considered herein for analysis are not intended to be an inclusive list encompassing all species that can possibly appear in the combustion of a wide variety of solid, liquid, and solid fuels. This is neither practical nor intended. Instead, the selection of a few gaseous species that are representative of combustion reactants, combustion products, and seeding alkali metals makes this study more concise and easier to comprehend. The six gaseous species covered herein were selected such that they are all neutral molecular species, without dissociation [51; 52], which have data in the four literature sources of the four methods investigated herein. For example, molecular nitrogen (N\({}_{2}\)) was not included in the current study, because it is not one of the nine gaseous species listed in the literature source of method 3 (integration). Fourth, the inclusion of molecular oxygen herein (although not an ordinary combustion product gas) is justified, because despite being a reactant oxidizer species that is consumed during combustion, it may still exist in the combustion products (and thus, in the plasma mixture) at a small concentration, in the case of the lean fuel (oxygen-enriched) mode of combustion [53; 54]. Similarly, the inclusion of molecular hydrogen (despite being a fuel that is consumed during combustion) in the current study can be justified through the opposite fuel-rich or fuel-enriched combustion modes [55; 56], with hydrogen being the fuel or a component of it. In addition, including these two reactant species in the analysis enriched this study by manifesting at least one unique feature in their temperature-dependent electron-neutral collision cross section results, which was not found in the remaining four species covered here.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 3 Proposed Method
### Proposed Architecture  
The fuzzy system has been used in many areas for decision-making. Thus, we used the fuzzy system to reflect the uncertainty of the placement condition. It can control SP, according to the objective, by determining the VMs that require placement and selection of the target machine, according to calculated weights. It helps improve performance and minimize resource wastage in DDCs. The fuzzy-AHP system architecture and phase for SP are as follows.  
As shown in Figure 3, the proposed system consists of three main functions: Fuzzy System, AHP System, and placement manager.  
* In the fuzzy system, it consists of two main functions: fuzzy inference system and rule configure system.
* Fuzzy inference system (FIS) provides an appropriate target machine to meet service requirements when resources reach the threshold assigned for the service. In this rule-configure system (RCS), the initial rule is to reflect the current uncertainty of the placement condition. After the adaptive rule has been configured, the stability of the range of RU is increased by reconfiguring the range. We can set the objective rule for application to the fuzzy system by reconfiguring the range. We present a detailed explanation fuzzy system in Section 3.2.
* In the AHP system, we describe how to measure the action. In exploring the optimal placement scheme, Table 2 defines relative value-based weight for our set of policies based on Table 4. The evaluated ranking is reflected by the rationality of the ranking scale. The ranking is a numerical scale that indicates the relative importance of metrics through a comparison. We use the standard Saaty 'AHP [48; 49] 9-level scales, which Table 2 shows. Table 2 describes the notation for the proposed scheme. We send this result to the placement manager for weigh the calculation.
* The placement manager consists of two modules: decision and action for placement. In the decision module, it is the decision on the SP situation whether or not based on the fuzzy system values. In the action module, we can perform placement based on the result of the AHP system considering the target machine. As a result of our calculation, we can select the target machine. In the next section, we will explain the detailed proposed scheme by the step.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Metric** & **Explanation** & **Optimization Behavior** & **Previous Research** \\ \hline \multirow{3}{*}{VM cost (VC)} & The operation cost of using the & & \\  & VMs when services are & Minimize VC & [12; 25; 34] \\  & executed in CDC & & \\ \hline \multirow{2}{*}{PM cost (PC)} & The cost of using the PM in the & & \\  & specific period & Minimize PC & [36; 37; 38] \\ \hline Distance between VMs & Distance between the VMs and & & \\  & and service users (DS) & the service users & Minimize DS & [39; 40] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cost metrics.
# Wireless Remote Control for Underwater Vehicles
## 5 Simulation Results  
In this section, we evaluate the designed system through simulations in order to verify whether the network can provide the QoS needed for HROV remote control. Network metrics such as throughput, packet delivery ratio, and packet delivery delay are analyzed for each data traffic. Moreover, also other metrics tailored to analyze specific application requirements are assessed, such as the packet delay variation of the video traffics and the root mean square error (RMSE) of the HROV path compared to the desired path. To analyze the impact of errors introduced by wireless communication on ROV control, we assume an optimal positioning system on-board the vehicle.  
Figure 8: Protocol stack used in the DESERT simulations.  
Figure 9 presents how the throughput of each traffic type varies with the distance between the receiver and the transmitter: the evidence is that the SRM can be supported up to a maximum distance of 40 m: from there onward, both HQ video streams and image traffics can no longer be transmitted. With the scenario considered for our simulations, this distance corresponds to the maximum range of the optical modem: we remark the importance to set into the simulator the expected conditions of the environment where the system has to be deployed because, considering waters with a different attenuation coefficient profile or different background light noise conditions, the maximum optical range can change drastically, from 100 m in deep dark waters down to 2 m when the modem is deployed 1 m below the sea surface during a sunny day.  
MRM instead can be supported up to a distance of 500 m, when low quality video can no longer be sent. According to our simulation setup that neglected the multipath effect, this distance corresponds to the maximum distance of the HF modem: also in this case, we need to remark that, considering a different environment with more multipath or different noise sources, the range can be reduced (e.g., during a sea trial in 2019, we observed a maximum range of 200 m when the modem was deployed in very shallow water). From a distance of 500 m onward up to a distance of 10 km, only LMM can be supported, as only the basic monitoring traffic and the way-point position control packets can be transmitted through the MF acoustic network. The curves corresponding to the traffic types for which packets are generated according to predefined trace files (e.g., video streams, images, joystick, and way-point position control) present some variations in the bit rate between adjacent distance windows: this happens because the considered generation files are the same for all simulation runs, and when the HROV is at a distance between 10 m and 20 m, the transmitted video frames are larger than the video frames transmitted when the HROV is at a distance between 3 m and 10 m from the control station.
# Cascading Pose Features with CNN-LSTM for Multiview Human Action Recognition
## 5 Result and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Action** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & Point & 0.92 & 0.94 & 0.93 \\ \hline
2 & Wave & 0.94 & 0.94 & 0.94 \\ \hline
3 & Jump & 0.89 & 0.93 & 0.91 \\ \hline
4 & Crouch & 0.94 & 0.95 & 0.94 \\ \hline
5 & Sneeze & 0.9 & 0.94 & 0.92 \\ \hline
6 & SitDown & 0.91 & 0.94 & 0.92 \\ \hline
7 & StandUp & 0.9 & 0.94 & 0.92 \\ \hline
8 & Walk & 0.97 & 0.94 & 0.96 \\ \hline
9 & PersonRun & 0.95 & 0.94 & 0.94 \\ \hline
10 & CellToEar & 0.95 & 0.95 & 0.95 \\ \hline
11 & UseCellPhone & 0.96 & 0.95 & 0.96 \\ \hline
12 & DrinkingWater & 0.95 & 0.94 & 0.94 \\ \hline
13 & TakePicture & 0.96 & 0.93 & 0.95 \\ \hline
14 & ObjectGet & 0.88 & 0.94 & 0.91 \\ \hline
15 & ObjectPut & 0.91 & 0.94 & 0.93 \\ \hline
16 & ObjectLeft & 0.94 & 0.94 & 0.94 \\ \hline
17 & ObjectCarry & 0.98 & 0.94 & 0.96 \\ \hline
18 & ObjectThrow & 0.9 & 0.94 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification report of proposed deep learning model (Precision, Recall and F1-Score) using MCAD Dataset. Figure 6: Confusion Matrix using MCAD Dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Action** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & Check-watch & 0.93 & 0.92 & 0.92 \\ \hline
2 & Cross-arms & 0.89 & 0.92 & 0.90 \\ \hline
3 & Get-up & 0.90 & 0.92 & 0.91 \\ \hline
4 & Kick & 0.91 & 0.92 & 0.91 \\ \hline
5 & Pick-up & 0.90 & 0.94 & 0.91 \\ \hline
6 & Point & 0.90 & 0.93 & 0.91 \\ \hline
7 & Punch & 0.89 & 0.92 & 0.91 \\ \hline
8 & Scratch-head & 0.90 & 0.91 & 0.91 \\ \hline
9 & Sit-Down & 0.90 & 0.91 & 0.91 \\ \hline
10 & Turn-Around & 0.91 & 0.91 & 0.91 \\ \hline
11 & Walk & 0.94 & 0.92 & 0.93 \\ \hline
12 & Wave & 0.97 & 0.91 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification report of proposed deep learning model(Precision, Recall and F1-Score) using IXMAS dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Accuracy** \\ \hline Shape Features [41] & 89.75 \\ \hline LBP [42] & 80.55 \\ \hline Motion Features [43] & 83.03 \\ \hline Shape features [44] & 85.8 \\ \hline Shape Features (3D) [45] & 90.91 \\ \hline Proposed method & 91.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Multi-view HAR on IXMAS dataset. Figure 9: Confusion Matrix using IXMAS Dataset. We also performed a complexity analysis between our proposed method and other existing methods by leveraging the size of the feature dimension. Table 8 lists the comparison with both the handcrafted and DL methods. The table clearly indicates that by far, the proposed method outperformed other existing methods in terms of the feature dimensions. Our approach uses only 50 features in total which is significantly small compared to other DL approaches.