esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Preparation of PNAMAm Microspheres and MSP@PNAMAm Core-Shell Microspheres
According to the analysis results, the magnetic composite microspheres, which were prepared with different feeding ratios of NAMAm-\(p\) and MBA and with the same solid content (0.5%), had almost similar shell thickness (Figure S4, Supplementary Material). Through the comparison of the weight changes before and after deprotection, the alkoxyamine group densities were calculated to be 1.49, 1.12, 0.95 and 0.77 mmol/g for the MSP@PNAAM-1, MSP@PNAAM-2, MSP@PNAAM-3, and MSP@PNAAM-4, respectively. In addition, the copolymerization properties of NAMAM-\(p\) were also investigated. NIPAm, AA, and HEMA were used as the second monomers. From the TEM images in Figure 6, the monomer NIPAm has the best performance in copolymerization with NAMAM-\(p\), resulting in a uniform and thick polymer shell. The relative ratio of PNAAM-\(p\) and PNIPAm could be adjusted by varying the feeding monomer ratios, which was also confirmed by FT-IR (Figure S5, Supplementary Material). Since the monomers of AA and HEMA tend to polymerize on their own, the resultant polymer shells were very thin (Figure 6b,c), but FT-IR spectra (Figures S6 and S7, Supplementary Material) validated that the shells were composed of PNAAM-\(p\)_-co_-PAA and PNAAM-\(p\)_-co_-HEMA copolymers, respectively. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Sample** & \(m_{\text{(MSP)}}\)**mg** & \(m_{\text{(NAAMm-p)}}\)**mg** & \(m_{\text{(MBA)}}\)**mg** & \(W_{1}\) & \(W_{2}\) & \(D\)**mmol/g** \\ \hline MSP@PNAAM-1 & 25 & 80 & 20 & 78.30\% & 65.40\% & 1.49 \\ MSP@PNAAM-2 & 25 & 75 & 30 & 72.40\% & 63.10\% & 1.12 \\ MSP@PNAAM-3 & 25 & 60 & 40 & 76.50\% & 67.96\% & 0.95 \\ MSP@PNAAM-4 & 25 & 50 & 50 & 73.80\% & 67.00\% & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recipe, TGA data and the calculated alkoxyamine group density of the MSP@PNAAM samples with different feeding amount of MBA. Figure 5: TEM images of MSP@PNAAM with different thickness of polymeric shell prepared with different solid contents of (**a**) 0.25%; (**b**) 0.375%; (**c**) 0.5%; (**d**) 0.625%, the scale bar is 100 nm; (**e**) DLS results of (i) MSP and MSP@PNAAMm prepared with different solid contents; (ii) 0.25%; (iii) 0.375%; (iv) 0.5%; (v) 0.625%.
## 1 Introduction
To suitably realize WSN-based SHM for real-time applications, data compression technology is required to acquire and transmit the dynamic response efficiently, together with the performance improvement of the sensor node to the level of a high-performance PC to record and store the dynamic response of the civil structure stably. Recently, [PERSON] et al. [25; 26] developed a wireless sensor node using an analog filter bank by several neuron boards, and performed a real-time response test in a model structure. The studies show that since the CPU and wireless sensor nodes are designed specifically for the lab, thus being inevitably limited in performance and capacity, exact data acquisition will require an increment of the sampling rate or an expansion of the measurement channels. When the amount of data becomes larger, their performance becomes limited. Furthermore, although the performance of the neuron boards used for the analog filter bank was tested by performing a response experiment, the neuron board would need to be re-designed, re-built, and re-tested because of the changes in the measurement targets for the civil structure. Therefore, flexible and efficient SHM is limited by the practicality and usability of hardware-type neuron boards for realizing the analog filter bank. In this context, the previous study [1; 27] developed a CAFB (cochlea-inspired artificial filter bank), which is a dynamic data compression technology using the D-S/W-D (digital-software-design) method. A CAFB is a digital filter bank which consists of n number of band-pass filters to select and compress only interesting ranges of frequency signals from an entire response of a structure. A previous study optimized the CAFB using an El-Centro seismic waveform to selectively acquire raw signals of under 10 Hz, which is required for SHM on long and large-scale civil structures. The CAFB optimized for the El-Centro seismic waveform was designed with six filters, a spacing of 1.0 Hz, and a bandwidth of 0.6 Hz [27]. Then, by executing a numerical study on CAFB, its validity as a dynamic data compression technology has been proven. This study, using CAFB optimized in the previous study [27], is to experimentally evaluate the dynamic acceleration response performance of civil structures. To do that, an intelligent data acquisition (IDAQ) system was developed based on digital software design by embedding the previously-developed CAFB into the wireless sensing system. For the IDAQ system developed here, the modal test was performed by application to a model cable-stayed bridge; the dynamic characteristics were analyzed from the acquired dynamic response. Furthermore, the result was evaluated in conjunction with the result of the wire measurement. The results show that the CAFB-based wireless sensing system realized in this study has excellent performance and stability compared to wired sensing systems. Additionally, the CAFB was effective in determining the accurate, compressed structural response around the frequency band of interest.
## 4 System Model, Algorithms, and Security Model
### Algorithms
#### 4.2.1 System Initialization  
GlobalSetup(\(\lambda\)) \(\rightarrow\) GPP: The algorithm uses the input \(\lambda\) which is also called the security parameter. After the setup executes, global public parameters GPP are generated.  
CASetup(GPP) \(\rightarrow\) (MPK, MSK): The central authority(CA) executes the CASetup algorithm. It outputs public key MPK and secret key MSK. All the authorities use MPK for verification purposes.  
AASetup(GPP, k, U\({}_{\text{k}}\)) \(\rightarrow\) (APK\({}_{\text{k}}\), ASK\({}_{\text{k}}\)): Every authority AA\({}_{\text{k}}\) present in the system executes this algorithm, where the inputs are GPP and its attribute domain is called \(U_{k}\). No two authorities have a common attribute domain, which means for i \(\
eq\) j, \(U_{i}\cap U_{j}=\varnothing\). This algorithm produces public key APK\({}_{\text{k}}\) and secret key ASK\({}_{\text{k}}\).
## 2 Materials and Methods
### Energy Measurement  
We used a custom USB power meter developed in [36] to measure and record the energy consumption of the sensor nodes. The custom power measurement device was based on the intrusive direct measurement method and was composed of an INA226 module with a 100 m\(\Omega\) shunt resistor, STM nucleo-32 F303k8 microcontroller, 128 \(\times\) 64 OLED display, SD card and USB port. The block diagram of the USB power meter is presented in Figure 2. We developed this device for two main reasons: (1) to enable us to measure very low currents in the \(\mu\)A range, particularly the current consumption of the node when the ESP32 is operating in deep sleep mode, and (2) to be able to monitor and store the power consumption of the nodes without being physically present (i.e., recording of power measurements collected periodically over a long period of time).  
The energy consumption metric represents the total amount of energy spent by the sensor node while performing sensing, processing and communication operations. It is expressed as:  
\[E=\sum_{k}(E_{MCU,k}+E_{TRX,k}+E_{SEN,k}) \tag{1}\]  
where E\({}_{(MCU,k)}\), E\({}_{(TRX,k)}\),and E\({}_{(SEN,k)}\) are the energy consumed by the sensor node's processing unit, communication unit, and sensing unit, respectively, at time step k.  
The energy consumed by the constituent parts of the sensor node are calculated from the integration of the product of current and voltage over the duration of operation. Thus, the expressions of the energy consumption of the sensor node's constituent parts are given as follows.  
\[E_{MCU,k}=I_{MCU}\times V_{MCU}\times t_{proc} \tag{2}\]  
where I\({}_{MCU}\) is the current consumption of the MCU, V\({}_{MCU}\) is the operation voltage of the MCU, and t\({}_{proc}\) is the time used by the MCU for processing.  
\[E_{SEN,k}=I_{SEN}\times V_{SEN}\times t_{sen} \tag{3}\]  
where I\({}_{SEN}\) is the current consumption of the sensor, V\({}_{SEN}\) is the operation voltage of the sensor, and t\({}_{sen}\) is the amount of time the sensor is active during the sensing process.  
\[E_{TRX,k}=\frac{L}{R}V_{TRX}(I_{TX}+I_{RX}) \tag{4}\]  
where L is the length of the packet transmitted/received, R is the data rate, and V\({}_{TRX}\) is the operational voltage of the transceiver, while I\({}_{TX}\) and I\({}_{RX}\) are the current consumption of the transceiver when operating in the transmit and receive modes, respectively.
## 2 Materials and Methods
### Weight Gain and Immune Organ Index Analysis  
The body weight of each mouse was measured on days 0 and 14. The percentage of weight gain was calculated using the following formula:  
Weight gain (%) = ([final weight \(-\) initial weight] \(\times\) 100/initial weight)  
On day 14, the thymus and the spleen were collected and weighed. The immune organ index was determined using the following formulas:  
Thymus index = thymus weight (mg)/body weight (g)
# Random Walker Coverage Analysis for Information Dissemination in Wireless Sensor Networks
## Appendix E Equivalence of Equations (5) and (7)  
If \(r_{c}\) is so large that the network topology becomes the complete graph, i.e., the number of neighbor nodes \(\approx N\), the \(P(r_{c})\approx 1\), assuming large \(N\), then Equation (5) becomes:  
\[C(t) =1+C(t-1)\left(1-\frac{1}{N}\right)\] \[=1+\sum_{n=1}^{t}\left(1-\frac{1}{N}\right)^{n}. \tag{10}\]  
Because:  
\[C(0) =1\] \[C(1) =1+C(0)\left(1-\frac{1}{N}\right)\] \[=1+\left(1-\frac{1}{N}\right)\] \[=1+\sum_{n=1}^{1}\left(1-\frac{1}{N}\right)^{n}, \tag{11}\]  
and if:  
\[C(t-1) =1+\sum_{n=1}^{t-1}\left(1-\frac{1}{N}\right)^{n}\] \[C(t) =1+C(t-1)\left(1-\frac{1}{N}\right)\] \[=1+\left(1+\sum_{n=1}^{t-1}\left(1-\frac{1}{N}\right)^{n}\right) \left(1-\frac{1}{N}\right)\] \[=1+\sum_{n=1}^{t}\left(1-\frac{1}{N}\right)^{n}. \tag{12}\]  
In Equation (10), if: \(\left(1-\frac{1}{N}\right)\) is replaced by: \(\rho\) (\(0<\rho<1\)):  
\[C_{t} =1+\sum_{n=1}^{t}\left(1-\frac{1}{N}\right)^{n}\] \[=1+\sum_{n=1}^{t}\rho^{n}\] \[=1+\rho+\rho^{2}+\cdots+\rho^{t}\] \[=\frac{\rho^{t+1}-1}{\rho-1}\] \[=\frac{\left(1-\frac{1}{N}\right)^{t+1}-1}{-\frac{1}{N}}\] \[=N\left(1-\left(1-\frac{1}{N}\right)^{t+1}\right). \tag{13}\]In order to prove that Equation (A11) is equivalent to Equation (7), it is sufficient to show that: \(\left(1-\frac{1}{N}\right)^{t+1}\) and: \(\mathrm{e}^{-\frac{t}{N}}\) are equal. If they are equal:  
\[\ln\left(1-\frac{1}{N}\right)^{t+1}=\ln\left(\mathrm{e}^{-\frac{t}{N}}\right) \Leftrightarrow\left(t+1\right)\ln\left(1-\frac{1}{N}\right)=-\frac{t}{N}\]  
therefore, if \(\left(1-\frac{1}{N}\right)^{t+1}\) and \(\mathrm{e}^{-\frac{t}{N}}\) are equal, the following should be true.  
\[\ln\left(1-\frac{1}{N}\right)^{N}=-\frac{t}{t+1}\]  
but:  
\[\lim_{t\rightarrow+\infty}\frac{-t}{t+1}=-1\]  
and:  
\[\lim_{N\rightarrow+\infty}\ln\left(1-\frac{1}{N}\right)^{N}=\lim_{N \rightarrow+\infty}\frac{\ln\left(1-\frac{1}{N}\right)}{\frac{1}{N}}=\lim_{ x\to 0}\frac{\ln\left(1-x\right)}{x}=-1.\]  
From the above, it follows that Equation (5) is equivalent to Equation (7) in marginal conditions.
## 4 Model and Control of PZT
### Design of Parallel Controller  
The parallel controller for a piezoelectric actuator is based on the prediction of hysteresis nonlinearity using the hysteresis inverse model predictor, and on the digital PID adjuster. The hysteresis inverse model predictor is used to compensate the nonlinearity of the PZT. A block diagram of the tilt vibration isolation control system, composed of a compensation control loop and a PID control loop, is shown in Figure 5. In the close-loop system, an external tilt signal, \(\theta\), is compared to the feedback tilt signal, \(\theta_{f}\), generated by the piezoelectric actuator, and the VP tiltmeter, including the VP and the capacitance transducer, translates the tilt error, \(\Delta\theta\), into the error of displacement, \(e_{y}(t)\), which is sampled by the A/D converter. The discrete displacement, \(e_{y}(k)\), is simultaneously sent to the digital PID adjuster and the hysteresis inverse model controller, based on the inverse \(y_{1}^{-1}\) of the hysteresis model. The digital PID numerical adjuster and the inverse model predictor produce the feedback voltages \(\Delta u(k)\) and \(u_{c}(k)\), respectively. The A/D converter converts the digital voltage \(u(k)\) into the analog voltage \(u(t)\), which is sent to the driving circuit of the PZT to produce the aforementioned tilt signal, \(\theta_{f}\).  
Figure 4: The inner loop model curves of the piezoelectric actuator.  
The algorithm of the digital PID controller is given by  
\[\Delta u(k)=K_{p}\left\{e_{y}(k)+\frac{T}{T_{i}}\sum_{i=1}^{k}e_{y}(i)+\frac{T_{d} }{T}\big{[}e_{y}(k)-e_{y}(k-1)\big{]}\right\} \tag{10}\]  
where \(K_{p}\) is the proportional gain, and \(T_{i}\), \(T_{d}\) and \(T\) are the integral, derivative and sampling time intervals, respectively.  
In the loop of the inverse model predictor, it is critical to calculate the compensated voltage, \(u_{c}(k)\), according to the error displacement, \(e_{y}(k)\). The inverse mathematical models of ascending and descending hysteresis are also solved directly, and the compensated voltage can be given by  
\[u_{c}(k)=\left\{\begin{array}{ll}y_{ssn}^{-1}\big{(}e_{y}\big{)},&e_{y}\in y _{ssn}\\ y_{dsn}^{-1}\big{(}e_{y}\big{)},&e_{y}\in y_{dsn}\end{array}\right. \tag{11}\]  
where \(y_{ssn}^{-1}(\cdot)\) and \(y_{dsn}^{-1}(\cdot)\) are the inverse functions of \(y_{ssn}(\cdot)\) and \(y_{dsn}(\cdot)\), respectively.  
The additional control voltage, \(\Delta u(k)\), is added to the compensated voltage, \(u_{c}(k)\), to obtain the total control voltage, \(u(k)\), which is used to drive the PZT through the driving circuit.
## 3 Geotechnical Analysis of the Dam
### Compaction Parameters
However, areas with samples 7, 8, and 10 might require more compactive effort or possibly soil mixing to achieve target densities. The wide OMC range (10.27% to 18.94%) underscores the importance of precise water content control during construction. [PERSON] et al. found that compacting soil just 2% above or below OMC can reduce shear strength by 15-20% [10]. In the Tiga Dam's downstream area, mainly where high-OMC soils are used, meticulous moisture control is essential to ensure uniform strength and prevent differential settlement. High compaction levels generally reduce permeability, which is desirable for minimizing seepage. However, the relationship between compaction and permeability is not linear. Despite lower densities, [PERSON] et al. showed that clays compacted wet of optimum often have lower permeabilities due to more dispersed structures [17]. Thus, samples 7, 8, and 10, even with lower MDDs, might provide better seepage control if compacted slightly wet of optimum. The downstream slope's stability is critically linked to fill compaction. High MDDs generally indicate higher shear strengths, improving slope stability. However, the medium-MDD, high-plasticity soils (7, 8, 10) pose a challenge. Their lower densities suggest lower shear strengths, but their high plasticity also means higher swelling potential. During wet seasons, these soils may absorb water, swell, and experience strength reduction, potentially destabilizing the slope. Well-compacted soils typically have better erosion resistance. The high-MDD samples, particularly 12 (2.21 Mg/m\({}^{3}\)), should resist surface erosion well. However, internal erosion (piping) risk remains. Compaction can reduce large pores that facilitate piping. Still, if zones of samples 7, 8, and 10 are adjacent to high-MDD zones, the hydraulic conductivity contrast could induce concentrated seepage paths, exacerbating piping risk. The compaction data suggest a strategic zoning approach for downstream rehabilitation or extension works. High-MDD soils (e.g., samples 4, 12) are suited for critical sections requiring high strength and low compressibility. Medium-MDD, high-OMC soils (7, 8, 10) could be used in less stressed areas or mixed with sandier soils to improve workability.
# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 3 Problem Formulation
### Constraints  
The constraints are discussed as follows;  
\[\sum_{i}X_{i,m}=1\quad\forall m \tag{2}\]  
\[\sum_{q}b_{i,q}=1\quad\forall i \tag{3}\]  
\[\sum_{q}qb_{i,q}=\sum_{m}X_{i,m}\quad\forall i \tag{4}\]  
\[\sum_{j}\left(Y_{ij}^{mn}-Y_{ji}^{mn}\right)=\begin{cases}X_{n,i},&\text{if }i=m, \ m\
eq n.\\ -X_{i,m},&\text{if }i=n,\ m\
eq n.\\ 0,&\text{otherwise.}\end{cases}\quad\forall mn,\forall i \tag{5}\]  
Equation (2) ensures that exactly one BBU pool is associated with every RRH. Equation (3) and Equation (4) ensure that the number of RRHs served by Pool \(i\) equals the sum of RRHs assigned to that pool \(i\). Equation (5) ensures that physical links are mapped to all virtual links connecting the RRHs to the BBU pool.  
\[X_{i,m}\leq 1\quad\forall i,\forall m \tag{6}\]  
\[K_{i}\geq\frac{\sum_{m}X_{i,m}}{M},\quad\forall m \tag{7}\]  
\[\sum_{q}b_{i,q}C_{q}\leq C\quad\forall i \tag{8}\]  
Figure 1: Routing the virtual link over physical links.  
\[\sum_{mn}Y_{ij}^{nm}\leq W \forall ij \tag{9}\] \[\sum_{ij}Y_{ij}^{nm}d_{ij}\leq D \forall mn \tag{10}\]  
Equation (6) ensures that the BBU pool for the same RRH is at different nodes. Equation (7) identifies the node as a BBU pool if it hosts one RRH. Equation (8) ensures that computation processing required by the base station hosted in a BBU pool does not exceed the computational capacity of this BBU pool. Equations (9) and (10) guarantee the link capacity and the delay requirements are met, respectively.  
The model in [46] is used to determine the computational effort needed by each cell site. This is expressed in Giga Operations Per Second (GOPS). First, we choose a statistical distribution (namely normal or uniform) to model the spatial distribution of mobile users in a given serving area. Then, we allocate the users to their serving cell sites according to a realistic user-cell site-association strategy. After that, we apply a scheduling algorithm to distribute physical resource blocks of the eNB among its attached users. Then, we calculate the computational effort per user after knowing his channel condition, used resources, modulation, code rate, and MIMO mode.
## 2 Related Works
### Model Compression and Acceleration
#### 2.1.2 Quantization  
Quantization methods are a viable approach to reduce the storage and computational costs of deep neural network models [22; 23]. Unlike pruning, which focuses on removing parameters, quantization focuses on how many bits can represent the parameters [24; 25; 26; 27]. In a deep neural network, quantization involves converting the parameter values (such as weight, activation, or inputs) from high-precision format (typically 32-bit floating-point format) to a lower-precision format [28; 29; 30; 31]. There are various quantization methods available in the literature, some of which can be applied to both training and inference stages [32; 33; 34; 35].  
In the inference stage, quantization can significantly reduce the memory storage and computational costs of the model. For instance, a deep neural network model with only 8 bits for convolutional layers and 5 bits for fully connected layers can achieve the same accuracy as its floating-point counterpart [5]. Other approaches use an 8-bit integer format for model training and inference [36].  
Some researchers have proposed using logarithmic representation for quantization, which uses a smaller number of bits (e.g., 3 bits) to represent the values in the neural network model. This technique can significantly reduce computation, storage, and hardware costs while maintaining accuracy [37].  
Recently, [PERSON] et al. proposed Quantization-aware Training (QAT) as a training technique that quantizes the model's weights and activations during training to simulate the quantization process that will be used during inference. This technique helps the model become more robust to quantization errors and achieve higher accuracy when using lower bit precision [38].  
In their recent work, [PERSON] et al. proposed Differentiable Quantization (DQ), a quantization method that optimizes a differentiable quantization layer, along with the neural network model, during training. This end-to-end approach results in improved accuracy and better performance compared to traditional quantization methods that use fixed quantization schemes [39].  
Differentiable Multi-Bit Quantization (DMBQ) allows different bit precision levels for each weight or activation element in a neural network model. The model can be trained from end to end, along with a differentiable quantization layer, allowing for optimization of the layer parameters during training. This approach leads to an increase in accuracy and a decrease in memory storage requirements compared to traditional quantization methods [40].  
Finally, binary neural networks (BNNs) are a powerful method to quantize neural network models that use only 1 bit to represent the deep neural network parameters [41]. Recent works demonstrate how to train such models with only a small accuracy degradation compared to the floating-point counterpart [42]. In this paper, we use the XNOR-Net method [10], which is a type of binary neural network. In the next section, we provide a full description and explanation of this method.
## 3 Results and Discussion
### Identification of Volatile Compounds in the Formulations
Other volatile compounds were found only in the LEE and LEE-Macro in relative low amounts (2-propyl-1-pentanol, 2,5-dimethyldecane, 3-(3,3-dimethylbutyl)cyclohexanone, 2,4,4,6,6,8,8-hHeptamethyl-2-nonene and 2-bromo dodecane). However, these compounds were not identified in the tomato fruits and tomato-processing products, and the odor type/description has not been described in the literature. The presence of different volatile compounds in the LEE-Macro with different odor types is the main reason for the undefined odor described by 15 of the volunteers. Nevertheless, three of the volunteers classified the odor of LEE-Macro as pleasant and fresh, probably due to the presence of compounds such as 6-methyl-5-hepten-2-one, D-limonene and citral, while the unpleasant odor described by four volunteers was probably caused by compounds with a musty and fatty odor (2-isobutylthiazole, n-hexanal and (E)-2-Octenal). These three mentioned compounds were not the compounds with the highest relative area but are the volatile compounds with a high odor threshold, being perceived by the volunteers in lower amount when compared with the compounds with a much pleasant odor, higher relative area and odor threshold (6-Methyl-5-hepten-2-one and D-limonene). The degradation of carotenoid materials (carotenoids and lycopene) through heat treatments originates volatile compounds that are responsible by the aroma, such as 6-methyl-5-hepten-2-one, \(\beta\)-ionone and citral [40,41]. Citral-derived compounds, such as citronellol have been identified in oleoresins of _Lyopersicum esculentum_[41] and ROMA VF varieties [39]; however, it was not identified in the GC-MS analysis in this study. Citronellyl formate is another citral-derived compounds that can be prepared by esterification of citronellol with formic acid [42], and its aroma is described a fruity, floral and rose-light odor [43]. Citronellyl formate has not been described in the fresh tomato and their processing products in the literature, but it was identified either in free LEE, LEE-ME and LEE-Macro. Its presence in the LEE may be due to the oxidative degradation of carotenoids and lycopene caused by the temperature and pressure during the extraction process, or eventually by the enzymes during the harvesting, storage or extraction of the tomato waste [44].