esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Human Activity Recognition Using Machine Learning and Wearable Sensor Data
## 3 Methods
### Classification Model Based on Integer Programming  
Integer programming is a specialized problem in the field of mathematical optimization, where all decision variables are required to be integers. It includes two main types: pure integer programming (where all variables are integers) and mixed integer programming (where only some variables are integers, while others can be continuous) [21]. Integer programming is used to solve optimization problems where variables can only take discrete values, typically non-negative integers. It is applicable to practical problems involving discrete selection, arrangement, or combination, such as task assignment, network design, and more. The procedure for integer programming is as follows:  
* Define the problem: clearly identify the optimization objective and constraints. For example, the objective could be to minimize costs or maximize benefits, and the constraints could be limitations such as resources, time, or capabilities.
* Determine variables: define the decision variables in the problem and ensure that their values are integers.
* Establish the objective function: construct a mathematical expression to represent the objective to be optimized. For instance, \(Z=c_{1}*x_{1}+c_{2}*x_{2}+\ldots+c_{n}*x_{n}\), where \(c_{1},c_{2},\,\ldots,\,\,c_{n}\) are coefficients, and \(x_{1},\,x_{2},\ldots,\,\,x_{n}\) are decision variables.  
Set up constraints: write down the limiting conditions to ensure that the solution satisfies all practical constraints. For example, \(a_{1}*x_{1}+a_{2}*x_{2}+\ldots+a_{n}*x_{n}\ \leq\ b\), where \(a_{1},a_{2},\ \ldots,\ a_{n}\) are coefficients and b is the upper limit of the constraint.
* Select a solution method: genetic algorithm is used to find feasible solutions close to the optimal solution.
* Result verification and analysis: verify whether the obtained solution satisfies all constraints and assess its optimality, and evaluate the practical effectiveness of the solution to ensure it meets the actual requirements within the context of the problem.  
Here, the decision variable is represented by a binary matrix X to indicate the classification situation of data points. Here, \(x_{ij}\) is a binary variable. If data point \(i\) belongs to category \(j\), then \(x_{ij}=1\); otherwise, \(x_{ij}=0\). The objective function is built as follows. For category \(k\), define the sum of its similarities as  
\[Sum_{k}=\sum_{i=1}^{60}\sum_{j=1}^{60}S_{ij}X_{ik}X_{jk}, \tag{3}\]  
Therefore, the overall objective function is  
\[Maximize\sum_{k=1}^{12}Sum_{k}, \tag{4}\]  
Then, the constraints were set up. Each data point can only belong to one category:  
\[\sum_{k=1}^{12}X_{ik}=1,\ for\ i=1,2,\ldots,60 \tag{5}\]  
Each category must exactly contain 5 data points:  
\[\sum_{k=1}^{60}X_{ik}=5,\ for\ i=1,2,\ldots,12 \tag{6}\]  
Therefore, by solving this integer programming model, the recognition of the human activities represented in the known dataset can be achieved.
## 4 Time Parameters
### Other Parameters  
**Lacunarity.** Lacunarity was initially conceived to characterize fractals (Mandelbrot [26], Lin and Yang [27], Gefen, Meir, and Aharony [28]), as it reveals variations in their space-filling structures, with lower lacunarity implying greater visual uniformity. In our case, lacunarity is used to quantify heterogeneity in the timing of showers. We use the gliding box method described in [PERSON] and Cloitre [29]. We consider a gridded timeline, with each cell corresponding to 1 ns. If any pixel has time tag \(t_{p}\), the cell corresponding to \(t_{p}\) on the timeline is assigned a 1, otherwise the cell is marked with a zero. A sliding box of size \(n_{M}\) (\(>\)4, it can be adjusted to optimize the result) is shifted over the timeline grid, and at each step, the mass M of the box (i.e., the number of non-zero cells) is recorded. The frequency distribution of M is converted into a probability distribution \(P_{M}\) across the number of gliding boxes, and the lacunarity is computed using the variance and mean of the number of full cells per gliding box, as  
\[T\_LAC=\frac{\text{Var}(\text{P}_{\text{M}})}{\text{Avg}(\text{P}_{\text{M}}) ^{2}}+1 \tag{5}\]  
This results in values between 1 and \(\infty\), with 1 indicating a uniform dispersion of full cells at a given scale, and values different from 1 signifying non-uniformity in the distribution. Our study considers three distinct types of lacunarity to accommodate the nature of our data. First, we introduce binary lacunarity (T_LAC), where cells can only have values of 0 or 1. Furthermore, we explore stretched lacunarity (T_LAC_S), where values greater than one are spread across multiple cells, reflecting a stretched representation. Finally, we employ weighted lacunarity (T_LAC_W), preserving cells with values greater than one in their original form (i.e., assigning to each cell \(t_{p}\) a value equal to the number of pixel with time tag \(t_{p}\)).  
**Temporal coherence.** It is a measure of the monotonicity of the shower evolution in space and time. Pixels are arranged in a time-ordered sequence (earliest to latest). For each of them, we evaluate the spatial distance from the one with the earliest time. To this distance (\(d_{p}\)), we assign a negative sign if it is lower than the one evaluated for the previous pixel, otherwise, \(d_{p}\) is defined as positive. The parameter of interest is then defined as \(T\_COHER=\sum d_{p}\),  
Figure 3: \(Cont.\)  
Figure 2: Distribution of selected time parameters in the Gamma-ray (black curve) and proton (orange curve) samples in different SIZE ranges.
# Effect of Chitosan on the Removal of Different Types of Tannins from Red Wines
###### Abstract  
(1) Background: most premium red wines are rich in tannins but the effect of chitosan on these macromolecules is unknown. In this work, the effect of a treatment with 0.5 g/L of chitosan on red wines, W, enriched with condensed tannins, CT, elaligatninnins, ET, and gallotannins, GT, was evaluated. In addition, to understand if the effect of C was stable during wine evolution, treated wines underwent an oxidative stress simulating an exposure to 18 mg/L of oxygen. (2) Methods: parameters describing the reactivity of phenolic compounds were determined: iron reactive phenolics, BSA reactive tannins, vanillin reactive tannins, and the saliva precipitation index. Individual anthocyanins, total and polymeric pigments and chromatic characteristics were evaluated to determine the influence of each treatment on colour parameters. (3) Results: a decrease in BSA reactive tannins after the addition of C was detected for all wines and the effect persisted after oxidation. W and CT wines previously treated with C and oxidized showed a significant decrease in the reactivity towards salivary proteins. C caused a lower formation of polymeric pigments in all wines. (4) Conclusion: these results suggest a possible use of C to treat wines very rich in condensed tannins and excessively astringent.  
chitosan: Tannins; protein reactivity; salivary proteins; red wine; astringency; gallotannins; condensed tannins; elaligatnins
## Materials and Methods
### Measurement of NO Production on LPS-Stimulated RAW 264.7 Cells  
NO formation was measured in cultured RAW 264.7 cells. RAW 264.7 cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), penicillin (100 units/mL), and streptomycin (100 \(\upmu\)g/mL) and incubated at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\) of humidified air. The cells were plated in a 96-well plate and then incubated for 24 h. The cells were pre-treated with various concentrations of compounds (6.25\(-\)50 \(\upmu\)M) for 2 h, and then incubated in the medium with 1 \(\upmu\)g/mL of LPS in the presence or absence of test samples for an additional 18 h. The media were collected and analyzed for nitrite accumulation by the Griess reaction. Briefly, 100 \(\upmu\)L of Griess reagent--0.1% \(N\)-(1-naphthyl)ethylenediamine dihydrochloride in H\({}_{2}\)O and 1% sulfanilamide in 5% H\({}_{3}\)PO\({}_{4}\)--was added to 100 \(\upmu\)L of each supernatant from LPS or sample-treated cells in 96-well plates. The absorbance was measured at 540 nm using an ELISA reader, and the nitrite concentration was determined by comparison with a sodium nitrite standard curve. The percentage inhibition was expressed as [1 \(-\) (NO level of test samples/NO level of vehicle-treated control)] \(\times\) 100. The IC\({}_{50}\) value, the sample concentration resulting in 50% inhibition of NO production, was determined by non-linear regression analysis (% inhibition versus concentration).
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 2 Background Study  
The following subsections outline the theoretical background regarding (1) the evolution of the HF communications, (2) NVIS history and current characteristics, and (3) the SC-FDE modulation to be analyzed.
## 5 Discussion
SPR repair associated with groove deepening showed a higher rate of return to sport in patients treated with isolated SPR repair, making it the recommended treatment in the sports population [19; 35; 37]. Although this result is based on limited evidence due to a lack of high-quality studies. In this study, we compared five different types of treatment for peroneal tendon dislocation. Group A: patients treated with SPR repair alone [1; 24; 26; 32; 34; 38];  
GROUP A+: patients treated with groove deepening using low-profile snap-off screws [36];  
GROUP B: patients treated with groove deepening and SPR repair [16; 23; 25; 27; 28; 29; 31; 34; 35; 37]. A few studies reported on patients treated with bony procedures (GROUP C) [32; 33] and rerouting procedures (GROUP D) [30]. The group that reported the highest improvement in the AOFAS score is GROUP B. The group that reported the worst improvement in the AOFAS score is GROUP A+--preoperative AOFAS: 69.96 \(\pm\) 13.14 (45 to 88); postoperative AOFAS: 87.72 \(\pm\) 10.13 (58 to 100). There were 105 out of 171 patients who rated their satisfaction as excellent, 50 out of 171 as good, and 15 out of 171 as fair or poor; 1 out of 171 was uncertain. The study that reported the best improvement of the VAS scale is by [PERSON] et al., where the preoperative VAS scale was 7.4 and the postoperative VAS scale was 1.4 [33]. The groups with the highest rate of return to sport are GROUP A and GROUP B (about 100% in both groups), but the same groups reported the highest number of cases of redislocation: GROUP A (three out of seven cases of redislocation) and GROUP B (three out of seven cases of redislocation). The surgical management of peroneal tendon dislocation has only been the subject of one previous systematic review [65]. A different study by [PERSON] et al. determined that SPR repair is the best surgical remedy for peroneal tendon subluxation, but because it was not founded on a methodical evaluation of studies that had been collected, there was insufficient evidence to support their conclusions [3]. This study is not without limitations; in fact, we have a low number of high-quality studies, which means low reliability of the results obtained. Moreover, we have a risk of selection bias due to a lot of studies with a high prevalence of lateral ankle comorbidities among the patients enrolled.
## 3 RTI Data Acquisition Methods
### Robotic Methods
#### 3.7.1 Unmanned Aerial Vehicles  
Unmanned Aerial Vehicles (UAVs) have become increasingly popular for tunnel inspection owing to their low cost designs, programmability and exceptional manoeuvrability, which has motivated in excess of $4 billion global investment in UAV technology development for infrastructure inspection [124]. However, practical performance of current UAVs remains limited by poor onboard charge retention [125]; stabilisation challenges from near-wall turbulence and common dependency on GPS. Note that being subterranean, Global Positioning Systems (GPS) typically struggle to operate reliably in tunnels. [126; 127]. Although, we did find considerable recent research applying collision-aversion protocol [128; 129] and'smart pathfinding' [130; 131; 132] (e.g., PLUTO [133]) to develop autonomous UAVs [134; 135; 136; 137]. However, backup pilots remain necessary which add costs and safety-risks [137]. Furthermore, no commercially available autonomous UAV has yet to be developed specifically for RTSSI, despite similar systems existing for hydroelectric pensotck surface level inspection [138].  
Being airborne, UAVs could quickly transport RTSSI sensors where articulated booms cannot reach, for instance UAV-SWIRL hovers inside vertical ventilation shafts [125; 139]. However, most systems still favour Optical Photometry and LiDAR sensing [140; 141; 142], permitting only implicit subsurface measurements. Of novel importance, we discuss several significant exceptions developed since 2015. These include development of new UAV-mounted GPR prototypes [143; 144; 145]; we found one commercial system [146] capable of 10 m penetration, however it is unclear if this incorporates UAV altitude.  
In addition, hybrid locomotion UAVs now encompass:  
* **Fixed Anchor-Point Docking**[147; 148; 149; 150]--Sustains surface contact for IST and UST but requires highly involved pre-installation of anchors;
* **Pivoting RTUs**[151] (Figure 7)--Tracks enable uninterrupted contact and continuous one-way surface coverage but increase weight and power drain, limiting survey completeness;
* **Negative Pressure Wall-Climbers**[152; 153]--Faster than track-based RTUs but require large flat contact surfaces, hence curved tunnel geometry risks UAV slip and hazardous control loss;
* **Fully Actuated Configurations**[154; 155]--Provides best all-round solution, providing unrestricted multidirectional movement even on curved surfaces, but is liable to near-surface turbulence.  
Figure 7: A recent novel innovation in hybrid locomotion UAVs. The pivoting traction crawler UAV performs IST on angled infrastructure surfaces inaccessible to engineers without gantries [151].
## 5 Results and Discussion
### A. ANN Model  
Two R packages, namely, Keras on TensorFlow and neuralnet, were used to build the ANN model. Keras is a high-level neural network application-programming interface (API) written in Python, and neuralnet is a well-known ANN package written in R. Keras runs on TensorFlow for the development and implementation of deep-learning models. TensorFlow is an open-source platform for machine learning developed by the GoogleBrain Team. A Keras sequential model with the rectified linear unit (Relu) activation function and neuralnet were used to determine the best model for predicting BTS.  
Loss function (MSE), epochs, batch size, and learning rate are the training parameters for the ANN sequential (ANNS) model. The epochs indicate the number of times the dataset is passed through the network. The best ANNS model identified by the accuracy measurement results was the model with a learning rate of 0.01%, a hidden-dim value of 2 with three and two neurons, respectively, the number of epochs as 100, a batch size of 16, and a validation split of 0.20. The model had 20 parameters--9 for the first hidden layer, 8 for the second hidden layer, and 3 for the output layer. The model was trained very well with the data, and the training error rate decreased very sharply, as seen in Figure 4; both MSE and MAE decreased exponentially before 60 epochs and stabilized thereafter.  
The best ANN Neural net (ANNN) model had the same number of hidden layers as ANNS. The coefficients of determination (R\({}^{2}\)) for the two models, ANNN and ANNS, were 62% and 69%, respectively.
## 4 Our Findings
### Distribution of Attack Categories  
In our study, we determined the distribution of attack types and their frequencies across the included studies. Also, we categorized the attacks into 42 distinct categories based on their characteristics and impact areas. The category \"Smart Contract Vulnerability\" received the greatest attention, with 48 mentions, emphasizing its importance as a significant area of concern in blockchain and related technologies. This was followed by \"Financial Fraud\" (18) and \"Privacy Violation\" (19), both of which received significant attention, emphasizing the need for tackling financial and privacy threats in digital ecosystems. Other important categories are \"Network Attack\" (10), \"Cryptographic Attack\" (9), and \"Denial of Service\" (8), emphasizing the importance of network security, cryptographic vulnerabilities, and service disruption threats. Additionally, the term \"IoT Vulnerability\" occurred 22 times, showing a rising level of worry about the security of Internet of Things (IoT) systems. Categories such as \"Authentication/Authorization\" (17) and \"Data Security\" (4) highlight the significance of safe access control and data protection techniques. The inclusion of less common but significant categories, such as \"Supply Chain Attack\" (2), \"Threat Intelligence\" (1), and \"Trusted Execution Environment\" (1), implies that a wide variety of security concerns is being investigated. Overall, these findings point to a research environment that emphasizes smart contract vulnerabilities, financial fraud, and privacy concerns while also addressing a wide range of additional security threats and weaknesses in blockchain and associated technologies. Figure 4 demonstrates the distribution of attack categories.  
Figure 3: Publication frequency based on included studies.
## 4 Conclusions  
In this study, the effects of the precipitation of the Mg\({}_{2}\)Si phase on thermal diffusivity were investigated according to the aging temperature of a binary Al-Mg\({}_{2}\)Si specimen. The following conclusions were drawn:  
1. The thermal diffusivity curves of the as-quenched and aged samples can be divided into three zones. The thermal diffusivity of the first zone is determined by the amount of residual solute after heat treatment and the additive content of alloys. The decreases of the residual solute increased thermal diffusivity at zone 1 after heat treatment. In addition, the Mg\({}_{2}\)Si content increased with the decrease of thermal diffusivity.
2. Zone 2 can be changed by an additional aging treatment condition; this zone was also affected by solute atoms and their precipitation temperatures. The increment of thermal diffusivity strongly depended on the amounts of \(\beta^{\prime\prime}\) and \(\beta^{\prime}\) precipitate in this zone. The aged samples at 240 \({}^{\circ}\)C had larger thermal diffusivity values than the others.
3. The temperature of the final zone was decided by the end temperature of Mg\({}_{2}\)Si precipitation. In this zone, thermal diffusivity decreased with the increase in temperature because of solvent re-melting, lattice vibration, etc. Thermal diffusivity was affected by thermal content in this zone only. The aging condition did not affect the thermal diffusivity of zone 3.  
Conceptualization, Y.-M.K. and S.-W.C.; methodology, [PERSON]-C.K. and S.-K.H.; formal analysis, [PERSON]-M.K. and [PERSON]-[PERSON]; investigation, Y.-M.K.; resources, [PERSON]-M.K. and S.-K.H.; writing--original draft preparation, Y.-M.K.; writing--review and editing, S.-W.C. and S.-K.H.; project administration, C.-S.K.  
This research received no external funding.  
We gratefully acknowledge the support provided by the Ministry of Trade, Industry, and Energy (MTIE) of the Republic of Korea for funding our research program on the development of convergent manufacturing technology for IE4-class electric motors. The present research is part of the above-mentioned program.  
The authors declare no conflicts of interest.  
Figure 10: The heat flow curves of Al-3.5 Mg\({}_{2}\)Si samples according to increase of aging treatment temperature: (**a**) 180 \({}^{\circ}\)C, (**b**) 200 \({}^{\circ}\)C, (**c**) 220 \({}^{\circ}\)C, and (**d**) 240 \({}^{\circ}\)C.
Quantitative analysis is performed on the deformation results of the Shanxi section of the Ming Great Wall in China to assess the future development trend of deformation, while qualitative analysis is conducted to create a deformation risk map. The research results demonstrate that SBAS-InSAR technology based on SAR satellite imagery possesses the capability to monitor the surface deformation of linear cultural heritage, particularly in situations where traditional detection methods such as field investigations and measurements present challenges. These results not only provide technical support for monitoring and preserving the Ming Great Wall in Shanxi Province but also establish a theoretical foundation for large-scale cultural heritage deformation monitoring worldwide.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 4 Network Performance Optimization Based on Dynamic Game Theory
### Network Performance Model
#### 4.1.2 Network Saturation Performance Model  
Stations send data packages using static \(p\)-persistent CSMA, which is relatively simple for network operations. Congestion is also not considered. Stations monotonically send data packets based on the \(p\)-persistent CSMA, which has a low bandwidth utilization.  
The bandwidth utilization \(\eta(r_{s})\) is expressed as follows:  
\[\eta(r_{s})=S(r_{s})/B \tag{19}\]  
where \(B\) denotes the communication bandwidths. The saturation throughput \(S(r_{s})\) is expressed as:  
\[S=\frac{E[P]}{T_{s}-T_{c}+\frac{T_{l}(1-R_{p})/P_{p}+T_{c}}{P_{s}}} \tag{20}\]  
where \(E[P]\) denotes the average packet length; \(P_{tr}\) is the probability of at least one transmission occurring in the considered slot time; \(P_{s}\) is the probability that a transmission will be successful; \(T_{s}\) and \(T_{c}\) are the average times that the channel is sensed to be busy because of either a successful transmission or a collision, respectively; and \(T_{l}\) denotes the duration of an empty slot time. In terms of \(p_{0}\), these are:  
\[P_{tr}=1-\left(1-p_{0}\right)^{n} \tag{21}\]  
\[P_{s}=np_{0}(1-p_{0})^{n-1}/P_{tr} \tag{22}\]  
\[T_{s}=PRS_{0}+PRS_{1}+T_{FRAME}+RIFS+T_{ACK}+CIFS \tag{23}\]  
\[T_{c}=PRS_{0}+PRS_{1}+T_{FRAME}+CIFS \tag{24}\]  
\(\eta(r_{s})\) is a strictly concave function that changes with probability \(p_{0}\) when the station numbers are greater than one. Refer to the parameters in Table 1, which are depicted in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation area & 800 \(\times\) 800 m\({}^{2}\) \\ Station numbers & 30 \\ transmission range & 200 m \\ Bandwidth & 1.8-20 MHz \\ PRS0 & 35.84 us \\ PRS1 & 35.84 μs \\ RIFS & 140 μs \\ CIFS & 100 μs \\ PHY transmission rate & 1 Mbps \\ Data packet time & 6.65 ms \\ Payload time & 6 ms \\ ACK length & 16 B \\ Payload length & 128 B \\ Data packet length & 136 B \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network parameters.  
The maximum bandwidth utilization reaches 0.8% bit/s/Hz at a PHY of 1 Mbps for a single station accesses channel. The collision probability also increases with an increasing number of stations. The bandwidth utilization of a single load is 1.1 times the maximum load.