esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Area and Data Description  
The Voshmgir basin is located between 54\({}^{\circ}\)40\({}^{\prime}\) and 56\({}^{\circ}\)00\({}^{\prime}\) E longitude and 36\({}^{\circ}\)50\({}^{\prime}\) and 37\({}^{\circ}\)50\({}^{\prime}\) N latitude in the east-south of the Caspian Sea. It has an area of 5748 km\({}^{2}\), an elevation ranging from 2 to 2865 m, and an average slope of 2.2%. The basin is one of the main sources of irrigation water that is supplied to 25,000 hectares of farmland in Golestan province, Iran. The basin has a semi-arid climate, and the main river profile with a length of approximately 59 km is steep, which exposes it to soil erosion and flooding. Precipitation in this basin shows extreme nonstationary changes [23]. The annual mean precipitation is 540 mm, and 60% of the total precipitation occurs from January to June. The annual mean temperature is approximately 18 \({}^{\circ}\)C [24; 25]. Five rainfall stations and one runoff gauging station at the outlet of the basin were considered in this study. The location, elevation, rainfall, and runoff at the gauging stations are shown in Figure 1. The daily measured river runoff data ranged from 0 to 310 m\({}^{3}\)/s. Daily measured mean temperature data and daily tropical rainfall measuring mission satellite (TRMM_3B42_daily_v7) precipitation data were collected from 2002 to 2007 from the Iran water resources management company (IWRMC) and the GES-DISC interactive online visualization and infrastructure (GIOVANNI) websites ([[https://giovanni.gsfc.nasa.gov/](https://giovanni.gsfc.nasa.gov/)]([https://giovanni.gsfc.nasa.gov/](https://giovanni.gsfc.nasa.gov/)), accessed on 31 December 2019), respectively (Figure 2).
# Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems
## 7 Conclusions  
The vast literature about Bluetooth features a gap relative to the analysis of the latency in multi-hop communications. This QoS metric depends on several factors. Thus, this paper deals with the dependences of latency with the major factors, namely the hop count, the power mode of the nodes (sniff and park) and the period of inactivity.  
First, we implemented several Bluetooth nodes for the test bench in order to analyze the latency in the multi-hop wireless network. Furthermore, we interconnected the nodes and deployed the test bench to carry out the tests. Next, we applied a specific innovative methodology to measure the end-to-end latency along the data transportation network. In addition, the data mining of the results of the tests lead to the main findings for the end-to-end latencies with each one of the power modes, and their dependence with the hop count and period of inactivity of the mode.  
The main outcome of the paper is the set of equations obtained through linear regression to describe the low power modes in Bluetooth multi-hop wireless networks in terms of latency. To the best of our knowledge, this is the first approach to the end-to-end latency modeling in Bluetooth multi-hop communications when nodes are in sniff and park low power mode. The equations ease the calculation of the latency in communications over ACL links in scatternets when links are in park and sniff low power modes. They benefit the designers of not only home automation systems but also  
Figure 5: Curve where the latency in the sniff mode matches the one in the park mode for both directions of communication: (**a**) ping data-packets for master-to-slave communication; and (**b**) pingback data-packets for slave-to-master communication.  
wireless networks. However, the set of equations do not consider further processing in the nodes of the network, so the additional latency due to extra processing tasks must be added to the latency given by the equation in the model. Moreover, it is important to take into account that the probability for retransmissions during the multi-hop communication could not be negligible, as shown in the tests.  
Finally, in future research, it is our intention to enhance the introduced model to be more accurate by extending the topology and the network application scenario considered as well as by using devices of the most recent release of Bluetooth standard.  
The research described in this paper was included in AIRHEM IV project and financially supported by the Basque Government Research Program called Elkartek 2015 (code KK_2015/0000085).  
The authors declare no conflict of interest.
# Is It Possible to Restore a Heavily Polluted, Shallow, Urban Lake?
## 4 Discussion
### Conception of Restoration Treatments
The doses of PIX and PAX coagulants were determined on the basis of the phosphorus fractions in the water and bottom sediments and the amount of natural components in the sediments having sorption capacity in relation to phosphorus, such as Al, Fe and Ca [57; 58]. The calculated doses of aluminum coagulant for Karczemne Lake were as follows: the area of the central zone of the lake was 20.0 ha, the volume of the central zone was 660,000 m\({}^{3}\), the demand of the profundal sediment for aluminum was 23.6 g m\({}^{-2}\), the demand for aluminum with which to bind the phosphorus in the water column in the central zone was 1650 kg and the total amount of reactive aluminum was 6370.8 kg. The total dose of PAX 18 was 70,787.0 kg. Next, the dose of the iron coagulant for Karczemne Lake was calculated as follows: the area of the coastal zone was 20.4 ha, the volume of the coastal zone was 378,000 m\({}^{3}\), the demand of the littoral deposits for iron was 44.26 g m\({}^{-2}\), the demand for iron for binding the phosphorus in the water depth in the coastal zone was 945 kg and the total amount of reactive iron was 9974.0 kg. The total dose of PIX 111 was 74,433.0 kg. To maintain the positive effects of restoration in Karczemne Lake, rational fish farming should be utilized. This method can be implemented through the use of biomanipulation, which involves the conscious shaping of the biocenosis of aquatic organisms by changing the species composition of ichthyofauna. The main goal of biomanipulation is to increase the number of large forms of zooplankton, mainly Cladocera, and through their control, feed the amount of phytoplankton, which are the first link in the trophic pyramid, thus reducing water blooms. To achieve this effect, the number of sedentary fish, such as bleak, sunflower, small perch, juvenile roach, silverfish, bream and crucial, which feed on zooplankton or seek food in bottom sediments, should be limited. The reduction in these fish assemblies can be achieved, inter alia, through selective harvesting (without the use of towed tools). According to [PERSON] [59], at least 75% of the initial fish should be harvested. This treatment should be intense and short term and should optimally occur within one year but no later than two years. The best effects result from the use of a large amount of pike fry. The recommended amount of recessed fry is at least 1000 pcs/ha [59], and the restocking material should have a size exceeding 10 cm because at this stage of development, the pike is going to feed on fish.
## Data Acquisition and Model Construction
### Inductor Series Resistance Characterization and Modelling
#### 2.2.1 Small Signal Loss Characterization and Modelling  
A first characterization is made with an impedance analyser (Keysight E4990A + Keysight 16047E from Keysight, Santa Rosa, CA, USA). A first frequency-dependent characteristic of the equivalent series resistance is obtained and depicted in Figure 6. Since the characterization method with the impedance analyser is based on applying low-magnitude HF currents, the obtained characteristic for the equivalent series resistance accounts mainly for the winding resistance. The evolution of the series resistance of the characterized inductor with respect to the frequency is representative of the skin effects as well as the proximity effects in the wire. If the skin effect is to be the major part of the frequency-dependent resistor, this resistance is expected to evolve with the square root of the switching frequency, as explained chapter 5.2 of [10]. As it can be seen in Figure 6, the evolution of the series resistance appears not to be proportional to the square root of the frequency as it would be the case if only skin effects were taken into account. This difference highlights the difficulty to rely only on theoretical models to represent the behaviour of the inductor resistance accurately with respect to the frequency. From this characterization, an equivalent inductor value and frequency dependent resistor model \(R_{lr}\) can be derived by fitting the two parameters \(k_{lr}\) and \(P_{lr}\) in Equation (4). In these parameters, \"\(l\)\" accounts for \"a low ripple\" current. This fit can be made by applying common exponential regression on the second part of the curve. For the characterized inductor, the values for parameters \(k_{lr}\) and \(P_{lr}\) are respectively 210 and 1.5. As the principal factor frequency dependence in inductors are skin effect and core losses, evolving respectively with the square root of the frequency [10; 18] and a power factor between one and two [11; 12]. This resistance is a combination of several physical effects with respect to the frequency, it is difficult to represent with theoretical modelling technique without materials and winding knowledge.  
\[R_{lr}(f)=L\times k_{lr}\times f^{p_{lr}} \tag{4}\]  
\[R_{total}=R_{0}+L\times k_{lr}f^{P_{lr}} \tag{5}\]  
Figure 5: Characterization and modelling approach to derive the generic mathematical inductor losses model and corresponding parameters with respect to inductor value.
# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 5 Roll Stability Control in Off-Road Vehicles
### Active Roll Stability Control Methods
#### 5.3.1 Active Leveling Control
These tilting mechanisms, often actuated by hydraulic or electromechanical systems, significantly reduce the risk of rollovers and ensure the efficiency of grain separation and collection. [PERSON] et al. [6] studied the dynamic load characteristics of tracked combine harvesters and validated the stability of hydraulic active leveling mechanisms under complex conditions, further enhancing the safety of agricultural machinery operations.
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 4 ICN Migration Towards IoT Scenarios  
IoT embraces a large set of different environments where both pull and push-based communication support is required. Devices are resource constrained, and the whole communication system needs to be engineered in a way that is battery and bandwidth efficient.  
The ICN/NDN publish/subscribe pull-based model is relevant in IoT environments, as it provides secure, asynchronous support for many-to-many communication. However, its original design requires devices to only send data if an Interest packet is received, which does not scale well with an increasing number of IoT devices. Nevertheless, communication based on the plain pull-based model of NDN is applicable, for instance, in the context of the communication between a gateway or controller (e.g., IoT gateway) and the cloud. In this case cloud applications would use a pull-based approach to gather large amounts of (small) data stored in IoT gateways, and related to a large set of IoT devices.  
While a pull-based model is suitable for the communication between cloud applications and IoT gateways, such model may end-up in reducing network performance if applied to the communication between IoT devices and gateways, since IoT gateways are aggregation points. Interest packets are expected to be sent with high frequency. The polling interval in some cases (e.g., industrial IoT) may be in the order of milliseconds, preventing an ICN-based architecture to adequately scale, as devices would quickly be drained out of energy while dealing with a large amount of Interest packets.  
Hence, a push-based communication model needs to be supported as well, at least between IoT devices and gateways. This allows for energy saving, reduction of used network resources, and a better data management. Such a scenario corresponds to a first step in further potentiating the application of ICN in large-scale IoT environments. This would be an architecture that would suit industrial IoT scenarios as well, given that the notion of controller (gateway or broker) will always be required to assist translation into different domains, as well as to assist in reaching the required availability needs.  
In scenarios holding a higher degree of mobility and/or decentralization, as occurs in vehicular networks, in personal IoT environments, the need to support push-based communication is present as well. For instance, IoT devices may be mobile and alerts may need to be sent. In such cases, push-based communications are required from an end-to-end perspective, for event-triggered communications as well as for periodic communications. Guidelines for such support are debated in the next sections.
## 2 GVNS Heuristics
### GVNS Schemes  
For each perturbation method a GVNS scheme is formed. Specifically, the GVNS_1 contains Shake_1 as its shaking method, GVNS_2 uses Shake_2 to diversify solutions, and GVNS_3 adopts the Shake_3 perturbation method. The initial solution is produced by the Nearest Neighbor heuristic in all GVNS schemes. The pseudocode for three GVNS approaches is given in Algorithms 5-7, respectively.  
```
1:procedureGVNS_1(\(S,k_{max},max\_time,l_{max}\))
2:while\(time\leq max\_time\)do
3:for\(k\gets 1,k_{max}\)do
4:\(S^{*}=\text{Shake\_1}(S,k,l_{max})\)
5:\(S^{\prime}=pVND(S^{*})\)
6:if\(f(S^{\prime})<f(S)\)then
7:\(S\gets S^{\prime}\)
8:endif
9:endfor
10:endwhile
11:return S
12:endprocedure
```  
**Algorithm 5** GVNS_1.  
```
1:procedureGVNS_2(\(S,n,max\_time\))
2:while\(time\leq max\_time\)do
3:\(S^{*}=\text{Shake\_2}(S,n)\)
4:\(S^{\prime}=pVND(S^{*})\)
5:if\(f(S^{\prime})<f(S)\)then
6:\(S\gets S^{\prime}\)
7:endif
8:endwhile
9:return S
10:endprocedure
```  
**Algorithm 6** GVNS_2.  
It should be mentioned that in all three GVNS methods the neighborhoods are searched with both the First and Best improvement search strategy.
# High-Pressure Effects on Selected Properties of Pea and Soy Protein Isolates
## 3 Results  
### Protein Composition and Solubility  
Electrophoretic analysis was performed for the whole protein isolate dispersions under reducing conditions, for unprocessed samples and for those submitted to the more pronounced HPP conditions, i.e., at 400 and 600 MPa for 15 min. There were no major changes in the relative volume of the bands for the identified protein fractions, for both pea (PP) and soy proteins (SP), at the different pH values and HPP conditions analyzed (results are shown as supplementary material--Figures S1 and S2). The similar polypeptide composition observed for unprocessed and HPP-treated samples indicates that HPP did not induce any relevant changes in the relative amount and molecular weight of the main proteins and their subunits under the electrophoretic conditions analyzed.  
The solubility of proteins strongly affects their techno-functional properties, including gelation and interfacial activity, and, consequently, their ability to help in the formation/stabilization of disperse systems. Non-pressurized PPI showed soluble protein values of 25.5 \(\pm\) 0.6, 25.4 \(\pm\) 0.3 and 28.0 \(\pm\) 0.3% at pH 6, 7 and 8, respectively. In general, plant proteins show lower solubility in the pH range of 4 to 6, in the vicinity of the isoelectric point, with expected increases above and below this range.  
SPI showed higher protein solubility than PPI, namely 33.6 \(\pm\) 1.3, 43.8 \(\pm\) 1.7 and 34.3 \(\pm\) 0.3% at pH 6, 7 and 8, respectively. The decrease in solubility observed when the pH increased from 7 to 8 was somewhat surprising. Probably, the relatively high isoelectric point of the glycinin's basic subunits (8.0-8.5; [15]) contributes to the overall decrease of SPI solubility at pH 8.  
The relative effects on protein solubility of the different HPP treatments within the pH 6-8 range are shown in Figure 1.  
Overall, the HPP treatments lead to higher protein solubility than that observed for the unprocessed samples. The increase in protein solubility was considerably higher for SPI than for PPI samples; for SPI at pH 7 and 8, more intensive conditions (i.e., higher pressure or holding time) lead to protein solubilities above 90%. At pH 6, the increase in solubility was less pronounced than at higher pH for both protein isolates and HPP conditions. At this pH (Figure 1A,D), where we expect lower electrostatic repulsions among protein chains if compared to higher pH, increasing pressure for longer processing times leads to lower increases in solubility, probably related to pressure-induced protein aggregation.  
In general, a more pronounced solubility increase was observed at pH 8 regardless of the protein isolate (Figure 1C,F). In addition, the solubility increase was more pronounced as the pressure increased, with a lower effect of increasing the processing time.
## 6 Security and Efficiency Analysis
### Efficiency Analysis
#### 6.2.2 Memory Usage  
In the WSN, the capacities of BSs are considered unlimited, while the capacities of SNs and CNs are limited.  
In the proposed protocol, ID-PVK is kept in SNs and ID-PVK-IDcard is kept in CNs. The proposed protocol has been implemented on the WiSeN node. The RAM capacity of the WiSeN node is 512 bytes, while its ROM capacity is 16 K = 16 \(\times\) 1024 = 16,384 bytes. The memory usage results of the proposed protocol are given in Table 3.  
The proposed protocol uses 34 bytes of RAM and 4 K = 4 \(\times\) 1024 = 4096 bytes of code memory. Therefore, the proposed protocol uses 6.64% of RAM and 25% of ROM.
# Hyperparameter Optimization of Ensemble Models for Spam Email Detection
## 4 Results and Discussion
### Results and Discussion  
To optimize the performance of the baseline random forest model, efforts were targeted at determining the best ntree and mtry by tuning these parameters through grid search. The mtry of the baseline random forest model is 23; hence, mtry values between 6 and 41 were examined during the first run (Table 2). After 10-fold cross-validation, accuracy was used to determine the optimal ntree and mtry which were 1500 and 7, respectively, as shown in Figure 3. At ntree = 1500 and mtry = 7, the highest training accuracy of 0.9750725 was obtained. It took about a week for the model to converge.  
From Figure 3, it can also be observed that as the value of mtry increases the predictive accuracy of the model was decreasing. This necessitated the need for a second run to examine mtry values between 1 and 7 given ntree values of 1000 and 1500. After about four days, the 10-fold cross-validation training converged, at the accuracy of 0.9752454, the optimal ntree = 1500, and mtry = 6 (Figure 4).  
Figure 3: First run: model plot of the 10-fold cross-validation for the determination of best hyperparameter values for random forest.  
The grid search with 10-fold cross-validation based on accuracy returned the optimal values of 7560, and 0.25 for max.depth, nrounds, and eta, respectively, for the XGBoost model. From Figure 5, it can be observed that the accuracy improved progressively as the nrounds increased for each of the max.depths. However, as the number of boosting iterations (nrounds) tends toward 700, the improvement in accuracy became marginal. With a boosting iteration of 560, the accuracy peaked at 0.9781017 on the third face grid where max.depth is 7 and eta is 0.25.  
Figure 4: Second run: model plot of the 10-fold cross-validation for the determination of best hyperparameter values for random forest.  
Figure 5: The model plot of the 10-fold cross-validation for the determination of best hyperparameter values for XGBoost. The face grid shows the performance of the model in terms of accuracy at max.depth of 5, 6, and 7 with varying nrounds and learning rates (eta). The colsample_bytree was held constant at 0.6.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 4 Experiment Analysis
### Analyzing AQI from Spatiotemporal Distribution Characteristics
The AQI progressively declines as one moves away from the densely populated and industrially active city center, highlighting a transparent urban-rural gradient in air quality. This spatial and temporal heterogeneity underscores the complexity of managing air pollution in Xi'an. Strategies for mitigation must account for these geographical and meteorological peculiarities, along with the urban layout and energy consumption profiles. Data-driven insights from the APSO-CNN-Bi-LSTM model can guide targeted interventions, such as optimizing urban planning, promoting cleaner energy alternatives, and implementing pollution control measures during high-risk periods. These measures will contribute to a more sustainable and livable urban environment.
## 3 Methods
### Functional Model

Table 2: Parameter S-basis restriction implicitly applied in Equation (2). The restricted parameters or the specified combination are set to 0 to eliminate rank deficiencies. non-rank-deficient observation equations in the case where the network is connected. Here the term connected is derived from graph theory; see, e.g., [20]. More specifically, the GNSS network needs to be connected on two different frequencies, or on one code and one phase frequency. This condition needs to be satisfied at each epoch when no temporal constraints on the receiver/satellite clocks or ionospheric slant delays are imposed. Otherwise, satellite clocks will not be separatable from the ionospheric slant delays. An example to illustrate this is given in Figure 2. Furthermore, to obtain integer estimable ambiguities, the GNSS network needs to be connected on every phase signal, introducing a satellite and receiver bias, since the ambiguities used as S-basis restrictions have to be a spanning tree (ST) of the respective graph (see [21]). After initialization of the ambiguity datum, the ambiguity remains set until a receiver or satellite experiences a loss of all ambiguities, i.e., all ambiguities of this receiver or satellite are reset or stop being observed. Then, this receiver or satellite is reintroduced into the GNSS network graph with a new node, and a new datum ambiguity is introduced to complete the ST again. In order to initialize the ST that determines the ambiguity datum, the edges are weighted by the inverse of the respective satellite elevation. The ST with the least total weight then determines the choice of ambiguity datum. The rationale for weighting the edges by their respective satellite elevation is that observations from satellites with high elevation are generally more accurate, i.e., they are less affected by measurement noise and effects like multipath and are less likely to experience a cycle slip. When adding a new receiver (satellite) ambiguity as a datum ambiguity, the corresponding satellite (receiver) edge with the highest elevation is used as the ambiguity datum.