esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SnBi-Based Solders
### Mechanical Properties  
The formation of IMCs layer is the basis of fine bonding between the solder and substrate, but the excessive growth of IMCs layer will reduce the reliability of interconnection and the lifetime of electronic devices. Research [57] found Ag nanoparticles addition could suppress the growth of Cu-Sn IMC and improve the shear strength of the solder joints by 18.9% after 180 min liquid reaction at 220 \({}^{\circ}\)C. The improvement behavior can be explained by refinement strengthening, dispersion strengthening, and absorption theory. Under external force, the deformation of the composite solder is dispersed to smaller grains with different orientation, which suppressed the formation of cracks and increased the ductility of the solder.  
[PERSON] et al. [62] reported that 0.05 wt.% Ni-CNTs could effectively increase the ultimate tensile strength (UTS) of SnBi solder bulks and joints, as shown in Figure 9. What is more, the fracture surface of SnBi-CNTs solder appeared more regular than the plain SnBi solder and CNTs were detected pining at the grain boundaries in high magnification, which demonstrated that the grain growth was suppressed, and the microstructure was refined by Ni-CNTs addition. However, excessive CNTs could gradually translate the fracture modes from ductile fracture to brittle fracture and decrease the UTS and elongation of the composite solder at the same time.  
Figure 8: SEM morphology of as-prepared solder: (**a**) Pure SnBi solder; (**b**) SnBi solder-doped 31 nm Ag nanoparticles; (**c**) SnBi solder-doped 76 nm Ag nanoparticles; (**d**) SnBi solder-doped 133 nm Ag nanoparticles [57].  
It was found that the effect of GNSs on the fracture surface of SnBi solder was similar to that of CNTs [63]. The phenomenon that GNSs embedded in the fractured solder bulk was investigated, meaning strong bonding was formed between GNSs and the matrix. When the solder was under tensile stress, GNSs would be pulled out from the matrix to reduce the stress concentration that appeared in the crack tip. However, due to the restacking behavior of GNSs, the elongation of the composite solders decreased when the content exceeded 0.01%. [PERSON] et al. [64] used finite element modeling (FEM) method to analyze the stress distribution at the interface of GNSs reinforced Sn58 Bi0.7 Zn solder joints (after aging for 120 min) in the tensile test. The result showed that the GNSs addition changed the maximum stress position from the Cu/IMC interface in undoped joints to the IMC/solder interface in GNSs-doped joints, which indicated that the composite solder joints have better tensile properties.
## 3 Low-Cost Multi-Sensor
### Audio Data Processing  
Regarding the audio processing, the acoustic noise should be continuously sampled by means of the selected microphone, and the equivalent A-weighted acoustic level evaluated. To this purpose, processing of the data stream from the microphone is carried out in step 7, which is performed asynchronously to step 5 and 6 of the flowchart shown in Figure 5. Indeed, the microphone output stream is directly copied in a memory area of the microcontroller, not to the increase the workload of the firmware itself. In accordance with the ANSI S1.42 standard [47], the A-weighting transfer function \(H_{\text{A-weight}}\) defined in the Laplace domain is given by:  
\[H_{\text{A-weight}}(s)=G_{\text{a}}\frac{(\omega_{\mathrm{q}}^{2}s^{4})}{(s+ \omega_{1})^{2}(s+\omega_{2})(s+\omega_{2})(s+\omega_{4})^{2}}, \tag{4}\]  
where \(\omega=2\pi f\), and \(f_{1}=20.6\) Hz, \(f_{2}=107.65\) Hz, \(f_{3}=737.86\) Hz, and \(f_{4}=12,194.2\) Hz [48]. The magnitude and phase of \(H_{\text{A-weight}}\) are plotted in Figure 6a in solid lines. It is worth noticing that \(|H_{\text{A-weight}}|=0\) dB at 1 kHz. To implement such a filter directly by the developed multi-sensor, (4) was time-discretized, meaning transformed in the \(z^{-1}\) domain, using [PERSON]'s method [49]. The corresponding magnitude and phase are shown in Figure 6a in dashed lines. As the microphone sampling time was set to \(f_{\text{s}}=22\) kHz due to memory constraints, the magnitude and phase of the discretized transfer function differ from those of (4) for frequencies higher than 5 kHz.  
With the transfer function in the \(z^{-1}\) domain, an infinite impulse response filter (IIR) in transposed direct form II was implemented in step 7. The effect of applying the IIR filter to broadband 100 Hz-10 kHz noise, which was generated by a sound source [50] and sampled by the selected microphone, is shown in Figure 6b.
# Seismic Response Analysis of a Curved Bridge under Near-Fault and Far-Field Ground Motions
## 4 Test Results
### Displacement of Pier  
As illustrated in Figure 10, the pier of the curved bridge has the largest displacement under NP ground motion excitation and the smallest displacement under FF ground motion excitation. The pier displacement under unidirectional excitation (X\({}_{\text{P}}\)) of NP and NN ground motions is 1.83 and 1.19 times that of FF ground motions, respectively. Meanwhile, the pier displacement under bidirectional seismic excitation (X\({}_{\text{P}}\) + Y\({}_{\text{S}}\)) of NP and NN ground motions is 2.39 and 1.15 times that of FF ground motions, respectively. The pier displacement under unidirectional excitation (Y\({}_{\text{P}}\)) of NP and NN ground motions  
Figure 9: Bearing deformation: (**a**) Bearing deformation in case 1; (**b**) Bearing deformation in case 2.  
is 1.75 and 1.16 times that of FF ground motions, respectively. The pier displacement under bidirectional seismic excitation (\(\mathrm{X_{S}}+\mathrm{Y_{P}}\)) of NP and NN ground motions is 2.39 and 1.15 times that of FF ground motions, respectively.  
Piers 1 to 4 are symmetrically distributed. The height of pier 4 is higher than that of pier 1, and as a result the seismic response at pier 1 is less than that at pier 4. The stiffness of the double-column pier in the radial direction is relatively large, and a fixed pot bearing is installed on pier 2. Hence, the pier and the main girder move synchronously. Most of the seismic force of the main girder is transferred to the pier to make the tangential displacement most significant at pier 2.  
In summary, the pier displacement is more significant under NP ground motion excitation, which has a certain amplification effect. Regardless of the relationship between the seismic input direction and the curved bridge, the tangential displacement of the fixed pier is significant, while the radial displacement of the pier is related to the pier height.
# Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things
## 1 Introduction
The network routing manager considers these system inputs and routing performance metrics to distribute traffic to the nodes in the network and forward data packets. Typical routing performance metrics include the end-to-end reliability, transmission latency, and overall network energy consumption. These routing performance metrics can be directly affected by the MAC technology because different MAC protocols exhibit different reliability, delay, energy consumption, and traffic congestion metrics for each link. Consequently, when MAC and routing layer technologies are designed independently in the feedback loop (Figure 1), it can be difficult to simultaneously satisfy various requirements such as reliability and delays. High reliability and low latency transmission are very important factors in wireless sensor networks, and many studies are being conducted [39]. In a typical routing protocol that ensures reliability, a routing table with a calculated hop count is configured so that the shortest hop can be statistically selected. However, link failures, node mobility, and routing path changes make reliable data transmission difficult. Figure 1: Modeling and design based on MAC and routing layer interaction. In this paper, we proposed a solution to support the reliability, scalability, and robustness of multi-hop IoT environments. We designed a MAC layer-based cross-layer protocol termed Topological Structure Mesh Routing (TSMR) protocol The proposed scheme provides robust connectivity and efficient route discovery procedures from the gateway to mobile and static leaves, and vice versa. Additionally, the proposed scheme provides fast and efficient association/reassociation procedures for the mobile nodes while limiting the impact of the signaling overhead to support the device's mobility.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
###### Abstract  
Infestations of _Tomicus_ spp. have caused the deaths of millions of Yunnan pine forests in Southwest China; consequently, accurate monitoring methods are required to assess the damage caused by these pest insects at an early stage. Considering the limited sensitivity of optical reflectance on the early stage of beetle stress, the potential of thermal infrared (TIR) can be exploited for monitoring forest health on the basis of the change of canopy surface temperature (CST). However, few studies have investigated the impact of the leaf area index (LAI) on the accuracy of TIR data-based SDR assessments. Therefore, the current study used unmanned airborne vehicle (UAV)-based TIR and light detection and ranging (LiDAR) data to assess the capacity of determining the potential for using TIR data for determining SDR under different LAI conditions. The feasibility of using TIR for monitoring SDRs at the tree level and plot scales were analyzed using the relationship between SDR and canopy temperature. Results revealed that: (1) prediction accuracy of SDR from CST is promising at high LAI values and decreases quickly with LAI, and is higher at the single tree scale (R\({}^{2}\) = 0.7890) than at the plot scale (R\({}^{2}\) = 0.5532); (2) at either single tree or plot scale, a significant negative correlation can be found between CST and LAI (\(-\)0.9121 at tree scale and \(-\)0.5902 at plot scale); (3) LAI affects the transmission paths of sunlight and sensor, which mainly disturbs the relationship between CST and SDR. This article evaluated the high possibility of using TIR data to monitor SDRs at both tree and plot levels and assessed the negative impact of a low LAI (\(<\)1) on the relationship between temperature and SDR. Accordingly, when measuring forest health using TIR data, additional data sources are required to eliminate the negative impact of low LAIs and to improve the monitoring accuracy.  
forest infestation; shoot damage ratio; thermal infrared imagery; LiDAR; LAI +
Footnote †: journal: _Sciences_
# Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes
## 3 Description and Formulation of BACASP
### Problem Description
Then, we establish the dissatisfaction function of the behavior subject; based on the value function, the dissatisfaction function can be expressed as in Equation (2):  
\[\mu(t)\ =\ \begin{cases}1&t\geq rt\\ \lambda(t-t_{0})^{\eta}&t_{0}\leq t\leq rt\\ 0&0\leq t\leq t_{0}\end{cases}  \tag{2}\]  
where \(t\) denotes the time lost; \(rt\) is the maximum tolerance time; and \(t_{0}\) is a reference point indicating the decision maker's psychological cut-off point. After calculating \(t_{0}\ =\ 0\) and performing the normalization operation, the shipowner dissatisfaction affiliation function is expressed in Equation (3). \[\mu_{i}(t)\ =\ \begin{cases}1&t\geq rt_{i}\\ \lambda\frac{t^{\eta}}{\Delta}&0\leq t<rt_{i}\end{cases} \ \forall i\in V \tag{3}\]  
where \(\lambda\) is known as the loss aversion coefficient; \(\eta\) is the coefficient of risk attitudes toward losses; \(A\) is the normalization factor, and \(rt\) is the maximum tolerance time. In summary, we face the challenge of balancing terminal operating costs and shipowner satisfaction, as these two objectives are not always related or even mutually exclusive (as shown in Section 5.2.3). In fact, focusing only on operational costs may lead to terminal congestion and degradation of service quality, thus reducing shipowner satisfaction. Therefore, we propose a multi-objective optimization approach that aims to minimize both the operating cost and shipowner dissatisfaction at the CTs. The feasible berthing plan for BACASP can be represented as a rectangle bin-packing problem in a time-space diagram (as shown in Figure 2). In this layout, the horizontal axis represents the berthing position, and the vertical axis represents time. Each vessel is represented as a rectangle whose length indicates its length and width indicates its process time. Each rectangle corresponding to a vessel satisfies the spatial-temporal non-overlapping constraint, as well as QC availability and non-crossing constraints. For instance, Figure 2 shows a feasible berthing plan for four vessels within a 20-h planning horizon, where the terminal is 1000 m long and equipped with 10 QCs mounted on the rail. The number of the vessel and the specific QC assignment are indicated in each rectangle of the figure.
# Valuable Nutri from _Ulva rigida_: Modulation by Seasonal and Cultivation Factors
## 3 Results and Discussion
### Ash and Mineral Profile
The analyzed green macroalgae also proved to be an important source of Mg and Ca, with values fitting within the range previously described for this species (1775-8686 mg/100 g DW and 325-1266 mg/100 g DW, respectively) and even exceeded those of some edible higher plants and other common foods which are known to be rich in these nutrients [17; 27; 35; 49; 55]. Interestingly, the consumption of 8 g of dry _U. rigida_ covered between 62 and 91% of the recommended daily intake (RDI) of magnesium (Supplementary Materials Table S1). Seasonality did not affect the levels of Ca but those of Mg followed a close pattern to that registered for ash, with minimum values being registered upon October and maximum in spring. In addition, in this season, Mg was predominately accumulated in macroalgae grown under L\({}_{\text{sd}}\) conditions. Please note that, to our knowledge, macromineral content variation in _Uliva_ genus as a function of seasonality has only been described for wild _U. lactuca_ from Alexandria [56], in Egypt, while the effect of Fr and Gd is herein focused on for the first time. The trace elements iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) were also assessed in each season (Table 1). Samples were rich in iron (35.7-170 mg/100 g DW), followed by manganese (2.6-11.5 mg/100 g DW), copper (1.1-3.4 mg/100 g DW) and zinc (0.8-1.9 mg/100 g DW). In fact, eating a portion of 8 g of dry _U. rigida_ covered up to 196% of the RDI of iron, while Mn, Cu and Zn did not cover RDI above 15%. For this specific level of Fe, results of the recommended daily allowance (RDA) (Supplementary Materials Table S1) showed that the use of no more than 30 g/day of _U. rigida_ would not compromise health due to the iron content of this seaweed. These results were comparable to those reported for wild samples collected at Galician Coast (Spain) and Gabes Gulf area (Tunisia) [35; 49; 55]. Another study focusing on _U. rigida_ from different sites of Italy documented similar concentrations of the mentioned elements, but the relative abundance differed slightly, as copper was present in higher concentrations than zinc [22]. The same study also reported seasonal variability of these elements, which mainly peaked in autumn and spring, as a result of the proliferation of the macroalgae. In opposition, in the present study, no seasonal patterns were detected, mainly due to the high variability of results among replicates under the same experimental conditions. As for the influence of growing conditions, no significant differences were found as well. Nevertheless, this was the first study evaluating the effect of cultivation conditions on trace elements in cultivated _Ulva_.
## 2 System Modeling
### Network Power Consumption Modeling  
Essential requirements such as lifetime, available energy, and reduced cost need to be considered in modeling IoT applications' energy consumption. To the best of our knowledge, for modeling network energy consumption, there are two main scenarios:  
1. Point-to-Point Communications (PPC)[15]
2. Time Synchronized Networks (TSN)[16]  
We have used the PPC model for our simulations, considering parameters such as interference-free and a single hop communication scenario. Additionally, in our simulation, we have assumed the Medium Access Control (MAC) layer is ideal. This assumption means that co-channel interference and packet collisions can be neglected, so that any transmitted data packet can be assumed to reach the receiver correctly. The energy consumption in this scenario should be calculated separately for each MTC device. According to Figure 3, the device should consume energy \(E_{Datapacket}\) for the \(K\)th transmitted data packet, including all energy consumed in Figure 3 in a time of \(T_{Datapacket}(K)\). As a result, for transmitting all the \(N_{DataPacket}\) messages, the average power consumption can be expressed as:\[\mathbf{P}_{\text{Network}}=(1/N_{DataPacket})\sum_{K=0}^{N_{DataPacket}}\frac{E_{ Datapacket}}{T_{Datapacket}(K)} \tag{7}\]  
For different wireless communication technologies, as shown in Equation (7), \(E_{Datapacket}\) is a changing parameter. The two main terms of Equation (7) that can impact total energy consumption are the radio power and transmission time. Although the maximum radio transmit power is limited, the transmission time varies by applying different modulation and coding techniques, changing the data transmission speed over time. In conclusion, energy consumption is affected by the main three parameters for each data transmission process as follows:  
1. The Packet Repetition Factor
2. The Radio Power
3. The Number of Retransmissions
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 2 State-of-the-Art
### Licensed Spectrum LPWANs  
_Licensed spectrum LPWANs_ are deployed in the spectrum holdings of mobile operators and take advantage of cellular technologies to expand into new IoT usage scenarios, by providing improved support for low-cost and energy-efficient machine type communication (MTC) devices. NarrowBand IoT (NB-IoT), enhanced machine-type communication (eMTC) (also known as LTE-M), and extended coverage GSM IoT (EC-GSM-IoT) are the initiatives emerging from the 3 rd Generation Partnership Project (3 GPP) Release 13 [12].
###### Abstract  
Epileptic seizures, a leading cause of global morbidity and mortality, pose significant challenges in timely diagnosis and management. Epilepsy, a chronic neurological disorder characterized by recurrent and unpredictable seizures, affects over 70 million people worldwide, according to the World Health Organization (WHO). Despite significant advances in medical science, accurate and timely diagnosis of epileptic seizures remains a challenge, with misdiagnosis rates reported to be as high as 30%. The consequences of misdiagnosis or delayed diagnosis can be severe, leading to increased morbidity, mortality, and reduced quality of life for patients. Therefore, this paper presents a novel approach to enhancing epileptic seizure detection through the integration of Synthetic Minority Over-Sampling Technique (SMOTE) for data balancing and a Hybrid Feature Selection Technique--Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT). The proposed model aims to improve the accuracy and reliability of seizure detection systems by addressing data imbalance and extracting discriminative features from electroencephalograms (EEG) signals. Experimental results demonstrate substantial performance gains, with the Support Vector Machine (SVM) classifier achieving 97.30% accuracy, 99.62% Area Under the Curve (AUC), and 93.08% F1 score, which outperform the results of the existing studies from the literature. The results highlight the effectiveness of the proposed model in advancing seizure detection systems, highlighting the potential to improve diagnostic capabilities and patient outcomes.  
epilepsy; machine learning; epileptic seizures; EEG; seizure detection; SMOTE; PCA; DWT 2020
## Abbreviations  
\begin{tabular}{l l} a & Application layer message \\ \(\varphi\) & Message's flag alive \\ \(b\) & Buffer size in bytes \\ \(B\) & Buffer: a list to store messages \\ \(\mathfrak{c}\) & Controlled variable \\ \(c_{t+n}\) & Congestion state prediction for time \(t+n\) \\ \(d\) & Decay \\ \(\delta\) & Directive: \(\delta=(H,\theta)\) \\ \(\delta_{l}\) & Directive encapsulating a replication limit \\ \(\phi\) & Factor to calculate \(t_{t+n}\) \\ \(\underline{\mu}_{p}\) & Identifier of the network setting \(P\) \\ \(\underline{\mu}_{l}\) & Message's replication limit Id setting \\ \(k_{1}\) & Multiplicative decrease factor \\ \(k_{2}\) & Additive increase factor \\ \(g_{i}\) & A message \\ \(\#g_{d}\) & Number of delivered messages \\ \(\#g_{c}\) & Number of created messages \\ \(\#g_{r}\) & Number of relayed messages \\ \(l\) & Max. message copies in the network (replication limit) \\ \(\bar{\lambda}\) & Latency average \\ \(\lambda_{i}\) & Latency of message \(g_{i}\) \\ \(L_{1}\) & Highest message generation rate for the ISTH dist. \\ \(L_{2}\) & Lowest message generation rate for the ISTH dist. \\ \(m_{l_{i}}\) & Node \(n_{i}\)'s local network measurement: \(m_{l_{i}}=(v_{l_{i}},t_{l_{i}})\) \\ \(m_{i}\) & Aggregated received network measurements: \(m_{i}=(v_{l_{i}},\eta_{l_{i}},t_{l_{i}})\) \\ \(m_{l+n}\) & Predicted network measure value at time \(t+n\) \\ \(M_{i}\) & Aggregated network measurements list for node \(n_{i}\) \\ \(\bar{m}\) & Aggregation of the received measurements during \(\hat{l}\)s \\ \(\bar{M}\) & List of \(\bar{m}\) values \\ \(z\) & Max size of \(\bar{M}\) \\ \(\mu\) & Control system's manipulated variable \\ \(\eta\) & Number of aggregated measurements \\ \(n_{i}\) & A specific node \\ \(o_{i}\) & Buffer occupancy in bytes of node \(n_{i}\) \\ \(o_{t+n}\) & Buffer occupancy prediction for time \(t+n\) \\ \(o_{min}\) & Min. buffer occupancy rate \\ \(o_{max}\) & Max. buffer occupancy rate \\ \(\theta\) & Overhead ratio \\ \(r\) & Reduction factor \\ \(\mathfrak{r}\) & Control system's reference input \\ \(\varrho\) & Number of times a message has been relayed \\ \(\rho\) & Linear regression function \\ \(\sigma\) & Delivery ratio \\ \(t\) & Current time \\ \(t_{t+n}\) & Time ahead when calculating the congestion prediction \\ \(t_{c_{i}}\) & Creation time for either a measurement or an aggregation \\ \(\hat{t}\) & Time period for aggregating received measurements \\ \(\hat{T}\) & List of the timestamps of the aggregations performed \\ \(\theta\) & Network setting's value \\ \(v_{l_{i}}\) & Node \(n_{i}\)'s local network measurement \\ \(v_{l}\) & Result value after aggregating the received measurements in \(M_{i}\) \\ \end{tabular}