esa-sceva/satcom-chunk-collection huggingface dataset:
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 3 Communication Technologies and Protocols
### Other State-of-the-Art Communication Technologies  
In addition to the aforementioned established communication technologies, other burgeoning technologies such as EnOcean [101], BACnet [102], 6 LoWPAN [103], Z-Wave [104] and LoRaWAN [105] have also started to make their mark for building applications in recent years (see Table 3).  
EnOcean is a wireless energy harvesting technology which combines micro energy converters with ultra-low power electronics to enable transmitters to harvest their energy from the environment, i.e., ambient light, temperature, motion, etc. This energy harvesting makes the technology well suited for wireless and battery-less sensors and switches used in building automation systems.  
BACnet (Building Automation and Control Networking) is a standardized data communication protocol developed specifically for multiple devices to communicate across building automation systems by system users and building system manufacturers for applications such as HVAC, lighting control, fire access and security systems. The BACnet protocol services include Who-is, I-am, Who-has, I-have which are used for Object and Device discovery, and the standard defines a number of physical layers such as Ethernet, BACnet IP/IPv6, RS232/RS485 and Zigbee for different user requirements.  
6 LoWPAN (IPv6 over Low Power Wireless Personal Area Networks) is a wireless networking technology that optimizes IPv6 for use with low-power communication technologies such as IEEE 802.15.4 radios, hence enabling data packets to be carried efficiently within small link-layer frames at low bandwidths. In particular, 6 LoWPAN takes advantage of the strong AES-128 link security within IEEE 802.15.4 and is considered well suited for applications such as building automation with sensors and actuators, street/residential lighting and control, and smart metering.  
Z-Wave is another proprietary wireless technology designed specifically for the remote monitoring and control of residential and light commercial applications and has gained popularity in recent years due to the fact that the Z-Wave RF system operates in the sub-GHz (900 MHz) frequency range (unlike WiFi and other IEEE 802.11-based technologies), hence enabling a longer coverage range and lower power consumptions.  
Finally, LoRaWAN, the latest of all available wireless technologies, is a low-power wide-area network (LPWAN) specification developed by LoRa Alliance for connecting wireless battery-operated devices in a regional, national or global network. Attributed to its long range (up to 10,000 m) and capability for a very large number of nodes (2000-3000) to be connected, LoRaWAN is now beginning to gain popularity for a wide range of smart building applications requiring secure bi-directional communication, localization services and mobility.
## 3 Earthquake Early Warning System for Bursa Province (Turkiye) and Its Surroundings
### Determination of Magnitude Equation Coefficients for Bursa Province (Turkiye) and Its Surroundings
When the regression result is reversed to estimate the magnitude using \(Pd\) and \(R\), the magnitude relationship is:  
\[M=5.28+1.11\ log_{10}(Pd)+1.5\ log_{10}(R) \tag{4}\]  
The linear relationship between \(T_{p}^{max}\) and observed \(M\) is:  
\[M=4.88+1.97\ log_{10}\left(T_{p}^{max}\right) \tag{5}\]  
Figure 4 shows the relationship of \(T_{p}^{max}\) calculated for each station and observed magnitude (\(M\)). The coefficient of determination of this relationship is 0.824. The relationship exhibits a good fit despite the scattering observed, especially for large earthquakes. The relation parameters obtained for the Bursa province and its surroundings are given in Equations (4) and (5). In the following, we implement Equations (4) and (5) in EPIC and replay the data in \"simulated real-time\" and \"real-time\".
## 4 Analysis of Simulation Results  
This section first analyzes the bit error rate performance of the OAM optical communication system with and without polarization coding when \(C_{n}^{2}=1.0\times 10^{-15}\) K\({}^{2}\)m\({}^{-2/3}\) and \(\omega=-2.5\), and then analyzes the bit error rate performance of different decoding algorithms with different \(C_{n}^{2}\) values (\(1.0\times 10^{-15}\) K\({}^{2}\)m\({}^{-2/3}\) and \(1.0\times 10^{-13}\) K\({}^{2}\)m\({}^{-2/3}\)) and different \(\omega\) values (\(-2.5\), \(-1.5\), and \(-0.5\)).  
Figure 5 shows the comparison of bit error rate performance with and without channel coding under the condition of weak ocean turbulence. As can be seen from the figure, in the absence of channel coding, the bit error rate performance of the system deteriorates rapidly with the increase in transmission distance, which seriously affects the communication performance. However, in the case of channel coding, the bit error rate performance of the system is improved to a considerable extent. The bit error rate at 30 m is reduced from 0.435 to 0.209. Although the system bit error rate is still high, the bit error rate performance can continue to be optimized by other means.  
Figure 6 shows the bit error rate performance of different decoding algorithms (SC, SCR, and CASCL) under different turbulence intensities (\(\text{C}_{n}^{2}=1.0\times 10^{-15}\,\text{K}^{2}\text{m}^{-2/3}\) and \(\text{C}_{n}^{2}=1.0\times 10^{-13}\,\text{K}^{2}\text{m}^{-2/3}\)) and different ratios of ocean turbulence due to salinity to ocean turbulence due to temperature (\(\omega=-2.5\), \(\omega=-1.5\), and \(\omega=-0.5\)). The transmission distance is 50 m, the phase screen is spaced at an interval of 10 m, the phase screen size is 0.04 m \(\times\) 0.04 m, the selected beam wavelength is 532 nm, the girdle radius is 0.005 m, the yard length \(\text{D}=512\), the modulation method is BPSK, and the decoding result requires 50 iterations to obtain the average.  
Generally speaking, in the ocean turbulence channel, with the increase in the value of \(w\), three different decoding methods are seriously affected by turbulence and the bit error rate increases accordingly; further, the greater the turbulence intensity, the higher the bit error rate of the three decoding methods. The bit error rate of the system increases with the increase in transmission distance and sum. The best decoding performance is CASCL, while the performance of the SCL decoding algorithm has little difference from the CASCL decoding algorithm, and the worst performance is found for the SC decoding algorithm. The reason for this situation is that the SCL decoding algorithm and CASCL decoding algorithm consider multiple paths when making a judgment, instead of choosing only the path with the highest metric value for downward expansion, as the SC algorithm does.  
Figure 5: Performance comparison of bit error rate under weak ocean turbulence (\(\text{C}_{n}^{2}=1.0\times 10^{-15}\,\text{K}^{2}\text{m}^{-2/3}\)).
## 3 Methodological Background: LMI-Based Control and Observer Design
### Robust Output Feedback Control  
**Theorem 2** (Sufficient stability condition for output feedback control).: _Robust asymptotic stability of the closed-loop control system according to Case 2 for an error-free output feedback (i.e., \(\mathbf{C}\mathbf{x}\equiv\mathbf{C}\dot{\mathbf{x}}\)) is ensured if the gain matrix \(\mathbf{K}_{0}\) satisfies the bilinear matrix inequalities_  
\[(\mathbf{A}_{v}-\mathbf{B}_{v}\mathbf{K}_{0}\mathbf{C})^{T}\cdot\mathbf{P}+ \mathbf{P}\cdot(\mathbf{A}_{v}-\mathbf{B}_{v}\mathbf{K}_{0}\mathbf{C})\prec 0, \tag{21}\]  
\(\mathbf{P}=\mathbf{P}^{T}\succ 0\)_, for all polytope vertices \(v\in\{1,\ldots,n_{v}\}\) in (14)._Proof.: The proof of Theorem 2 is a direct consequence of the sufficient stability condition used for the proof of Theorem 1.  
**Corollary 2** ([25]).: _For precisely known matrices \(\mathbf{C}\), an LMI formulation of Theorem 2 is obtained by introducing a linearizing change of variables with \(\mathbf{Q}=\mathbf{P}^{-1}\succ 0\) and the equality constraints_  
\[\mathbf{MC}=\mathbf{CQ}\quad\text{and}\quad\mathbf{N}=\mathbf{K}_{\mathbf{o}} \mathbf{M}. \tag{22}\]  
_Multiplying (21) from the left and right by \(\mathbf{Q}\) and considering the relations (22) yields the LMIs_  
\[\mathbf{A}_{v}\mathbf{Q}+\mathbf{Q}{\mathbf{A}_{v}}^{T}-\mathbf{B}_{v} \mathbf{NC}-\mathbf{C}^{T}\mathbf{N}^{T}\mathbf{B}_{v}{}^{T}\prec 0. \tag{23}\]  
_If the matrix \(\mathbf{C}\), and therefore also \(\mathbf{M}\), have full row rank, the resulting controller gain is given by_  
\[\mathbf{K}_{\mathbf{o}}=\mathbf{NM}^{-1}. \tag{24}\]  
**Remark 3**.: _In the literature [10; 25], an alternative problem formulation is often given in terms of_  
\[\mathbf{PA}_{v}+{\mathbf{A}_{v}}^{T}\mathbf{P}-\mathbf{B}\mathbf{NC}-\mathbf{ C}^{T}\mathbf{N}^{T}\mathbf{B}^{T}\prec 0 \tag{25}\]  
_with \(\mathbf{P}\succ 0\) and the additional equality constraint_  
\[\mathbf{BM}=\mathbf{PB}, \tag{26}\]  
_leading to \(\mathbf{K}_{\mathbf{o}}=\mathbf{M}^{-1}\mathbf{N}\). Applying this formulation is, however, only useful for precisely known matrices \(\mathbf{B}\), where \(\mathbf{C}\) instead may be subject to a polytopic uncertainty model._  
**Remark 4**.: _Theorems 1 and 2 become identical for the case of a full state feedback, namely, for \(\mathbf{C}=\mathbf{I}\). Then, the constraint \(\mathbf{MC}=\mathbf{CQ}\) becomes redundant as \(\mathbf{M}=\mathbf{Q}\)._
## 1 Introduction
Results from an audit at Queen's University Belfast on technical quality of root fillings performed by undergraduate students [16] concluded that in most of the teeth the technical quality of the root filling was acceptable, and students were exposed to an appropriate case mix for endodontic training. The use of machine-driven systems for instrumentation, the selection of primary ET, as well as the exclusion of ET with post-root filling or where only manual instrumentation was used, might have influenced the results. Hence the outcomes of similar audits have conflicting results [17; 18; 19]. The European Society of Endodotntology (ESE) and the Association for Dental Education in Europe (ADEE) have published curricular guidelines to define and harmonize the skills of students by the end of graduation, emphasizing the requirement of a minimum pre-clinical and clinical training that allows future professionals to achieve the necessary quality in ET performance [20; 21; 22]. Several studies on the effect of operator experience using mechanized systems concluded that, despite the good performance obtained by inexperienced operators, the associated learning curve should be highlighted; they considered it a crucial factor for establishing the minimum amount of work required for students to obtain the necessary competence for independent professional practice [16; 23; 24; 25; 26]. Regarding the use of reciprocating systems, characterized by a simple and safer protocol [4; 11], there are still few studies that evaluate the quality of the treatments performed by inexperienced operators, such as, undergraduate students, and the extension of its learning curve. In this regard, this pilot study aimed to evaluate the quality of the endodontic treatments performed by an undergraduate dental student using the reciprocating motion (WOG) and obturating with a matched gutta-percha cone and epoxy-resin-based sealer, along the sequential preparation of 40 L-simulated root canals. It also had the purpose to determine the time required for each of the following stages: active instrumentation, total instrumentation and obturation, as the number of treated canals increased. Additionally, it had the purpose of tracing a learning curve of total instrumentation times/filling quality across the sequential preparation of the 40 canals. The null hypothesis was that there would be no differences in quality of the fillings or treatment time of students' ET, using reciprocating instrumentation and filling with matched gutta-percha cones and sealer, as the number of treated canals increased.
# Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
## 3 State of Health Estimation Model
### Support Vector Machine  
Support vector machines, steming from the statistical learning theory, is widely applied for classification and forecast [23; 30], which supplies an efficient manner to deal with nonlinear and high-dimensional problems. Here, the input-output dataset regarding the battery CC charging data \((x_{i},y_{i};i=1,2,\ldots,l)\) is defined, where \(x_{i}\in R^{d}\) is d-dimensional input vectors and \(y_{i}\in R\) is SOH. For the nonlinear \(\epsilon\)-support vector regression (\(\epsilon\)-SVR), the optimization goal is to find a function \(f(x)=w^{T}\phi(x)+b\) that tolerates the deviation of \(\varepsilon\) with the true value of sample y in a high dimensional feature space, where w is the weight vector and b denotes the regression parameter, and \(\phi(x)\) maps x into a high  
Figure 3: Terminal voltage curves during battery cycle numbers for different voltage region. (**a**) The curve of constant current (CC) charging voltage region [3.8, 4.2]; (**b**) The curve of CC charging voltage region [3.9, 4.0].  
Figure 2: Terminal voltage curves under different battery cycle numbers.  
-dimensional feature space. This can be realized by introducing the error penalty factor \(C\) and the slack variables \(\xi_{i}\) and \(\xi_{i}^{*}\). The related nonlinear problem can be formulated as,  
\[\begin{array}{ll}\min\limits_{w,b,\xi_{i},\xi_{i}^{*}}&\frac{1}{2}\|w^{2}\|+C \sum\limits_{i=1}^{l}\big{(}\xi_{i}+\xi_{i}^{*}\big{)}\\ s.t.&(w\cdot\phi(x))+b-y_{i}\leq\varepsilon+\xi_{i}\\ y_{i}-(w\cdot\phi(x))-b\leq\varepsilon+\xi_{i}^{*}\\ &\xi_{i},\xi_{i}^{*}\geq 0,i=1,2,\ldots l.\end{array} \tag{2}\]  
Then, Lagrange multipliers \(\alpha_{i}\) and \(\alpha_{i}^{*}\) are employed for solving w and b, respectively. Finally, the approximating function for predicting the battery SOH can be gained as,  
\[f(x)=\sum\limits_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})K(x,x_{i})+b, \tag{3}\]  
where \(K(x,x_{i})=(\phi(x_{i})\cdot\phi(x))\) is the kernel function. Based on the above offline training, Equation (3) can be programmed in the BMS and then can be applied to achieve the on-board SOH estimation for real operation. The authors aim to further exploit the potential SVM algorithm applied for the battery SOH estimation. The primary advantage of SVM is that the support vectors (SVs) can determine the regression model, of which the Lagrange multipliers should satisfy \(\alpha_{i}\
eq 0\) and \(\alpha_{i}^{*}\
eq 0\). Support vectors can not only help to catch the significant samples, but also can avoid the so-called curse of dimension. In the next step, the selection of input vectors and the corresponding parameters are conducted to achieve the SVM application.
## 5 Results and Discussion  
### Effect of Jammer's Proximity to CNR  
Once the jammer's position was obtained, the analysis of how the proximity of a jammer can affect a receiver's signal reception was possible. After inspecting the behavior of the CNR at the jammer approaching, the effect of the jammer on the receivers can be divided into three groups. As mentioned in the previous sections, the GNSS boards inside the smartphones reported the CNR almost continuously (Figure 11a). The two Leica GS15 receivers, instead, stopped reporting their CNR when the jammer interference was too strong, and they gave confused reporting even when the jammer was distanced from them (Figure 11b). The two Leica GS18 receivers had a similar behavior, except that they did not stop reporting completely, but instead gave fewer reports (Figure 11c). Although the figures  
Figure 10: Examples of fitting the path of the jammer to the calculated points: (**a**) good compliance; and (**b**) bad compliance.  
show only a single satellite and a single jammer, the above findings can be considered as general.  
Figure 12 shows how various jammers affect different signals from various satellites. The BeiDou satellites transmitting on B1 are mostly affected by the jammer J1, J2 affects the signal much less, while J3 has barely any effect, only in close proximity and in a much weaker fashion. The E1 Galileo signal is mostly affected by J2, less by J1 and J3. However, on E5a, J2 and J3 have almost no effect. J1, on the other hand, does. The same conclusion holds for GPS, with L1 having a similar dependency to E1 and L5 to E5a. The GLONASS satellite signals behave in the same fashion as the BeiDou signals.  
Figure 11: _Cont._  
Figure 11: CNR dependence on the position of the jammer on the road (0 is the point closest to the receiver, a negative value means the jammer is approaching, and a positive value that it is distancing): (**a**) typical behavior for a smartphone receiver; (**b**) typical behavior for a Leica GS18; and (**c**) typical behavior for a Leica GS15. The color of the points represents the satellite elevation.  
Figure 12: Cont.
## 4 Discussion  
This work proposes the HoRFNet model, which integrates high-order modeling with a multi-branch receptive field mechanism and demonstrates notable advantages in BCHI classification. Experimental results show that the model achieves leading image-level and patient-level recognition performance across all four magnification levels in the BreakHis dataset, confirming its strong generalization and discriminative capabilities. It is important to note that BreakHis, as a widely used public dataset, provides strong representativeness and comparability, serving as a standardized evaluation platform for various histopathological image analysis methods. However, histopathological image of breast cancer may exhibit structural differences across countries, regions, climates, and population backgrounds, such as variations in tissue morphology, cell density, or staining conditions.  
Looking ahead, we expand the scope of our study by exploring the model's adaptability on multicenter datasets collected from different countries and regions. We also introduce data with diverse sources, staining styles, and preparation protocols to systematically evaluate the model's cross-domain robustness and generalization performance. In addition, we consider incorporating other publicly available BCHI datasets to comprehensively assess the model's performance across varying data environments and to further advance the practical deployment of HoRFNet in real-world clinical applications.  
Figure 10: Comparison of Grad-CAM visualization results between the Backbone (first row) and HoRFNet (second row) at different magnification levels. Compared with the Backbone, HoRFNet consistently focuses more accurately on tumor-related regions across all magnifications.
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
## 3 Asran
### Motivation  
The UAV network is flexible and highly scalable. However, UAV network lacks network transmission stability. We experienced network instability while experimenting with UAV network. We constructed a flying ad hoc network based on multiple UAVs and experimented to transmit data. The throughput measurement data is set to be transmitted at 1000 kbps, and no additional traffic was generated in the UAV network. The cumulative distribution function (CDF) of the measured throughput in this environment is shown in Figure 1. As shown in Figure 1, the rate at which the network maintains the maximum transmission rate is less than 50% of total transmission time. In addition, the interval measured at 0 Kbps was 19% of the total transmission time because data transmission was impossible. In other words, transmission via UAV network has also been temporarily disconnected.  
As shown in our previous experiments, transient link instability occurs frequently in UAV networks. In other words, in a UAV network environment, data loss can occur frequently without buffer overflow due to heavy traffic. To cope with UAV network environment, we tried to improve the throughput of UAV network by improving TCP. Since the original TCP assumes that data loss is caused by buffer overflow due to heavy traffic, TCP reduces throughput to reduce network traffic and avoid congestion. However, since the transient link instability that occurs in a UAV network is not caused by heavy traffic, it is unnecessary to reduce the throughput. Therefore, we propose an _ASRAN_ that improves TCP to UAV network. _ASRAN_ operates by distinguishing transient link instability from buffer overflow due to heavy traffic. With this distinction, _ASRAN_ quickly recover throughput in case of transient link instability and acts as on original TCP to avoid congestion in case of heavy traffic.
## 2 Experimental Test Scheme
### PDPA Test Experimental System  
The PDPA test system mainly comprises a PDPA tester (TSI corporation, 500 Cardigan Road, Shoreview, MN 55126, USA), a high-pressure spray system, and a closed spray field system, which is shown in Figure 3.  
Figure 1: Basic parameters of the dust suppression solution.  
Figure 2: Schematic diagram of the internal structure of the nozzle and the characteristics of the fog field.  
The PDPA tester is composed of two solid-state lasers that generate three groups of lasers with different frequencies [29]. These lasers are dark green (532 nm), yellow (561 nm), and light green (553 nm). The tester employs a laser beam to irradiate the droplets in the droplet flow and captures parameter information such as the droplet size and velocity of the droplets by detecting the reflection of the droplets on the laser beam and the scattered light signals. After receiving the feedback signal, the transmitting device feeds back the signal source to the receiving sensor of the device and uploads the signal to the data system. This process measures the three-dimensional droplet velocity and size of the dust suppression solution in the spray field in a non-contact manner [30; 31; 32; 33]. The basic parameters of the PDPA tester are illustrated in Figure 3.  
To reduce the influence of gravity and wind flow on the atomization effect, the nozzles were arranged vertically on the ground. The fog field measurement coordinate system was established with the nozzle as the origin, as shown in Figure 4. In the coordinate system, the Z-axis was perpendicular to the ground, and the X-axis was parallel to the ground. The Z-axis measurement point was calculated from the origin, and the step distance was 20 mm. The X-axis measurement point was centered on the Z-axis origin, and the step distance was 20 mm. To obtain a stable amount of data and ensure the reliability of later data processing, the amount of data collected at each measuring point was not less than 10,000 [34]. The location of the fog field measuring point was represented by the black point in Figure 4.  
Figure 4: Spray structure and measuring point layout.  
Figure 3: PDPA test experimental system.  
Diameter Count is the drop count; Diameter is the droplet size; Time is the instantaneous time when the system collects the droplets; \(Vx\) is the velocity value in the X-axis direction.  
During the experimental process, the prepared solution was sprayed into the PDPA experimental chamber through a spray system to form a stable fog field. The PDPA sampling points were adjusted to the origin according to the above-mentioned arrangement of the fog field for measurement, and the atomization parameters of each measuring point in the fog field were tested sequentially according to the step size.