esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
## 4 Results and Discussion
### Throughput  
A comparison of the throughput parameter of the proposed MBLCR and two existing methods, MP-OLSRv2, MEQSA-OLSRv2 and EMBLR, at various device speeds is depicted in Figure 5. From the figure, it can be seen that the MBLCR approach constantly delivers higher network throughput value than its counterpart routing approaches at all device speeds. The MBLCR approach attains higher throughput, indicating its superiority in selecting reliable and robust paths. The MBLCR reduces the flooding impacts of RREQ packets, resulting in a precise assessment of the ETX metric. Besides that, the MBLCR employs a CW size with the back-off increment factor \(\sigma\) that shortens the waiting time of devices in the optimal path selection, which allocates higher channel access and mitigates data packet collision among the devices. The MP-OLSRv2 exploits path recovery with loop detection methods, whereas the MEQSA-OLSRv2 exploits the network topology sensing with path computational methods for relay device selection in the path computational procedure. These methods require additional data packet transmission to establish the route, which leads to a decreased number of data packets being successfully delivered to the destination at a specific period. Meanwhile, the MBLCR employs the ETX metric, which is evaluated with the probe message prior to data packet transmission and mitigates the probability of link breakage and increases the network throughput. As the device speed increases from 10 m/s to 60 m/s, the network throughput decrements for all routing schemes. The network throughput of the MP-OLSRv2 decrements from 54.12 kbps to 44.56 kbps, the MEQSA-OLSRv2 decrements from 55.28 kbps to 47.21 kbps, the EMBLR decrements from 59.45 kbps to 50.64 kbps, and MBLCR decrements from 60.92 kbps to 51.47 kbps, when the device speed increases from 10 m/s to 60 m/s.
# Fast Conflict Detection for Multi-Dimensional Packet Filters
## 6 Conclusions  
Conflict detection ensures the correctness of packet classification and has received considerable attention in recent years. However, most conflict detection algorithms detect conflicts for only prefix fields. For greater practicality, the non-prefix fields should be considered. In this study, our proposed algorithm focuses on the conflict detection of non-prefix fields and is used either to extend an existing 2D conflict detection algorithm to support fast 5D conflict detection, or to increase the throughput of an existing 5D conflict detection algorithm. As the experimental data revealed, our proposed algorithm showed significantly better performance than the _FastDetect_ and SBV algorithms when detecting conflicts for filter databases of different sizes and types. Since the SBV algorithm can also filter out in advance those filter sets that do not conflict, the performance of our algorithm is clearly not better than the SBV algorithm when detecting some FW-type filter databases. However, the SBV algorithm requires additional running time and memory cost for additional auxiliary data structure; when the number of fields in the filter comparison increases or the update operations are frequent, these additional costs will affect the performance of the SBV algorithm. With our algorithm, the additional cost can be avoided because the memory demand is minimized.  
Conceptualization, C.-L.L. and Y.-C.C.; Data curation, G.-Y.L.; Formal analysis, G.-Y.L.; Investigation, G.-Y.L.; Methodology, G.-Y.L.; Project administration, C.-L.L. and Y.-C.C.; Resources, Y.-C.C.; Software, G.-Y.L.; Supervision, C.-L.L. and Y.-C.C.; Validation, G.-Y.L.; Writing--original draft preparation, G.-Y.L.; Writing--review and editing, C.-L.L. and Y.-C.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Databases** & **Number of Filters** & \
\begin{tabular}{c} **Total** \\ **Time (\(\mathbf{\mu}\)s)** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Build Time** \\ \end{tabular} & **Space (KB)** \\ \hline ACL1 & 19,912 & 104,103.40 & 5.23 & 3197.71 \\ ACL2 & 18,674 & 110,627.00 & 5.92 & 680.75 \\ ACL3 & 19,049 & 172,434.20 & 9.05 & 4625.60 \\ ACL4 & 19,221 & 166,306.80 & 8.65 & 5388.98 \\ ACL5 & 13,585 & 80,651.80 & 5.94 & 752.05 \\ FW1 & 18,643 & 190,337.60 & 10.21 & 1386.76 \\ FW2 & 19,254 & 180,186.80 & 9.36 & 214.29 \\ FW3 & 17,743 & 157,403.60 & 8.87 & 1183.20 \\ FW4 & 17,333 & 533,745.20 & 30.79 & 1711.34 \\ FW5 & 17,470 & 129,372.20 & 7.41 & 1141.49 \\ IPC1 & 19,477 & 117,780.00 & 6.05 & 2037.23 \\ IPC2 & 20,000 & 53,946.80 & 2.70 & 146.86 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Cost of the SBV algorithm to build auxiliary information with 20K filter databases.  
**Funding:** This research was funded by the Ministry of Science and Technology of Taiwan (MOST 104-2221-E-182-005 and 110-2221-E-182-010) and Chang Gung Memorial Hospital (BMRP 942).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# IoT System for Detecting the Condition of Rotating Machines Based on Acoustic Signals
## 2 Condition Detection Algorithm  
In order to develop a portable device capable of detecting the state of various rotary machines, it is necessary to develop an algorithm that considers the characteristic properties of acoustic signals obtained from such machines. Because rotary machines have characteristic acoustic and vibrational signatures, namely distinct peaks on typical frequencies and their higher harmonics, the problem becomes how to extract useful information in the frequency domain that can easily be adjusted to different characteristic frequencies of different machines.  
The algorithm for acoustic-based rotary machine state detection presented in this paper is named FASTER (fault and state detection of rotary machineries) algorithm. It was designed bearing in mind two main objectives: (i) it needed to be easily generalizable for a wide range of rotary machines with a fixed rotational frequency; and (ii) it needed to be computationally inexpensive so that it can be implemented in real-time at microprocessor platform with modest performance. In order to provide both seemingly contradictory requests, the algorithm needs to consider the characteristic rotary acoustic signatures, as well as hardware feature performance. As was initially proposed in [19], a combination of discrete wavelet transform and simple neural networks provides an ideal solution to this problem.
## 2 Geological Setting
### Geology of Fe-Ti-V Oxide Deposits
#### 2.2.2 The Udryn Fe-Ti-V Oxide Deposit and Other Prospects  
The Udryn deposit is located in the central part of the Suwalki Anorthosite Massif, approximately 4 km east of the Krzemianka deposit (Figure 1). Twelve boreholes were drilled in this region down to 2300 m [28]. The total area of the Udryn deposit is approximately 0.8 km\({}^{2}\). Magnetite-ilmenite mineralizations with a content of \(>\)15% Fe are considered ore materials. The shapes of the bodies of magnetite-ilmenite mineralization are similar to those found in the Krzemianka deposit, e.g., major lenses and schlieren, dipping generally towards the SE (Figure 3) [28]. The ore bodies in the Udryn deposit are variable in size, with lengths up to 0.8 km and widths up to 0.4 km. Their thicknesses vary from a few centimetres to 100 m. The ore bodies have sharp boundaries, and the boundary reaction zone is defined by the occurrence of volatile-rich minerals such as brown mica, apatite, and cummingtonite [10]. The deposit is also cut by faults and small fissures and the ore bodies have a complicated internal structure, with variations in their texture and composition, but they can be easily traced in the drills because of their large size [28]. This deposit has a similar composition to that of Krzemianka but contains more spinel minerals. In the Udryn deposit, massive ores account for approximately 7-8% of the Fe-Ti-V type mineralization and sub-economic resources are estimated at approximately 263 million tons of Fe-Ti-V ore [39].  
Figure 2: Schematic geological cross section (without Cenozoic sedimentary rock cover) of the Krzemianka Fe-Ti-V ore deposit in NE Poland (modified after [39]). Abbreviations: 1—anorthosite; 2—norrite; 3—ilmenite-magnetite ore; T1—Lower Triassic; P—Permian; Cm—Cambrian.  
The Jeleniewo prospect was recognized at the SW side of the SAM (Figure 1). This Fe-Ti mineralization was emplaced under leucogabronorites and anorthosites in the 1115-2300 m depth interval. Mineralizations occur in parallel lenses, running in a NW-SE direction and dipping to the SW. Massive Fe-Ti ores represent about 7-8% in volume of the boreholes' materials and their sub-economic resources are estimated at approximately 116 million tons [28].  
On the western margin of the SAM, in the Lopuchowo IG-1 borehole (Figure 1), interesting sulphide-oxide mineralizations containing REE-bearing fluorapatite in nelsonite dikes, were described for the first time in Poland [17].
## 3 Proposed Solution
### Architecture Proposed
#### 3.1.1 Starting a Trip  
At the beginning of the trip, the Vehicle Agent sends a route suggestion message to the Zone Agent. This message contains its id, the origin and destination of its trip. The Zone Agent forwards this message to the Route Guidance Agent. The RGA verifies if there are alternatives generated. If there are none, it generates a set of k shorter path (kSP) using a multithreaded algorithm [19]. With the set of kSP, the RGA sends the shortest route less suggested until that moment to the Zone Agent. The Zone Agent forwards the response to the VA, but first registers the id of the vehicle and the route that will  
Figure 1: (**a**) As soon as a Probe Car Agent crosses an intersection, it sends to the Cruise Agent the status of that street; (**b**) the Cruise Agent transmits to the Network Agent, who makes the prediction.  
follow. This information will be used to send alert messages when congestion is detected in the area (Figure 2).  
A similar exchange of messages is made between the Probe Car Agent, Zone Agent, and Route Guidance agent. The difference is that: when the Zone Agent forwards the route, it adds a list of Cruise Agents to which Probe Car Agent must report on the situation of the streets through which it crosses.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 5 Anthropometric and Morphological Approaches for HRTF Personalisation
### Regression
#### 5.3.1 Sparse Measurements
The approach extracts low-dimensional SH features from HRTFs across different frequencies, incorporating perceptual auditory characteristics like critical bands and loudness sensitivity. A deep learning model maps anthropometric measurements, such as head, torso, and ear dimensions, to SH coefficients. By using the HUTUBS HRTF database for training, this method predicts personalised HRTFs for all spatial directions through a single model, improving upon previous techniques that used separate models for each elevation. To summarize this section, Table 2 encapsulates all the parameters employed in the approaches discussed thus far, aiming to assist the reader in assessing which measurements from the CIPIC dataset have been considered effective by researchers in the personalised HRTF generation task.
## 3 Experiment and Analysis
### Temperature-Controlled Hyperthermia  
Through the focusing process of HIFU, the ultrasound beams with high intensity are focused in the center of the tumor to generate acoustic pressure field that demonstrates a good focusing effect. Long-term constant temperature hyperthermia can be divided into two heating stages. The first heating stage aims to quickly heat the human thigh tissue until the temperature of the tumor center is close to the target temperature. The second heating stage is constant temperature control realized by the combination of the heat conduction equation (as shown in Equation (2)) and the equation of temperature-controlled hyperthermia model (as shown in Equation (4)).  
The key of the second heating stage is dynamical modification of the heat source according to Equation (4) every 60 s. The core operator is set to multiple initial values suitable for different \(\Delta T\) based on the relationship and are inversely proportional (the product of \(m\) and \(\Delta T\) ranges from 8 \(\times\) 10\({}^{4}\) W/m\({}^{3}\) to 2 \(\times\) 10\({}^{5}\) W/m\({}^{3}\), which is also 0.005 to 0.01 times the amplitude of the heat source of the previous period). According to Equation (4), when the temperature of the tumor center exceeds the upper and lower limits of the target temperature, the operator will self-adjust by \(\alpha\) times that of the previous  
Figure 5: (**a**) The sound speed iteration of tumor region. (**b**) The average sound speed fluctuation of tumor region in the black rectangular box of (**a**).  
period. It will self-decrease when the temperature rises and exceeds the upper limit, and it will self-increase to prevent the area of tumor cooling down when temperature drops. The initial values of\(\alpha_{1}\) and \(\alpha_{2}\) in our study are both set to 0.01. Since the relationship between temperature and SOS in the different human tissue is available (temperature and SOS vary in direct proportion), the temperature detection in the last heating period is converted from the SOS value which is updated accordingly.  
We set target temperatures at 43 \({}^{\circ}\)C for each model and the temperature control curve of long-term constant temperature hyperthermia is illustrated in Figure 6. It can be observed that the temperature can be kept within \(\pm\)0.2 \({}^{\circ}\)C range of the target temperature with slight fluctuations for 5 h. Furthermore, we are also concerned with the temperature distribution in three-dimensional perspective during the whole heating process. We select model B in Figure 4b with the target temperature of 43 \({}^{\circ}\)C as an observation object,using COMSOL Multiphysics\({}^{\circledR}\), for simulation. As shown in Figure 7, the heat is mainly concentrated in the center of the tumor and there is almost no obvious heat deposition in other areas, which proves that our HIFU heating can achieve precise focusing on the tumor.  
Figure 6: Temperature control curve at tumor center for about 5 h’ HIFU heating with 43 \({}^{\circ}\)C target temperature.
# Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization
## 4 Experiment  
### Environment  
In this study, we employed a hardware and software setup consisting of a Raspberry Pi 4B (Raspberry Pi Foundation, Cambridge in England) running Ubuntu 20.04, a RB-SDA-V1 IMU sensor (ROBOR, Ansan in Republic of Korea), an GeForce RTX 3080 GPU (NVIDIA, Santa Clara in United States) with CUDA support, and a Canon EOS 60D DSLR camera equipped with an EF-S 18-55 mm IS STM lens (Canon, Tokyo in Japan). The hardware configuration was designed for the simultaneous collection of IMU sensor data and video data, attaching the Raspberry Pi and the IMU sensor to the DSLR. The Raspberry Pi utilized Ubuntu 20.04 as its operating system and communicated with the RB-SDA-V1 IMU sensor via a USB port (see Figure 5).  
Figure 4: The overall pipeline of the proposed cross-interaction-based hybrid full-frame video stabilization framework. (**Stage 1**) We first perform motion compensation using IMU sensor data. (**Stage 2**) Then, the motion compensated frames are estimated using the optical flow and these are input into a stabilization network to create stabilized frames. (**Stage 3**) Finally, multiple frames are fused and residual details are combined to generate the final rendered stabilized frames.  
The collected CSV sensor data were converted into GCSV format, which is recognizable by Gyroflow [21], to apply motion compensation to the original video data. As part of the preprocessing steps, the video resolution was resized from 1080p to 480p to accelerate the processing. Subsequently, we utilized the computing power of the NVIDIA RTX 3080 GPU to perform full-frame video stabilization, leveraging the PCA-flow-based video stabilization [8] and neural rendering [5] algorithms.
## 4 Results and Analysis
### Influence of Lateral Pressure Coefficient  
#### 4.2.1 Plastic Damage Zone of Roadway Sidewall  
Figure 13 illustrates the distribution of the tensile shear plastic area in the rock mass of a sidewall under varying \(\lambda\) values. As the \(\lambda\) increases, the extent of the tensile plastic zone at the roof and floor of the roadway initially decreases before eventually increasing. The shape of the failure zone in the surrounding rock transitions from an X shape to a V shape, with the failure zone of the roadway side consistently displaying a substantial range. Furthermore, the increase in \(\lambda\) results in noticeable changes in the plastic failure area of the roof, floor, and roadway sidewall. This underscores the significant role of the \(\lambda\) in influencing roadway stability. Therefore, it is crucial to conduct ground stress measurements and estimations during the roadway support design phase to implement effective support measures.  
The range of tensile and shear plastic areas in the middle of rock mass varies with \(\lambda\) value is illustrated in Figure 14. With the rise of \(\lambda\) value, the range of the tensile plastic area in a roadway sidewall's surrounding rock first decreases and then basically remains unchanged. The \(\lambda\) value has a quadratic polynomial relationship with the depth of the  
Figure 11: Vertical stress monitoring curves inside the sidewall of the roadway under different \(h/w\).  
Figure 12: Relationship between \(h/w\) and maximum vertical stress inside of the roadway sidewall: (**a**) First stress peak point inside of roadway sidewall; (**b**) Second stress peak point inside of roadway sidewall.  
tensile plastic area. With the rise of the \(\lambda\) value, the range of the shear plastic area of the surrounding rock of the sidewall first increases and then decreases. When \(\lambda\) increases from 0.1 to 0.3, the range of the shear plastic zone increases from 2.0 m to 2.75 m (maximum). When \(\lambda\) increases from 0.3 to 1.0, the depth of the shear plastic region decreases to 1.5 m.  
Figure 14: Scatter of the relationship between the \(\lambda\) and the depth of tensile plastic zone and shear zone of the roadway sidewall.  
Figure 13: Distribution of tensile and shear plastic zones in the roadway sidewall under different lateral pressure coefficients \(\lambda\) (green is tensile and red is shear): (**a**) \(\lambda\) = 0.1; (**b**) \(\lambda\) = 0.2; (**c**) \(\lambda\) = 0.3; (**d**) \(\lambda\) = 0.4; (**e**) \(\lambda\) = 0.5; (**f**) \(\lambda\) = 0.6; (**g**) \(\lambda\) = 0.7; (**h**) \(\lambda\) = 0.8; (**i**) \(\lambda\) = 0.9; (**j**) \(\lambda\) = 1.0.
## 4 Results and Discussion
### Reversely Variable Circuitry  
Based on the above analysis, the ideal circuitry of the FTHX should change the refrigerant flowpath between the evaporator and condenser modes flexibly, so that:  
1. [label=()]
2. the evaporator will hold four parallel circuits
3. the condenser will exhibit as few circuits as possible  
Therefore, the reversely variable circuitry was realized with two single-way valves and two distributors, as conceptually shown in Figure 6. The entire FTHX was divided into four elementary circuits with two distinct features. First, the FTHX was fed with a two-branch distributor and a three-branch distributor successively, rather than a four-branch one directly. Second, two single-way valves were added, one inside the vapor header, and the other between the two distributors. In this way, the FTHX exhibited four parallel circuits in the evaporator mode but 1.5 circuits (two circuits merging into one) in the condenser mode, as illustrated in Figure 7.  
The optimal flowpath for the FTHX in the condenser mode is the single-circuit one. However, the reverselyvariable circuitry with two single-way valves could achieve the 1.5-circuit flowpath at the least, similar to the single-circuit one in terms of heat transfer performance, as also illustrated in Figure 3. With three or more single-way valves, the single-circuit flowpath should be obtained in the reversely variable circuitry. However, that will also result in additional problems such as higher pressure drops and costs and is not suitable for other applications.  
It should be noted that the two-distributor scheme might lead to non-uniform feeding among the four circuits. In the evaporator mode, Branch B of the two-branch distributor should supply the refrigerant to the upper three circuits, while Branch A should distribute it only to the bottom one, as indicated in Figure 6. Hence, the parameters of the branch tubes and the manifold should be adjusted in the rated heating conditions. The diameters of Branch A, Branch B and the manifold were 3.36 mm, 5.6 mm and 8.12 mm, respectively. The length of the two branch tubes was also carefully modified. In this way, approximate uniform feeding was achieved for the four parallel circuits in the rated heating conditions.  
Figure 6: Overall heat transfer coefficient and temperature difference with a different number of circuits.
## 5 Test Results and Interpretation
### Results of Impact and Braking Load Tests
In detail, data from sensor L1 (placed on the bridge deck) allows the identification of the first two frequencies of the unloaded bridge (0.85 and 1.15 Hz), while that from sensor L2 (that was mounted on the downstream north pylon) permits the identification of the first and the third frequencies. As described previously, the horizontality of these lines indicates that the bridge response is elastic when subjected to accelerations due to the truck breaks; the latter are slightly lower than those experienced from the structure under truck impacts, but anyway higher than those measured during AVTs. Unfortunately, the longitudinal displacements of the deck due to the braking were not measured by the transducers that were mounted on the supports and on the isolator because the adopted sensors were not suitable for dynamic measures.
## 3 Experimental Results and Analysis
### Analysis of Experimental Results
#### 3.3.1 Comparisons of Insulator Target Detection Training Results  
The model parameters are set as follows: the number of iterations, the initial size of the learning rate, and the batch size are set to 150, 0.00005, and 8, respectively. Figure 5 shows the train loss curve, validation loss curve, smooth train loss curve, and validation loss curve, respectively, of faster R-CNN with VGG16 network as the backbone feature extraction network. These loss curves refer to the training loss curves of migration learning under the pre-training model of imagenet. After 126 epochs, the training loss converges to around 0.535, and the validation loss converges to around 0.716.  
In order to verify the effectiveness of Resnet50 and Resnet50+ECA-net, we compare the loss curve of faster R-CNN with Resnet50, and compare faster R-CNN with Resnet50+ECA-net with the loss curve of the original faster R-CNN with VGG16, respectively. Figures 6 and 7 show the train loss curve, validation loss curve, smooth train loss curve, and validation loss curve of the faster R-CNN with the Resnet50 backbone and the faster R-CNN with the Resnet50 backbone and ECA-net, respectively. In the faster R-CNN with the VGG16 backbone, when the number of epochs is 140, the train and val losses are 0.536 and 0.718, respectively. Compared with the faster R-CNN with the VGG16 backbone, the train loss of the faster R-CNN with the Resnet50 backbone and the faster R-CNN with the Resnet50 backbone and ECA-net decreases by 16.6% and 20.1%, respectively. The val loss of the faster R-CNN with the Resnet50 backbone and the faster R-CNN with the Resnet50 backbone and ECA-net decrease by 9.6% and 10.7%, compared to the faster R-CNN with the VGG16 backbone, respectively. Experimental results indicate that the addition of the Resnet50 backbone and ECA-net helps to reduce the loss value.