esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of the State of the Art about Packet Analysis and Related Work
### DPI Common Challenges and Requirements  
Deep packet inspection is not only a powerful tool regarding network anomalies detection, but it may also be used in bandwidth management, advertisement and copyright content filtering [7]. However, it may be used with bad purposes, such as interfering within net neutrality [8] or allowing governmental surveillance [7] and privacy invasion [9]. Due to the nature of DPI, some limitations in its implementation arise some challenges, requiring, therefore, a study of the viability of its deployment.  
Regarding some of the most common challenges Deep Packet Inspection faces, as presented in Table 2, its implementation, in order to be effective, should consider the attributes of the hardware assigned to perform the inspection. Slow hardware or software may become the bottleneck in packet processing, compromising the results that could be achieved. To allow the implementation of these devices in high speed networks, existing studies focused on improving their performance with algorithms that integrates CPU and GPU [10]; hardware acceleration [11]; and new architecture for DPI devices [12].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Level** & **SPI** & **MPI** & **DPI** & **OSI Model** & **TCP/IP Model** \\ \hline
7 & & & & Application Layer & \\
6 & & & & Presentation Layer & Application Layer \\
5 & & & & Session Layer & \\
4 & & & & Transport Layer & Transport Layer \\
3 & & & & Network Layer & Network Layer \\
2 & & & & Data Link Layer & Data Link Layer \\
1 & & & & Physical Layer & Physical Layer \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different depths of packet inspection and the OSI and TCP/IP layer they encompass.  
The implementation of DPI devices may vary according to requirements specific to the technology in use. [PERSON] et al. [23] described the implementation of Deep Packet Inspection in a Software-Defined Network (SDN) and proposed a method for reducing the network load and improve the efficiency of the devices, significantly reducing the implementation cost.  
When deployed for network forensics, the inspection depends on a good localization of the network sensors. In turn, it depends on what information from the network is wanted and may vary from inbound or outbound interfaces, pre-Network Address Translation (NAT) or post-NAT traffic, behind or in front of a firewall, and so on, in relation to the nature of the data wished to be checked [24]. Encrypted traffic also poses a challenge to DPI and should be considered prior to its implementation. Deep packet inspection and encryption are discussed in more detail in Section 2.4.
# Monitoring of the Variation in Pore Sizes of Woven Geotextiles with Uniaxial Tensile Strain
## 3 Variation in POAs and Characteristic Pore Size
### Variation in POA Results  
POA is the ratio of open area to the total area of a woven slit-film geotextile. The open area and area of filaments correspond to the number of white and black pixels, respectively, in a binary image. Figure 4 shows that the POA of unstrained specimens varies in a wide range from 1.98% (geotextile code W150) to 6.10% (code W250). As uniaxial tensile strain increases, the POA is monitored and recorded at each 1% tensile strain.  
Figure 4 indicates a relative gentle variation trend under the low tensile deformation for each tested woven geotextile. Then the POA increases gradually in the late period of uniaxial tensile strain. For example, the percent open area of geotextile W120 increases by 22.3% with uniaxial tensile strain increasing to 6%. Then the POA grows by 35.1% when tensile strain ranges from 6% to 14%. For geotextiles W120 and W120, local failure occurs as the uniaxial tensile strain reaches 10%, and then their POAs grow rapidly until the tensile  
Figure 3: Binary images of: (**a**) Unstrained specimen for W120\({}^{\text{a}}\); (**b**) Corresponding area under strain of 8% for W120\({}^{\text{a}}\); (**c**) An unstrained specimen for W150\({}^{\text{c}}\); (**d**) Corresponding area under strain of 8% for W150\({}^{\text{c}}\).  
strain increases to 12%. The variation trend results exhibit less difference among five tested woven geotextiles.
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## 4 Discussion  
This study explored the phenological process of three subtropical longleaf pine forest sites with different soil water-holding capacities over nine years, which included phenological responses to prescribed fire and varying weather events. Our results supported our hypotheses, suggesting that soil water-holding capacity affected the growing season length. Low-intensity prescribed fire did not cause significant physiological stress on plant communities, and thus had a small effect on early spring phenology with short-term shifts of aboveground biomass; however, it may increase the sensitivity of early spring phenology to radiation and air temperature. Dynamics in water availability caused by periodic weather events affected the senescence mechanism of plant communities, and in this process air temperature, precipitation and radiation were found to be the main climatic drivers for vegetation carbon phenology. Restoration of precipitation after a short-term summer drought reduced the lagged effect of water stress (Appendix F, Figure A5), resulting in a positive phenological response in ecosystem productivity to precipitation restoration after short-term water stress. In addition, we found evidence of the adaptability of plant community photosynthesis to long-term precipitation dynamics which affect ecosystem phenological response (Figure 7) (Appendix A, Figure A1).
# Numerical Study on Track-Bridge Interaction of Integral Railway Rigid-Frame Bridge
## 5 Parameters Study
### Influence of the Arrangement Schemes of Adjacent Bridges  
Different arrangement schemes of the adjacent bridges have a great influence on the additional forces of rails, since the expansion length of the bridge inevitably changes. As a consequence, three representative calculation models, as shown in Figure 17, are described in this study in order to discuss and explore the most reasonable arrangement scheme of the adjacent span.  
Figure 16: Total additional stress of the rail: (**a**) under rising temperature; (**b**) under falling temperature.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Schemes**} & **Additional** & \multicolumn{2}{c}{**Additional**} & **Additional** & \multicolumn{2}{c}{**Total**} & \multirow{3}{*}{**Gap**} \\  & **Expansion Stress** & **Bending Stress** & **Braking Stress** & **Additional** & & **Stress of Rail** & **/mm** \\  & **of Rail** & **of Rail** & **of Rail** & **/MPa** & **/MPa** & **/MPa** & \\ \cline{2-8}  & **Min/Max** & **Min/Max** & **Min/Max** & **Min/Max** & **Min/Max** & **Max** \\ \hline
**WJ-8 fastener** & \(-103/109\) & \(-4.3/2.9\) & \(-8.5/5.9\) & \(-26.5/26.9\) & \(-55.6/36.8\) & \(-143.1/151.7\) & \(12.9\) \\
**Small resistance** & \(-46.9/48.1\) & \(-2.8/1.9\) & \(-5.6/3.8\) & \(-24.7/25.0\) & \(-44.5/33.0\) & \(-84.6/84.9\) & \(38.6\) \\
**Limit** & / & / & / & / & / & \(-92/92\) & \(70\) \\ \hline \hline \end{tabular} Note: Positive stress is tensile stress and negative stress is compressive stress.  
\end{table}
Table 5: Comparison of various additional stresses and strength checks with different fasteners.  
Under the effect of the beam temperature dropping, which is shown in Figure 18, the minimum/maximum stress of the rail on the middle integral rigid-frame bridge can be represented as: scheme III (\(-\)37.0 MPa/45.1 MPa)! scheme I (\(-\)37.6 MPa/48.1 MPa)! scheme II (\(-\)42.4 MPa/49.9 MPa). Under the action of the train braking, as shown in Figure 19 (condition II), the minimum /maximum stress of the rail can be represented as: scheme III (\(-\)33.0 MPa/19.3 MPa)! scheme II (\(-\)33.0 MPa/33.1 MPa)! scheme I (\(-\)44.5.0 MPa/33.0 MPa). As a whole, scheme III, adopting four simply supported bridges as adjacent bridges, can be deemed to be the best choice to reduce the additional forces on CWRs in this novel integral rigid-frame bridge.  
Figure 19: Additional braking forces of the rail for three different schemes.  
Figure 17: Three calculation models of the adjacent bridges with different arrangement schemes.
## 2 Related Work
### Limitations of LSTM  
Despite their successes, LSTM models are not without limitations, particularly in hyperparameter selection and long-term stock price forecasting. Research indicates that while LSTM models are robust for short- to medium-term predictions, their performance tends to degrade over longer time horizons. To address these limitations, various improvements and alternative approaches have been introduced.  
In recent years, swarm intelligence optimization algorithms, such as Particle Swarm Optimization (PSO) [48], Ant Colony Optimization (ACO) [49], and the Bat Algorithm (BA) [50], have been widely applied to optimize LSTM hyperparameters. These algorithms emulate collaborative behaviors in nature, enabling them to identify optimal hyperparameter configurations in multidimensional search spaces. For instance, [PERSON] et al. [51] proposed a CS-LSTM approach, using Cuckoo Search (CS) to optimize LSTM's learning rate, number of layers, and neuron counts, significantly enhancing LSTM's performance in stock price prediction. Similarly, [PERSON] and [PERSON] [52] employed ACO to optimize LSTM hyperparameters, demonstrating that the optimized LSTM outperformed traditional LSTM models in both prediction accuracy and convergence speed.  
In addition to improving LSTM architectures through optimization techniques, researchers have also proposed entirely new forecasting frameworks. Notably, the N-BEATS model [53] employs a pure deep learning architecture with backward and forward residual links to achieve highly accurate and interpretable univariate time series forecasting, without relying on recurrent or convolutional components.  
Furthermore, to address LSTM's limitations in long-term forecasting, many studies have explored integrating Transformer's self-attention mechanism. The self-attention mechanism allows Transformers to weigh different parts of the input sequence, effectively capturing long-term dependencies. [PERSON] et al. [54] proposed a hybrid LSTM-Transformer model that leverages LSTM for short-term dependencies and Transformer for long-term dependencies. Experimental results showed that this hybrid model achieved higher accuracy and stability in stock price prediction, particularly in long-term scenarios. Similar studies have also demonstrated that combining Transformer with LSTM can overcome the limitations of traditional LSTM models in handling long-term trends, thereby improving prediction accuracy [55].  
Significant progress has been made in enhancing LSTM models for financial time series forecasting by optimizing hyperparameters with swarm intelligence algorithms and integrating Transformer's self-attention mechanism. These advancements have yielded remarkable results, particularly in addressing LSTM's deficiencies in long-term forecasting.
# Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments
## 3 Design and Implementation of Video Transmission  
In response to the video transmission demands for unmanned driving [28; 29; 30], a video transmission scheme incorporating mine 5G super uplink, collaborative computing, and transmission strategies has been conceptualized and executed.
## 4 Validation and Evaluation
### Experimental Results and Analysis Based on Self-Collected Data Sets
#### 4.1.1 TX and RX at Different Distances, Activity Accuracy Results and Analysis  
When the human body is between TX and RX, varying the TX and RX spacing, the schematic diagram is shown in Figure 2 of Section 2. The recognition accuracy of each action is shown in Figure 9. In general, the closer the AP is to the computer, the more accurate the results obtained. This is because, as the communication distance shortens, the reception of the received WiFi signal is enhanced, thus providing a more reliable CSI feature extraction to capture the different actions of the human body. However, the results were not optimal when the distance was at 1 m. This is because the multipath effect is smaller when the distance decreases, but the perturbation caused by irregular limb movements also increases. From the experimental results, it can be seen that the experimental results were better than at other distances when the distance between the transmitter and receiver was 2 m. Therefore, the following experimental distance was set as 2 m between the transmitter and the receiver in this paper.  
1.2 Recognition Results and Analysis of Changing Human Position When the Distance between TX and RX Is Certain  
The fixed TX and RX spacing was 2 m, and the changing human position was 0 cm, 50 cm, 100 cm, and 150 cm, respectively. The schematic diagram is shown in Figure 3 of Section 2. The recognition of each action was accurate, as shown in Figure 10. The average recognition rate of the action decreased with increasing distance when the distance was greater than or equal to 50 cm. This is because the strength of the signal decreased as the distance increased. When the distance was less than or equal to 50 cm, the average motion recognition rate increased. When the interval was 0 cm, the average motion recognition rate decreased. Since the multipath effect became smaller when the distance decreased, but the interference caused by irregular limb movements also increased, for SM-TCNNET applications, a distance of about 20 cm was ideal.  
Figure 10: Graph of the accurate results of recognition activities for changing the position of the human body when the distance between TX and RX is certain.  
Figure 9: Activity accuracy results for TX and RX at different distances.
## 3 Results and Discussion
### Surface Structural Deformation  
#### 3.2.1 Structural Deformations Caused by a Single P Atom  
Dopants in different locations have varying effects on the surface structure and lead to deformation. In this study, only the bonds adjacent to the dopant atom, particularly the C-P, P-H, and C-H bonds, are studied in detail.  
Figure 5: The possible H sites around a PV complex.  
Figure 6: The lowest formation energies of PV and PVH complexes in each layer in (001) and (111) surface.  
In the case of doping a single P atom, there is a noticeable elongation of the surrounding C-P bonds, especially when P is present in the second layer, as shown in Table 2. As the number of P-doped C layer increases, the bond length deformation gradually decreases in both (111) diamonds and (001) diamonds. The bond's elongation in the site A of (001) surfaces is obvious than in the site B. The different surface structural environments between A and B generated by the reconstruction of (001) surfaces may be one of the influencing factors. When the original C-C bonds are symmetric and of equal length, the doped P atom does not change the symmetry and the two bond lengths remain the same after structural optimization. Comparing the structural changes with the formation energy, there is an obvious relationship between the bond length and formation energy that the greater the change in the bond length, the lower the formation energy of doping a single P atom. Because P has a larger size than C atom, doping P atom in diamond changes the structure around it. In the deeper layer, due to the constraints of surrounding structure, the deformation caused by doping is getting smaller, which puts the doping position in a compressed state and prevent the doping process. In contrast, due to the smaller constraint in the upper layer, the formation energy decreases with the relaxation of the structure. This conclusion does not apply in the following case: the bond's elongation in site A of (001) surface is greater than that in site B in the same layer, but the formation energy of site A is larger. Because of the 2 \(\times\) 1 reconstruction of (001) surface, the bonds around site B are stretched and bonds around site A are constricted. Since phosphorus atoms are larger than carbon atoms, doping Phosphorus in site B, although the deformation is relatively small, it can effectively reduce the expansive force.
# An FPGA-Based Performance Analysis of Hardware Caching Techniques for Blockchain Key-Value Database
## 4 FPGA Implementation Details of Cache Mapping  
Mapping is a technique or process that focuses on how the data from the main memory is brought to the cache memory. In other words, mapping explains how data are duplicated or mapped (from main memory to cache memory) when there is a cache miss. As discussed in the last section, we used three cache mapping techniques: (1) direct-mapping technique; (2) 2-way set-associative mapping technique; (3) 4-way set-associative mapping technique. These three techniques are briefly discussed below.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 4 Discussion
### Application
Since earthquake-triggered landsling correlates spatially with areas of ground weakened by shaking and since existing landslide deposits may be remobilised as debris flows, causing more damage, earthquake-triggered landslide maps were used as input for predictive monsoon-triggered landslide hazard maps [7]. A landscape-scale landslide density map could have been used in this process and would have the advantage of being homogeneous, the whole area having been imaged by a single satellite. Finally, landslide density could be used in directing field investigations such as those carried out by [PERSON] and [PERSON] [6], who targeted sites of potential landslide dams. Since landslide mappingwas incomplete when they began their investigation, it was necessary to add target sites while the investigations were ongoing. The initial list of target sites was determined at the start of their field investigation, 32 days after the Gorkha mainshock, meaning that SAR products could easily have been made available during this time. Although here we have focussed on earthquake-triggered landslides, SAR-based approaches have as much or more potential in identifying monsoon- or typhoon-triggered landslides. Since rainfall events are generally accompanied by cloudy weather conditions which obstruct optical mapping, a SAR-based classifier could be particularly advantageous. The ARIA team have released damage proxy maps for urban damage following typhoons in the USA, Tonga and Puerto Rico [20]. There are various factors, however, which could complicate the use of SAR products in mapping rainfall-triggered landslides. In the case of typhoons, one such complication would be the damage to vegetation caused by the typhoon, which would be likely to decrease coherence, possibly leading to false positives. SAR coherence methods may prove unable to distinguish between landslides and damaged vegetation, although combination with SAR amplitude or other predictors might ameliorate this. Monsoon-triggered landslides represent a different problem: since the trigger extends over several months, pairs of SAR images would contain both old and new landslides as well as reactivated or partially reactivated old landslides. The signals associated with each of these would be different, which might cause confusion, particularly if aggregating pixels containing several landslides triggered at different times. Landslides which pre-date a SAR image pair and are stable would be expected to have a higher coherence than new landslides and possibly higher than the surrounding vegetation.
# LoRa Resource Allocation Algorithm for Higher Data Rates
## 3 LRA Algorithm Framework
### Designed End Device (DED)  
Based on the proposed LC (Figure 1b), a customized end device is designed that comprises two Semtech SX1276 LoRa transceivers connected and controlled by a single processor to avoid data processing/sharing complexity. As shown in Figure 2, the processor is a Texas Instrument Tiva C series unit and is programmed using Energia IDE 1.8.11, a fork of Arduino IDE. Both LoRa modules are connected to a shared Synchronous Peripheral Interface (SPI) bus and are equipped with identical dipole antennas for enhanced transmission power. The LRA algorithm, which is elaborated in the next section, is performed on this processor and the resulting data packets are fed to the radios to be transmitted simultaneously.  
It is assumed that multiple SFs are used in the same RF channel, which are not assigned for any other link at the same time in the network to avoid co-channel interference. Figure 3 shows the hardware and software blocks of the DED with two LoRa transceiver modules used for the proposed LRA algorithm implementation. All the transceivers are configured to have the same LoRa parameters, except for SF. The transferred data are measured in bytes.  
To improve network capacity, the LRA algorithm improves the BR of a LoRa link and the goodput of the network by reducing the total data transmission time \(T_{tx}\) between EDs and GW, regardless of the application type. As shown in Figure 4, the physical layer utilizes the LoRa SF orthogonality for parallel data transmission by efficiently selecting multiple SFs. The proposed algorithm can act as a MAC layer that facilitates packet distribution and synchronization among the SFs.  
Figure 4: Proposed LoRa stack for the LRA algorithm.  
Figure 3: DED’s block diagram for the LRA algorithm implementation.  
Figure 2: Lab prototype of the DED.
## 5 Case Study
### The Main Prediction Results  
#### 5.3.1 The Prediction Results for Regional Wind Power  
The prediction results of several structures of the M2 GSNet are listed in Table 2. From Table 2, it is obvious that M2 GSNet is the model that performs best. Besides, methods which take consideration of the NWP are better than those that do not include NWP data. From the results, the prediction error of the M2 GSNet method is smaller than LSTM by over 2 percent in the fourth hour. This means it can reduce more than 50 MW prediction error for the whole cluster, which is vital progress for the operation center of the power grid.  
LSTNet is a kind of deep learning method that takes consideration of the spatiotemporal relationship of wind farms in the cluster. It is an improved version of spatiotemporal prediction model [17]. From Table 2, we can see that LSTNet is indeed better than the MLP, LSTM and ELM which do not consider the spatiotemporal relationship. However, the M2 GSNet method is better than the LSTNet due to its ability to extract the geographical location information feature.  
Even for the M2 GSNet, when using multi-task learning to predict the wind power of each wind farm, the results are better than predicting the regional wind power directly (w/o MT1) or predicting the wind power of each and summing them together (w/o MT2) which proves the effectiveness of multi-task learning. Besides, the result of using the cubic NWP windspeed in the multi-modal learning is better than the result of using NWP windspeed directly.