esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### The Self-Assembly Approach
#### 2.1.3 Preparation of **1**-MIPs  
Having identified **6**, **7** and **12** as suitable FMs from MM and NMR studies, the corresponding **1**-MIPs were prepared in T:FM ratios of 1:1, 1:2 and 1:4 in CH\({}_{3}\)CN and CHCl\({}_{3}\) as porogens and EGDMA and TRIM as crosslinkers. The T:FM ratios of 1:1 and 1:2 were chosen as per our MM and NMR results, showing the most favourable interaction between **1** and FMs **6**, **7** and **12** at these ratios. The 1:4 T:FM ratio is the most common ratio used in the literature to promote T-FM interactions and complex formation. The **1**-MIPs were prepared in acetonitrile and chloroform to determine the effect of porogen polarity on the binding capabilities of the MIPs. Similarly, the influence of the level of crosslinking on the binding efficiency of **1**-MIPs was also evaluated by employing di- (EGDMA) and tri-(TRIM) XLs.  
All 36 formulations of **1**-MIPs were prepared using 7 mL of porogen (~1 mmol FM + XL per 1 mL), with AIBN as the initiator, at 60 \({}^{\circ}\)C for 12 h. The resulting MIPs were ground with the fraction 32-63 \(\upmu\)m collected and Soxhlet extracted with 10% acetic acid in methanol, followed by pure methanol. Extraction was repeated until **1** could no longer be detected in the HPLC trace of the methanol extract.  
Subsequent reference to these **1**-MIP formulations follows the following codes: E and T for EGDMA and TRIM XLs, respectively; **6**, **7** and **12** for the corresponding FM, 1, 2 or 4 for 1:1, 1:2 and 1:4 T:FM ratios, respectively; and CH\({}_{3}\)CN or CHCl\({}_{3}\) for the porogens. Thus, E7\({}_{\text{-MIPCHCl3}}\) refers to a **1**-MIP prepared using EGDMA as XL and **7** as FM in a 1:1 T:FM ratio, with chloroform as the porogen.
## 3 Methodology
### Phenology Module Modification  
Vegetation phenology determines the start (SOS) and end (EOS) of the growing season. Accurate representation of phenology is critical for terrestrial biosphere models to simulate the temporal dynamics of biological processes on the land surface [49]. The phenology module used in the Biome-BGC was developed based on the empirical relationship between phenology and meteorological conditions. It defines SOS as the date when both the summed precipitation and the summed soil temperature are greater than the critical values, and defines EOS as the date after July 1 st where the average minimum temperature for the next 3 days is less than the annual average minimum temperature [22]. However, this module was found to have significantly delayed the SOS of vegetation  
Figure 2: The flow chart of this study. EC, eddy covariance; GPP, gross primary productivity.  
in alpine regions, resulting in a severe underestimation of GPP [21]. To this end, we proposed to incorporate remotely sensed phenology into the Biome-BGC model to better prescribe the phenological states within the model. Specifically, we first extracted the SOS and EOS of alpine grasslands on the TP from 1982 to 2015 based on the GIMMS NDVI dataset, and then we used the SOS and EOS extracted from the NDVI dataset to prescribe the phenological states of vegetation within the Biome-BGC model. Through this modification, the Biome-BGC can effectively overcome the limitations of its original phenology module.  
In order to extract phenological parameters from NDVI time series data, several methods have been proposed, such as the NDVI thresholds method [22; 50], fitting logistic functions [51], largest NDVI increase method [52], and delayed moving average method [53]. Among them, the dynamic threshold method [50] has been proven to be one of the simplest and most effective methods for the extraction of phenological parameters [54; 55]. Therefore, we also used this method to extract the SOS and EOS from the NDVI dataset. The ratio of NDVI is defined as  
\[NDVI_{ratio}=\frac{NDVI-NDVI_{min}}{NDVI_{max}-NDVI_{min}} \tag{1}\]  
where \(NDVI_{ratio}\) represents the output ratio, ranging from 0 to 1, \(NDVI\) represents the daily \(NDVI\), \(NDVI_{max}\) represents the annual maximum NDVI, and \(NDVI_{min}\) represents the annual minimum NDVI in the upward or downward direction, which corresponds to the calculation of SOS or EOS, respectively.  
Based on the \(NDVI_{ratio}\), the SOS and EOS are defined as the dates when the \(NDVI_{ratio}\) reaches the dynamic threshold of 0.2 in the upward and downward directions, respectively [55]. Accordingly, the length of growing season (LOS) can be defined as the difference between EOS and SOS.
## 3 Results and Discussion
### Microbiological Diversity
_Rhodospirillaceae_, detected at all three sites, is known to inhabit stagnant water bodies with a pH of around 5.5 and tends to increase in abundance as the acidity rises [93]. This family thrives in environments with rapid OM production, aligning with the high OM levels found in this study. The other abundant families detected at all three sites included _Acetobacteraceae_, _Clostridiaceae_, _Desulfobulbaceae_, _Hyphomicrobiaceae_, and _Sphingomonadaceae_. _Desulfobulbaceae_, part of the phylum _Thermodesulfobarteria_, are well-known sulfate-reducing bacteria (SRB) involved in sulfate transformation in bottom sediments, playing a crucial role in passive treatment systems like wetlands [87; 94]. The SRB phyla is known to mediate AMD-polluted waters, and it includes _Proteobacteria_, _Firmicutes_, _Thermodesulfobarteria_, and _Nitrospirae_. In summary, the 16S rRNA sequencing results indicate a rich diversity of microorganisms in the bottom sediment and rhizosphere, further illustrated by SEM images of the tertiary roots of _T. capensis_ (Figure 8). Figure 8: Scanning electron microscopy images of a diverse variety of microorganisms on the tertiary roots of _T. capensis._ (**A**) Small round organisms: _Microococcus_, long thin organisms: _Flavobacterium_; (**B**) Long rods: _Pseudomonas_, _Bacillus_, _E. coli_, SRB, _Mycobacterium_, shorter rods: Methanogens; (**C**) Long, big structures: Actinomycetes, small round organisms: _Microococcus_; (**D**) Filamentous, cable-like organism: Desulfobulbaceae.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 1 Introduction
The rest of the paper is structured as follows: in Section 2, we provide a brief introduction to the Siamese tracking algorithms, and Transformer tracking algorithms closely related to this paper in recent years. Next, in Section 3, we introduce the overall tracking process of SSTrack and the network structure of three sub-networks. In Section 4, extensive comparative experiments and ablation experiments are conducted on multiple datasets to evaluate SSTrack. Finally, Section 5 summarizes the conclusions and proposes plans for future research. Figure 1: Comparison with other advanced object tracking algorithms on GOT-10k and LaSOT benchmark.
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
## 4 The Problem  
As aforementioned, confirmed traffic in LoRaWAN may lead to retransmissions that progressively increase the SF values used by the EDs performing confirmed transmissions. Eventually, all such EDs will end up using SF12. This network condition, which we call the SF12 Well, results in an inefficient network operation due to the high frame ToA. The SF12 Well can lead to severely degraded network performance, even for a relatively low offered load.  
In this section, we characterize the SF12 Well problem by means of extensive simulations. We evaluate a diversity of scenarios in order to determine the conditions that cause and accelerate the SF12 Well. In addition, we illustrate the performance degradation produced by the SF12 Well. For the evaluation, we developed a simulator called AFLoRa on the basis of the FLoRa simulator.  
This section is divided into four parts. Section 4.1 presents the main features of the AFLoRa simulator. Section 4.2 provides the details of the scenarios considered in the evaluation, along with the simulation conditions assumed. Section 4.3 demonstrates the SF12 Well problem. Finally, we show and discuss a set of global performance results in Section 4.4.
# Springback Prediction of Free Bending Based on Experimental Method
## 3 Experimental Verification
### U-\(R\) Experiment  
\(U\)-\(R\) experiments were carried out on carbon steel Q235 and aluminum 6061. The uniform tube diameter was 32 mm, the tube wall thickness was 2 mm, and the feeding speed was 10 mm/s. The \(U\)-\(R\) experimental data are shown in Tables 1 and 2. The experimental data were fitted separately to obtain the \(U\)-\(R\) general curve, as shown in Figure 4a,b.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**U (mm)** & **2** & **2.5** & **3** & **4** & **6** & **10** & **15** \\ \hline
1 & 4998.16 & 1823.5 & 1167.2 & 619.85 & 312.8 & 155.11 & 97.32 \\
2 & 5137.7 & 1892.7 & 1172.7 & 624.5 & 317.22 & 155.11 & 94.42 \\
3 & 6096.1 & 1982.9 & 1255.2 & 619.85 & 315 & 154.03 & 94.83 \\
4 & 4806.3 & 2047.9 & 1212.6 & 621.39 & 313.9 & 161.88 & 95.64 \\
5 & 4097.2 & 1810.4 & 1172.7 & 626.06 & 321.77 & 157.3 & 64.42 \\
6 & 5317.7 & 1892.7 & 1161.8 & 613.76 & 319.48 & 159.56 & 96.23 \\
7 & 5433.3 & 1982.9 & 1151.1 & 613.76 & 322.93 & 158.42 & 95.23 \\
8 & 4715.6 & 1878.5 & 1178.2 & 612.26 & 319.48 & 151.92 & 94.83 \\
9 & 5206.9 & 1892.7 & 1195.1 & 612.26 & 319.9 & 156.19 & 93.63 \\
10 & 5680.4 & 1759.4 & 1200.9 & 615.27 & 319.48 & 147.88 & 93.23 \\ Average & 5148.936 & 1896.36 & 1186.75 & 617.896 & 318.196 & 155.74 & 91.978 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of Q235 \(U\)-\(R\) Relationship Experimental Data.  
According to the curve obtained by fitting, the mapping relationship between \(U\) and \(R\) was obtained and the \(U\)-\(R\) database was established. As can be seen from the curves above, when the bending die offset \(U\) increases, the tube bending radius \(R\) decreases. This rule was found in both \(U\)-\(R\) experiments. As we all know, carbon steel Q235 has better toughness than aluminum 6061. The carbon steel tube has a larger springback when the \(U\) value transits from 2 to 2.5, so when we carried out the \(U\)-\(R\) experiment of aluminum, we narrowed the interval to observe the springback phenomenon of aluminum with lower toughness in the free-bending process. In fact, the material still adhered the rule that \(R\) decreases as \(U\) increases, but the degree of springback varies according to the material. Next, the springback analysis was performed on the transition and bend sections based on the \(U\)-\(R\) database.
## 3 Model Description
### Detailed Structure of TCReC
#### 3.2.1 Data Pre-Processing  
Typically, the captured network data are divided into multiple data flows based on the five-tuple (source IP address, destination IP address, source port number, destination port number, and transport-layer protocol) information. The input size of TCReC is fixed, while the size of network flows varies greatly. In order to adapt the input size of TCReC, a fixed amount of continuous data packets is required. We use the concept of segmentation to label different parts of the data flows. The size of the segment is fixed. As reported in previous studies [40], most network flows in real-world scenarios contain only a small number of packets--often around five. Based on the previous studies and experimental comparison, we set the segment size to 10 packets to accommodate this characteristic while preserving more contextual information. When the length of the data flow is longer than a  
Figure 4: The structure of TCReC.  
segment, every 10 packets will be cut into a segment and input to TCReC for processing. When the data flow length is less than the segment size, or the remaining length is less than 10 packets, 0 will be used to fill. A total of 112 bytes are selected from each packet to represent the whole packet, comprising 56 bytes of the header and 56 bytes of the payload.  
This pre-processing method can effectively expand the number of input data and facilitate the model to fully learn the local characteristics of the data flow.
## 7 Experimental Results and Discussion
### Error Vector Magnitude  
Error Vector Magnitude is the most common performance metrics that is utilized for performance evaluation for this study item in 3 GPP. EVM determines the difference between the 'expected' value of the symbol in demodulated form to 'actual' value of the demodulated received symbol. EVM can be mathematically written as [5]:  
\[EVM\left(\%\right)=\sqrt{\frac{\frac{1}{M}\sum_{m=1}^{M}\mid S_{m}-S_{0,m} \mid^{2}}{\frac{1}{M}\sum_{m=1}^{M}\mid S_{m}\mid^{2}}} \tag{21}\]  
where \(M\) is the quantity of constellation symbols, \(S_{m}\) is the real symbol of the constellation associated with the symbol \"\(m\)\" and \(S_{0,m}\) is the real symbol associated with \(S_{m}\). The 3 GPP has set an EVM limit for 256 QAM to be 3.5% [74].  
In Figure 11(a), the Error Vector Magnitude EVM is represented by sweeping the RF input power. It is evident that MSA-DPD results in EVM reduction to <3% as compared to 5% obtained with GMP. The MSA-DPD has a slight improvement as compared to CPWL, but this is not the significant contribution, we expect to have similar improvement but with smaller complexity. In addition to this, DPD NN has a better performance as compared to MSA-DPD by 1%. It can be seen that DPD NN has a better performance as compared to MSA technique between 0 and 5 dB, however, MSA has overall better performance from \(-15\) to 0 dB as compared to all other techniques. Similarly, In Figure 11(b), EVM is reported for all the comparative methodologies employed for different flexible waveforms that has been used in 5G NR framework for 0 dBm RF input power. Clearly, MSA-DPD has almost similar performance as compared to CPWL knowing MSA reduces the complexity as compared to CPWL. It can be seen that MSA has a better reduction by 1% as compared to CPWL. When compared to NN, the NN has better performance as compared to MSA. It is clearly observable that NN has the best performance.
# A Wide-Range Four-Phase All-Digital DLL with De-Skew Circuit
## 3 Critical Circuit Description
### The Three-Stage Digitally Controlled Delay Line  
To achieve the de-skew circuit with a wide frequency range and high phase accuracy, the delay line is designed as a three-level structure, consisting of CDL, MDL and FDL cascaded. Figure 7 shows the structure and unit of sub-lines. The delay units of CDL and MDL are based on NAND gates to save power consumption [23]. To achieve a fine delay, the delay unit of FDL is composed of an inverter and a capacitor. The capacitor is implemented by a transmission gate (TG), which generates a small propagation delay difference by the difference in load effect between its on and off states [24], thus achieving a fine delay. The cascaded inverter in the fine delay unit is used to restore the driving capability of the delay path.  
Through the optimized design of the sub-lines to cover the frequency range required by the protocol, the adjustment range of MDL (\(T_{MDL,max}\)) should cover the unit delay of CDL (\(t_{CDU}\)) and the adjustment range of the FDL (\(T_{FDL,max}\)) should cover the unit delay of MDL (\(t_{MDU}\)), even in the case of PVT variations. The above relationships can be expressed as Equations (7) and (8):  
\[t_{CDU}\leq T_{MDL,max}=N_{MDU}\times t_{MDU} \tag{7}\]  
\[t_{MDU}\leq T_{FDL,max}=N_{FDU}\times t_{FDU} \tag{8}\]  
where \(N_{MDU}\) and \(N_{FDU}\) are the unit numbers of MDL and FDL, respectively, and \(t_{FDU}\) is the unit delay of FDL.  
The maximum delay of 3S-DCDL (\(T_{DCDL,max}\)) determines the minimum operating frequency of the de-skew circuit, and the finest unit delay determines the phase accuracy of the de-skew circuit (\(T_{step}\)). Thus, \(T_{DCDL,max}\) and \(T_{step}\) are given by Equations (9) and (10):  
\[T_{DCDL,max}= T_{DCDL,int}+N_{CDU}\times t_{CDU} \tag{9}\]  
\[T_{step}=t_{FDU} \tag{10}\]  
where \(N_{CDU}\) is the unit number of CDL, and \(T_{DCDL,int}\) is the intrinsic delay of 3S-DCDL. The maximum operating frequency (\(f_{max}\)) of the HDSC-based de-skew circuit is limited by  
Figure 7: The structure and delay unit of sub-lines. (**a**) CDL and MDL; (**b**) FDL.  
the CMOS process, and the minimum operating frequency (\(f_{min}\)) is shown in Equation (6). Therefore, on the premise of satisfying Equations (7) and (8), the proposed de-skew circuit can achieve a wider frequency range and higher phase accuracy by optimizing the parameters \(N_{CDU}\), \(t_{CDU}\) and \(t_{FDU}\).
## 4 Conclusions  
This paper presents a dataset of measurements performed over commercial 5G networks operating in the mid-band. To the best of our knowledge, this is the first 5G dataset collected and made available for operational 5G mid-band networks. The provided dataset contains power delay profiles, and related information, for measurements in outdoor-to-indoor scenarios, such as the number of paths, delay and power of each path, and total received power. Measurements originate from multiple campaigns and were collected at different locations. The dataset can serve as the basis for multiple analyses, including comprehensive channel propagation characterizations and modeling, based on real-deployed networks and standardized signals. The presented dataset can also be used for coverage evaluation of 5G communication systems deployed in urban environments. In general, measurements performed in dedicated laboratory setups do not always ensure a proper characterization and modeling of the channel propagation experienced in real-world scenarios. On the other hand, measurements performed on real deployed networks, such as the ones made available in the present dataset, can lead to channel characterization and modeling that better represent real-world scenarios. As a future work, we plan to adopt the same measurement setup to perform novel measurement campaigns on 5G networks, focusing on outdoor scenarios under mobility (e.g., user walking and driving). Hence, we also plan to make the corresponding dataset available in order to trigger further analysis of the propagation characteristics of mid-band 5G systems.  
Software, U.A.; conceptualization, methodology and data curation, U.A., G.C.; writing--original draft preparation, review and editing, U.A., G.C., L.D.N. and M.-G.D.B.; review and editing, K.K., M.R., O.A., M.N. and A.B.; project supervision, M.-G.D.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Sapienza University of Rome within research projects with Grants No. RP11816433F508D1, RP1916B88A04 AE6 and RP120172B6D62941.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset described in this study is openly available at Zenodo website: [[https://doi.org/10.5281/zenodo.6322868](https://doi.org/10.5281/zenodo.6322868)]([https://doi.org/10.5281/zenodo.6322868](https://doi.org/10.5281/zenodo.6322868)) (accessed on 5 February 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ID** & **Timestamp** & **Longitude** & **Latitude** & **PCI** & **SSB\_Idx** & **Power\_dBm** & **P\_total\_dBm** & **Delay\_us** \\ \hline
**1** & 12:00:25 AM & 12.494398 & 41.893274 & 174 & 0 & \(-119.55\) & \(-112.37\) & 8494.06 \\
**1** & 12:00:25 AM & 12.494398
## 2 Experimental Study on Adhesion Properties of Raw Coal
### Research Results and Discussion
#### 2.3.2 Influence of Moisture Contents on Raw Coal Adhesion Properties  
As a crucial parameter reflecting the adhesive properties of raw coal, the cohesion of raw coal represents the ultimate shear stress at the interface when the normal stress is zero. Figure 8 illustrates how the cohesion at the coal-coal and coal-wall interfaces changes with moisture content.  
The figure indicates that cohesion at both interfaces increases with rising moisture content. This trend is attributed to the fact that at lower moisture levels, water is primarily present as adsorbed water between coal particles. This adsorbed water forms liquid bridges and generates negative pressure at the interfaces, which together promote the agglomeration of coal particles. As moisture content increases, more liquid bridges form, enhancing the cohesion of raw coal. Additionally, the contact between coal particles and the bulker wall is more substantial than between coal particles themselves, resulting in the coal-coal interface having lower cohesion than the coal-wall interface under the same compaction force and moisture content.  
Figure 9 presents the variation in internal friction angles at the two interfaces with different moisture contents. It is observed that the internal friction angles increase with moisture content, but the rate of increase diminishes, indicating a decreasing influence of moisture content on the internal friction angle of raw coal. For example, at a compaction force of 300 kPa on the coal-wall interface, the internal friction angle increases by approximately 3.6\({}^{\circ}\) when moisture content goes from 0% to 1%. However, an increase in moisture content from 3% to 5% results in only about a 0.7\({}^{\circ}\) rise in the internal friction angle. At lower moisture levels, contact between coal particles is primarily through friction. As moisture content increases, adsorbed water fills the gaps between particles, leading to increased contact between smaller and larger particles and a consequent rise in the internal friction angle. Yet, as moisture content continues to grow, the lubricating effect of the adsorbed water between interfaces starts to reduce the impact on the internal friction angles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{p}\) & \(\mathbf{w}\) & \(\mathbf{1\%}\) & \(\mathbf{3\%}\) & \(\mathbf{5\%}\) \\ \hline
300 kPa & 30.15\({}^{\circ}\) & 33.76\({}^{\circ}\) & 36.14\({}^{\circ}\) & 36.87\({}^{\circ}\) \\
400 kPa & 33.44\({}^{\circ}\) & 37.02\({}^{\circ}\) & 39.60\({}^{\circ}\) & 40.78\({}^{\circ}\) \\
500 kPa & 35.38\({}^{\circ}\) & 39.16\({}^{\circ}\) & 41.25\({}^{\circ}\) & 42.43\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Internal Friction Angle for Coal-Wall Interface.  
Figure 8: Effect of moisture content on cohesion of raw coal.
## 4 Materials and Methods
### Transcriptome Analysis  
The raw RNA-seq data were filtered using Trimmomatic with the default parameters, and adapters in reads and low-quality reads were removed. The filtered clean reads were mapped to the kiwifruit reference genome 'Hongyang v3' [24] with HISATT2 [48] using the default settings. The gene annotation of the 'Hongyang' assembly was downloaded from the kiwifruit genome database ([[http://kiwifruitgenome.org/organism/5](http://kiwifruitgenome.org/organism/5)]([http://kiwifruitgenome.org/organism/5](http://kiwifruitgenome.org/organism/5))). Gene expression (fragments per kilobase of transcript per million fragments mapped) (19 October 2021) was calculated by Stringtie v2.1.4 [49]. Differential expression analysis was performed using edgeR, and differentially expressed genes (DEGs) between two samples were defined as genes with a \(p\) value \(<\) 0.01 and a Benjamin-Hochberg false discovery rate (FDR) \(<\) 0.05. In addition, to identify the transcription factors in kiwifruit, the coding sequences of annotated genes were mapped to PlantTFDB [50] using BLAST with a \(p\) value \(<\) 1 \(\times\) 10\({}^{-5}\).