esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Selecting an Optimal Period for Discriminating Degraded Pastures  
This study employed a pasture mask specific to the Cerrado biome, prepared by the Image Processing and Geoprocessing Laboratory of the Federal University of Goias (LAPIG/UFG). The construction of this mask involved utilizing data acquired in 2020 by the MSI/Sentinel-2, supplemented by field information [1]. To align this mask with the spatial resolution of SuperDove and the specific year under consideration (2022), it underwent editing through visual interpretation using the Quantum GIS (QGIS) software (v3.36.3).  
To identify the most suitable period for discriminating between the five classes of pasture degradation under investigation in this study (NDP, LID, MID, SAD, and SBD), we initiated the analysis by examining color composites of SuperDove images captured during both the rainy and dry seasons of 2022. Then, the average seasonal reflectance of each class, as measured by SuperDove, was plotted against the DOY in 2022. To compute the average reflectance, pixels were randomly sampled within the plots visited in the field. In cases of vegetation heterogeneity, degraded classes were oversampled as needed. The Mahalanobis distance, calculated using the eight reflectance bands of SuperDove, was employed to assess the dissimilarity between pairs of classes. All possible class pairs were taken into consideration, encompassing even the most similar ones, such as LID and MID.
## 4 Results and Discussion
### Packet Loss and Link Reliability  
The results presented in previous sections outline the variance in latency across different network configurations, but they do not include packet loss. For these tests, a packet loss is considered to have occurred whenever a TCP packet requires retransmission. In the wired network configuration, no packet losses were observed in 70,000 command packets, which gives a loss rate of less than 1.43 \(\times\) 10\({}^{-5}\), or reliability of at least 99.9986%. When testing over University Wi-Fi, no packet losses were observed in 6100 samples, giving a loss rate of 1.64 \(\times\) 10\({}^{-4}\), or the equivalent of 99.984% reliability. A single packet loss was observed in 3346 samples of the mobile hotspot scenario, thus providing 99.97% reliability, and we observed three packet losses in total in 5000 samples measured during our transatlantic link tests, giving a 99.94% reliability or a better-than-6 \(\times\) 10\({}^{-4}\) packet drop rate. However, these results were measured end-to-end from the application layer, which would not indicate any packet loss or re-transmissions occurring at the link layer between the individual servers, routers, and switches located between the two application devices. While not directly measurable, their impact can be observed in the mobile hotspot tests as an increase in outliers and variance in the samples that are indicative of retransmissions in the wireless interface or within the cellular backhaul network.
## 3 Energy-Efficient Architectures
### Spatial Architectures: Fpgas and Asics
#### 3.2.2 Variable Bitwidth Accelerators  
A DNN can have over a hundred million parameters. Therefore, the memory required to store the data during computation is huge. One of the main techniques used to reduce the memory constraints is bitwidth reduction. Rather than expressing the numbers in IEEE 754 32-bit floating-point format, it is possible to represent them in fixed-point format [75], reducing the bitwidth as much as possible without affecting the accuracy significantly. This strategy allows not only to reduce the occupied memory but also to decrease the power consumption associated with computation [76; 77]. It has been demonstrated that most DNNs can be inferred using 8-bit fixed-point values without accuracy degradation [78; 79]. However, several studies [80; 81] have shown that each layer of a DNN has a different impact on the accuracy, and it is, therefore, possible to use a fine-grained quantization where the bitwidth of the weights and activations is different in each layer.  
To exploit not only the memory gain deriving from the bitwidth reduction but also the lower power consumption, hardware accelerators with flexible-bitwidth arithmetic have been developed (see Table 3 for a comparison). Stripes [82] implements variable bitwidth with bit-serial computation. The latency increases linearly with the bitwidth, but this increment can be compensated by heavier exploitation of the inherent parallelism of DNNs. Besides, multipliers, which are one of the most considerable sources of energy consumption excluding memory accesses, are no longer necessary. The bit-serial computation engine consists, in fact, of AND gates and adders only. Stripes is a hybrid architecture that fixes the bitwidth of the weights and provides flexibility for the activations. UNPU [83] has a very similar bit-serial computation engine, but the bitwidth of the activations is fixed to 16-bit while the weights have 1-bit to 16-bit flexibility. Loom [84] architecture is fully temporal since both weights and activations have variable bitwidth and are processed serially. To achieve this flexibility, Loom adopts bit-serial multiplication, that, however, requires the transposition of the inputs. For a more efficient implementation, Loom transposes the outputs rather than the inputs, but the overhead is not negligible. Bit Fusion [85] implements the flexible bitwidth spatially, with an array of PEs that are combined differently depending on the required bitwidth. In detail, the overall computation is partitioned in 2-bit \(\times\) 2-bit multiplications, followed by shifted additions. BitBlade [86] is based on the Bit Fusion accelerator, but it further optimizes the architecture eliminating the shift-add logic using bitwise summation.
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 4 Results
### Computing Platforms Used for Edge Computing  
Based on the analysis in the previous section, real-time object detection primarily utilizes edge computing to implement, so the computing platforms used for real-time edge computing are important. They can provide a reference for selecting hardware for subsequent studies. In the meantime, GPU-based computing platforms can provide more computing power for deep learning algorithms, which are popular for image processing. Table 4 lists three typical GPU-based computing platforms that are frequently used in edge computing applications in the selected literature.  
The Nvidia Jetson TX2 is a typical and the most used computing platform. The reason for this may come from the fact that it has a relatively balanced processing power, power consumption, and price. The widespread use of GPU-based computing platforms is also a side note to the development of artificial intelligence, especially deep learning, which has  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Computing** & & & **GPU-Based** \\
**Component** & & & \\ \hline Model & NVIDIA Jetson Nano & NVIDIA Jetson TX2 & NVIDIA Jetson Xavier NX \\ Physical Figure & & & & \\ CPU & ++ & ++ & ++ \\ GPU & + & ++ & ++ \\ Memory & + & + & ++ \\ Power & ++ & + & ++ \\ AI Performance & + & ++ & ++ \\ Price & ++ & ++ & ++++ \\ \multirow{2}{*}{Application} & Weed detection[67], & Powerline & \multirow{2}{*}{Vehicle detection[78], \multirow{2}{*}{Tree detection[76]}} \\  & Disease detection[63] & & detection[88], Disease & \\ \end{tabular}
\end{table}
Table 4: List of commonly used GPU-based computing platform examples. The number of symbols ‘+’ in the table represents the level of performance, with more ‘+’ indicating stronger performance. The critical parameters of (central processing unit (CPU), graphics processing unit (GPU), memory, power, and AI performance are compared. The price of these computing platforms is also listed for comparison. Some typical studies’ applications in the selected literature are also listed.  
become almost preferred in the field of image recognition, making GPU platforms more popular due to their parallel computing performance.  
In this review study, field programmable gate arrays (FPGA) are not used much, with only one mention in the literature [69], although it is also ideal for these edge computing applications due to its reprogramming ability and low-power characteristics. Some other GPU-based computing platforms have also been reported, such as the tensor processing unit (TPU), which is optimized for tensor computing [74], and the Intel Neural Compute Stick, which is based on a visual computing unit (VPU) [31; 75]. Some CPU-based computing platforms have also been reviewed, such as the Raspberry Pi series. Although Raspberry Pi is normally used as a comparison item in benchmark studies, it has also been used as the core computing platform in some studies. For example, an insulator detection study used a lightweight algorithm on a Raspberry Pi 4B to achieve real-time detection [87].
# Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System
## 5 Conclusions  
The ISP equipped in a POS results in a variable lever arm, making it difficult to compensate. Instead of inefficient manual measurement strategies, our proposed method uses the encoder data from ISP gimbals to decompose the dynamic lever arm into two constant lever arms to be estimated automatically. Based on the established 21-dimensional state Kalman filter, our error analysis and simulation tests provide some meaningful conclusions. First, rotation of the ISP and an attitude change of the vehicle are necessary to determine the dynamic lever arm. Second, encoder errors of the ISP have a limited effect on estimation accuracy, so that general turntables can be accepted if the lever arm \(L_{OG}^{b_{c}}\) is small enough. Third, precise time compensation is necessitated in both the lever arm estimation method and the manual measuring method. Last, the estimation accuracy of the dynamic lever arm mainly depends on the positioning accuracy of the CDGPS. Therefore, the proposed method can be used as an effective substitute for manual measurement with acceptable accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Error Combinations** & **Error Sources** \\ \hline
1 & Initial SINS errors & 1–15, 22–39 elements of state vector in (A3) \\
2 & Initial lever arm \(L_{OI}^{b}\) & 16–18 elements of state vector in (A3) \\
3 & Initial lever arm \(L_{OG}^{b_{c}}\) & 19–21 elements of state vector in (A3) \\
4 & Gyro random walk & Process noises \(\underline{\mathbf{v}}_{w}^{b}\) \\
5 & Accelerometer random walk & Process noises \(\overline{\mathbf{v}}_{w}^{b}\) \\
6 & Positioning errors of CDGPS & Measurement noises \(\overline{\mathbf{v}}_{gp}\) \\
7 & Encoder measuring noises & Measurement noises \(\mathbf{w}_{\mu_{c}}\) \\
8 & Time-asynchrony noise & Measurement noise \(\mathbf{w}_{dt}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error classification.  
Figure 8: The error distribution budgets. (**a**) The estimation error of the norm of the lever arm \(L_{OI}^{b}\); (**b**) The estimation error of the norm of the lever arm \(L_{OG}^{b}\); (**c**) The estimation error of the norm of positioning errors.  
**Author Contributions:** Conceptualization, Q.F.; Methodology, Q.F. and S.L.; Software Q.F. and Y.L.; Validation, Q.F., Y.L., Q.Z., and F.W.; Writing--Original Draft Preparation, Q.F.; Writing--Review & Editing, Q.F., S.L., Y.L., Q.Z., and F.W.; Supervision, S.L.; Funding Acquisition, Q.Z.  
**Funding:** This research was funded by the Aeronautical Science Fund of China (20165853041).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# New and Emerging Treatments for Generalized Pustular Psoriasis: Focus on IL-36 Receptor Inhibitors
## 4 Current Management of GPP
### Biologic Therapies  
In recent years, a better understanding of the pathophysiology of GPP has led to a shift in its treatment towards the greater use of biologists. These biologists have demonstratedhigh treatment efficacy, and in some cases, have fewer side effects due to their specificity of action [8; 24; 25].  
TNF is an important cytokine in the control and amplification of inflammatory pathways and is already used as a molecular target for the treatment of plaque psoriasis [24; 25]. Infliximab, etanercept, adalimumab, and certolizumab are known anti-TNF agents that have been shown to be effective and safe in the management of GPP, based on case reports and clinical trials conducted with a small sample of patients. However, in a few cases, paradoxical GPP developed during treatment with adalimumab, infliximab, and certolizumab [26; 28; 29].  
IL-17, a cytokine produced by T-helper 17 cells, contributes to the onset of psoriasis by activating keratinocytes, attracting neutrophils, and promoting neovascularization. IL-17A is the most prominent member of this cytokine family [24; 30]. Brodalumab, secukinumab, and ixekizumab target the IL-17 pathway: brodalumab by binding to IL-17 RA and secukinumab and ixekizumab by inhibiting IL-17A. Small clinical trials conducted with these therapeutic agents have shown good effectiveness in the treatment of GPP, along with a rapid onset of action and an excellent safety profile [30; 31; 32].  
IL-23 plays a crucial role in the development of plaque psoriasis by stimulating Th17 cells, promoting disease progression, and activating keratinocytes [25]. The biologics used to target this cytokine are guselkumab, Risankizumab, and ustekinumab (which also targets IL-12). Data on the use of Guselkumab and risankizumab for GPP are scarce, but a small clinical trial and some case reports have demonstrated their clinical efficacy and safety [24; 28; 33; 34]. Ustekinumab has not been assessed in clinical trials for patients with GPP; its effectiveness has only been reported in a few case reports [8; 24; 28].  
IL-1 is highly involved in the autoinflammation associated with GPP [25]. Canakinumab, gevokizumab, and anakinra are the available therapeutic options for targeting this pathway. Evidence of their use in patients with this condition is limited to a few case reports that have demonstrated encouraging results. However, clinical trials are needed to confirm their efficacy and safety as a viable therapy [24; 26].  
Despite none of these treatments being currently approved in Europe and the United States, Japan has approved the following therapies for GPP after small clinical trials: Bimekizumab [35], Adalimumab, Certolizumab pegol, Infliximab, Secukinumab, Ikekizumab, Brodalumab, Guselkumab, and Risankizumab [24].
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 3 System Modeling
### Energy-Consumption Model  
Since UAVs are limited by battery capacity, in the case of high energy consumption, UAVs may find it difficult to fulfill their tasks. Therefore, this subsection mainly describes the energy-consumption model of UAVs. The total energy consumption \(E_{m,n}(t)\) of UAV \(m\) providing services to user \(n\) can be expressed as the sum of the flight energy consumption and the hover energy consumption, as shown in Equation (25):  
\[E_{m,n}(t)=E_{\text{fly m,n}}(t)+E_{\text{hover m,n}}(t), \tag{25}\]  
Considering that the UAV is in a hovering state when providing computational resources for user requests, the hovering energy consumption \(E_{\text{hover m,n}}(t)\) of the UAV can be calculated as follows, where \(P_{\text{hover m}}(t)\) is the power consumption of the UAV when hovering, \(m_{\text{taav}}\) is the mass of the UAV, \(g\) is the gravitational acceleration, \(\rho_{\text{env}}\) is the air density in the marine environment, and \(A\) is the total swept area of the rotor blades of the UAV:  
\[P_{\text{hover}}(t)=\sqrt{\frac{(m_{\text{taav}}\cdot g)^{3}}{2\cdot\rho_{ \text{env}}\cdot A}}, \tag{26}\]  
\[E_{\text{hover m,n}}(t)=P_{\text{hover}}(t)\cdot\big{(}d_{\text{trans m,n}}(t)+d_{\text{comp m,n}}(t)\big{)}, \tag{27}\]  
According to the user position obtained from the user trajectory-prediction model in Section 4.1, after solving for the optimal position of the UAV, the flight energy consumption of the UAV moving to that position can be calculated by the following equation. Here, \(P_{\text{fly}}(t)\) is the UAV flight power consumption, and \(\beta\) is the air resistance experienced by the UAV in the marine environment:  
\[P_{\text{fly}}(t)=P_{\text{taav hover}}(t)+\beta\cdot d_{m}^{3}, \tag{28}\]  
\[E_{\text{fly}}(t)=P_{\text{fly}}(t)\cdot d_{\text{fly m,n}}(t), \tag{29}\]
## 3 Results and Discussion  
### Individual PAH Concentration at Different Agricultural Soils  
Table 2 indicates the concentrations of 16 PAHs at different land-use categories and comparison against other studies. The results revealed that individual PAH concentrations ranged from 0.08 to 546.97 \(\upmu\)g \(\pm\) 146.76 per Kg dry soil, with BghiP and Nap showing  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Children** & **Adults** & **References** \\ \hline Adsorption factor/ABS (no unit) & 0.13 & 0.13 \\ Adherence Factor/AF [mg/(cm\({}^{2}\)d)] & 0.2 & 0.07 \\ Average time/AT (d) & 25,550 & 25,550 \\ Body weight/BW (kg) & 15 & 70 \\ Carcinogenic slope factor/CSF\({}_{\text{fin}}\) [kg d)/mg] & 7.3 & 7.3 \\ Carcinogenic slope factor/CSF\({}_{\text{inh}}\) [kg d)/mg] & 3.85 & 3.85 \\ Carcinogenic slope factor/CSF\({}_{\text{derm}}\) [kg d)/mg] & 25 & 25 & [20] \\ Exposure duration/ED (y) & 6 & 24 \\ Exposure frequency/EF (d/y) & 180 & 180 \\ Exposure area/SA (cm\({}^{2}\)) & 2800 & 5700 \\ Ingestion rate/IR\({}_{\text{inh}}\) (mg/d) & 200 & 100 \\ Inhalation rate/IR\({}_{\text{inh}}\) (m\({}^{3}\)/d) & 10 & 20 \\ Particle emission factor/PEF (m\({}^{3}\)/kg) & 1.36 \(\times\) 10\({}^{9}\) & 1.36 \(\times\) 10\({}^{9}\) & \\ \hline Toxic equivalent factor/TEF (no unit) & Nap, Acy, Flu, Phe, Ace, Flu, Pyr & 0.001 & \\  & Ant, Chry, BghiP & 0.01 & [32] \\  & BbF, BkF, Ind & 0.1 & \\  & BaP, DBahaA & 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for the assessment of health risk.  
the lowest and highest values, respectively. Nap, Phe, BBF, and BKF exhibited higher concentrations (313.14 \(\upmu\)g/kg \(\pm\) 62.01 to 546.97 \(\upmu\)g/kg \(\pm\) 146.76), while the lowest concentrations were recorded for BghiP, DBahA, and Ind (1.25 \(\upmu\)g/kg \(\pm\) 1.10 to 5.74 \(\upmu\)g/kg \(\pm\) 3.77). Nap reached its peak concentration (546.97 \(\upmu\)g/kg \(\pm\) 146.76) in coastal areas, while Phe (352.14 \(\upmu\)g/kg \(\pm\) 64.71), BkF (321.18 \(\upmu\)g/kg \(\pm\) 55.74), and BbF (313.14 \(\upmu\)g/kg \(\pm\) 55.85) were higher in industrial areas. The sources of PAHs included biomass burning, smoking, and fuel combustion [28]. Farmers also burned their crop residue to reduce insect populations in agricultural fields, contributing to the production of low-molecular-weight PAHs. These findings align with [PERSON] et al. [21], where Nap showed the highest concentration, and higher levels of Phe and BKF were detected, while Ind, DBahA, and BghiP were not detected. The \(\sum\)16 PAHs in the research, rural, and local market areas were comparable to the findings of [PERSON] et al. [33] who analyzed PAHs in agricultural soils in Poland. Similarly, the PAH concentrations in coastal agricultural soils were consistent with those reported for agricultural soils in Sierra Leone [34]. However, the \(\sum\)16 PAH concentration in industrial soils was higher than in the other soils studied but was significantly lower (approximately half) compared to industrial soils in northwestern China [35].
# Measuring Torque and Temperature in a Rotating Shaft Using Commercial SAW Sensors
## 5 Conclusions  
As a general conclusion, it can be said that the COTS system fabricated by SENSEOR is adequate to measure temperature and strain/torque of a piece at rest--as long as there is flat area where the sensors can be glued on. If this is not the case, the surface of the shaft needs to be previously processed; alternatively, clamping the sensors or adding a ring or plate transducer may be considered. The development of a COTS solution that could be used to monitor a rotating piece would involve the following steps:  
1. Optimize the interrogation procedure in order to support higher acquisition rates, which would lead to a higher detail on the dynamics of rotational systems.
2. Increase the accuracy of the resonance frequency reading by using better control algorithms and, consequently, better interrogation algorithms, able to track the variation of the sensors resonance frequencies.
3. Give the user freedom to record the full range of measured frequencies at each interrogation cycle; this would give the user the freedom to post-process the frequency response and extract the resonance frequency with the desired accuracy.
4. Fabricate the SAW gage and temperature sensor dies without a W.FL connector. In this case, a small PCB board with the required W.FL connectors could be glued close to the sensors directly on the surface under probing. W.FL coaxial cables could be used to connect the coupler to the PCB. On the other side, the sensors could be electrically connected to the PCB by means of a short bond wire (for instance with a diameter of 25 \(\upmu\)m). This would minimize the centrifugal force and the influence of the rotation on the measurements.
5. Design and implement a mechanically stable RF coupler.  
This work was sponsored by Clean Sky project RTMGear 641593 and obtained financial support from FCT/MEC through national funds and when applicable co-funded by FEDER-PT2020 partnership agreement under the projects UID/EMS/00481/2013-FCT and CENTRO-01-0145-FEDER-02083. [PERSON] acknowledges the FCT for POPH (Programa Operacional Potential Humano) post-doctoral grant SFRH/BPD/90306/2012, co-financed by European (FSE--Fundo Social Europeu) and national (MCTES--Ministerio da Ciencia, Tecnologia E Ensino Superior) funds.  
[PERSON] and [PERSON] conceived, designed and assembled the mechanical setups. [PERSON] performed the experiments and analyzed the data under the supervision of [PERSON] and [PERSON]. [PERSON] wrote the paper. [PERSON] assisted with the results discussion.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 2 Materials and Methods  
### Materials  
NR was purchased from Elastomeros y Polimeros Sinteticos S.A. de C.V. (Tlalnepantla de Baz, Mexico). SWCNTs with an average diameter of 10 nm and lengths about 10-30 \(\upmu\)m were purchased from Bucky USA (Houston, TX, USA); they were used without any previous purification. CB DARCO G60 particles were obtained from ICI America Inc. (Wilmington, DE, USA). Stearic acid (95%), bis(benzothizyl) disulfide (MBTS, 99%), phenyl beta naphthyl amine (PBN, 97%), zinc oxide (97%), xylene (99%), and sulfur were purchased from Sigma Aldrich (St. Louis, MO, USA) and used as received. Three organic peroxides obtained from Akzo Nobel Chemicals (Dobbs Ferry, NY, USA) were used in this study: 1,1-bis(tert-butylperoxy)-3,3,5-trimethylcyclohexane (T29), dicumyl peroxide (91%) (DCP), and 2,5-bis(tert-butylperoxy)-2,5-dimethyl-3-hexylne (T145). The chemical structure and some physical properties of the organic peroxides used in this work are shown in Figure 1 and Table 1, respectively.