esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Siamese-Network-Based Attack Detection for FDI Attacks on LFC
### Network Structure  
The structure of the proposed Siamese network for attack detection is depicted in Figure 2.  
The Siamese network consists of two symmetrical branches that share equal weights (EW). Sharing equal weights can effectively reduce the training parameters in the training  
Figure 2: The Siamese network for FDI attack detection.  
process. The structure of each branch consists of four full connect (FC) layers and rectified linear units (ReLUs). The function of FC layers is to map the original data to the hidden layer feature space. The output produced by the FC layer is the hidden feature of the sample data. We use many neurons to fit the features of the input data. Generally, single-layer FC does not have the ability of nonlinear expression. Therefore, we increase the number of layers of FC to enhance its nonlinear expression ability. In each FC layer, the number of elements is equal to the dimension of sample data. The ReLUs, which can alleviate the over fitting problem of the Siamese network, are defined as  
\[f(a)=\max(0,a) \tag{27}\]  
After that, the input data (measured data and observed data) are transformed into two sets of feature vectors. Then, the two sets of vectors are transmitted to the loss layer to quantify the distance.  
We input the observed data and the true data into the Siamese network as the input pair. When it is a positive sample, the calculated distance of the input pair should be smaller than that of the negative input pair. Due to the equal weights and ReLUs, the features caused by communication delay and noise can be mitigated during the training process.
## 4 Experimental Evaluation
### Index Building Time  
The efficiency of the LearnS approach is not only reflected in join search but also in the building time. Figure 6 shows the building time of mainstream index structures. As Quad needs to build six B+ tree indexes, its time cost is the highest. The Two-step approach adopts the combination of R*-tree and \(k\)-d trees. Due to its hash-based semantic representation, the space complexity of indexing is higher than LearnS. Hence, it needs more building time than LearnS. The Midas and Darq approaches are index-based and distributed retrieval systems. However, the former uses \(k\)-d tree as its main index structure, hence its index building time is significantly lower than the latter. Dams, compared to Midas, spends more time because it does hash partitioning RDF data into multiple slave nodes.  
Figure 4: Join query performance for various join query types on LUBM dataset.  
Figure 5: Join query performance for various join query types on LinkedGeoData dataset.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 3 LWM2M Proxy
### Register, Update and De-Register  
The registration procedure is performed by the Proxy on behalf of the Client. When a Client connects to the network, the Proxy sends a Register message to the Server (see Figure 4, step 1). Then, it also sends an Update message when a refresh of the registration is needed (see Figure 4, step 3). When the Client is no longer available, the Proxy sends a De-Register message (see Figure 4, step 5).  
Let us consider as an example the scenario depicted in Figure 1a: we have that the first Client registers with the first Server; the second and third Clients register with all three Servers; the fourth Client registers with the first and the third Server. This means that the Clients send nine messages just to register with the Servers they need to communicate with. Then, suppose that the registration lifetime is 3600 s: this means that each Client has to send an Update message every 3600 s. So, during a day, i.e., 24 h, a Client sends 24 Update messages to each Server it is registered with. Therefore, during a day the Clients send 216 messages only to refresh their registrations. Instead, when we introduce the Proxy (Figure 1b), the Register and the Update messages are sent by the Proxy on behalf of the Clients, reducing the number of messages in the constrained network and saving energy on the device.
## 5 Opportunities and Challenges  
The simultaneous use of multiple RANs within a unified communication system offers distinct advantages for autonomous ships navigating in the maritime environment with heterogeneous multiple RANs. This becomes especially pronounced during global navigation, where the secure connectivity to on-land entities assumes a pivotal role in monitoring vessel status, tracking location or enabling remote control. The significance of this connectivity is further underscored by the variations in network infrastructure capabilities across different countries. The 6G communication systems are poised to provide a decisive advantage by offering both terrestrial and non-terrestrial networks, serving as indispensable components for ensuring ubiquitous connectivity in the maritime communication landscape.  
However, it is important to note that legacy maritime communications, such as VHF-based communication, have endured well beyond legacy cellular communications. This longevity can be attributed to their extended communication coverage capability stemming from their frequency characteristics, despite their relatively low performance. This extended lifespan presents a unique set of challenges that demand meticulous consideration during the transition from traditional maritime communication methods to the cutting-edge realm of 6G communications. The latter enables continuous advancements in the concurrent utilization of multiple 3 GPP access networks over 5G and beyond systems, optimizing the distribution of data traffic for autonomous ships during their navigation in the ocean, in port areas, or on inland rivers.  
As the maritime sector embarks on this transformative journey, thoughtful planning and collaboration among relevant stakeholders, technology providers, and regulators will be essential to explore these opportunities and address challenges. In this section, we provide insightful exploration about opportunities and challenges for further research in this continually evolving landscape.
## 3 Result and Discussion
### Power Capacity Measurement  
This test is necessary to obtain the power consumption of the sensor node in the one-cycle CO\({}_{2}\) measurement process. In the one-cycle measurement process, sensor nodes perform three processes: initialization, CO\({}_{2}\) sensor reading, and CO\({}_{2}\) data transmitting. The measurements of current consumption, time consumption, and power consumption were conducted in three processes of CO\({}_{2}\) measurement to obtain the sensor-node energy capacity.
###### Abstract  
As a reusable adsorbent to remove lead from water, a peptide-based magnetic adsorbent incorporating lead-binding peptide was constructed. First, a 7-mer lead-binding peptide (TNTLSNN) was covalently bonded onto the surface of a magnetic bead. Compared to the adsorption capacity of a bare magnetic bead (4.0 mg lead/g bead), the peptide-linked bead exhibited a capacity more than eight times higher than that of a bare bead (34.1 mg lead/g bead). The regenerated peptide bead, by desorbing the lead from the bead with EDTA, could be repeatedly used (tested over six cycles) for the following round of lead adsorption without any significant loss of adsorption capacity. The selective removal of lead in the presence of other interfering metals was demonstrated with the individual or the combinatory use of four metal ions, namely Pb(II), Ni(II), Co(II), and Cu(II), where the amount of adsorbed Pb(II) was remarkably higher than those of the other metal ions. The adsorption isotherm followed the Langmuir model well, with the maximum adsorption loading (qmax) of 70.4 mg lead/g bead.  
lead-binding peptide; magnetic bead; reusable adsorbent; Langmuir isotherm +
Footnote †: journal: Applied Sciences
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.2.2 Control Signaling
##### 9.1.2.2.1 Dedicated Common Control Channel  
Several studies [60] that use a dedicated common control channel [61, 62] usually assume its availability without providing details about how such a channel is secured. This fact alone indicates the need for securing the design of such a channel. Again, the most relevant work in this respect is [63], which proposed the design of an out-of-band CCC using the guard bands between the channels of the licensed spectrum. This design was achieved by carefully selecting relevant subcarrier parameters such as transmit power, bandwidth and the maximum possible number based on the constraints of OFDM technology and the permissible levels of spectral overlap with the PU transmission. In this scheme, the CR nodes are also able to selectively activate guard bands based on the local observed PU activity. However, the implementation of OFDM technology that was originally developed for wideband digital communication on the platform of single-transceiver CRSN communication is still an open issue.
## 4 Review: Slant Links Rain Fade Models
### Limitations
#### 4.1.8 Unified Model  
This [89] model uses a \"complete rain distribution\" based on several non-linear rain regressions using several attenuations and rainfall databanks. This model proposes an \"effective path length\" determination procedure for the terrestrial and slant links in a common way; hence it is called \"unified.\" It was first designed for the terrestrial links and later added to the slant communication links. The rain attenuation is given by Equation (27):  
\[A_{p}=k\Big{[}R_{eff}(R_{p},L_{S},\theta)\Big{]}^{\alpha}\frac{L_{S}}{1+L_{S} \cos\theta/L_{0}} \tag{27}\]  
where \(L_{S}\) and \(R_{eff}(R_{p},L_{S},\theta)\) are given by Equations (28) and (29):  
\[L_{S}=(H_{R}-H_{S})/\sin\theta \tag{28}\]  
\[R_{eff}(R_{p},L_{S},\theta)=1.74R^{0.786+0.197/L_{S}\cos\theta}\bigg{(}\cos \theta+\frac{120}{L_{S}^{2.88}}R^{-0.186}\sin\theta\bigg{)} \tag{29}\]  
The working procedure of this model is shown in Algorithm 8.  
```
1\(\gamma_{R}\gets k\cdot R_{0.01}^{a}\) /*\(L_{\mathit{eff}}\) computing start here */
2 while\(|\varphi|\leq 20^{0}\)do
3\(H_{R}\gets 3.0+0.28\cdot\varphi\)
4 if\(\theta\geq 5^{5}\)then
5\(L_{S}\leftarrow(H_{R}-H_{S})/\sin\theta\)
6\(L_{G}\gets L_{S}\cdot cos\theta\)/ *\(L_{G}\) in km */ \(1\) \(r_{0.01}\leftarrow\frac{1}{1+0.78\sqrt{\frac{L_{G}}{f_{R}}-0.38\left(1-\mathrm{ e}^{-2L_{G}}\right)}}\) compute \(\zeta\leftarrow\tan^{-1}\left(\frac{H_{R}-H_{S}}{L_{G}r_{0.01}}\right)\)
7 if\(\zeta>\theta\)then
8\(L_{R}\leftarrow\frac{L_{G}r_{0.01}}{\cos\theta}\)
9 else
10\(L_{R}\gets L_{S}\)
11 else
12 Invalid for tropical region
13
14 Calculate \(\chi\gets 36^{\circ}-|\varphi|\) /* In tropical \(\varphi<36^{\circ}\) */ \(v_{0.01}\leftarrow\frac{1}{1+\sqrt{\sin\theta}\left(31\left(1-\mathrm{e}^{-( \theta/(1+\chi))}\right)\right)\frac{\sqrt{L_{G}r_{G}}}{f^{2}}-0.45}\)
15\(L_{\mathit{eff}}\gets L_{S}\cdot v_{0.01}\) // \(L_{\mathit{eff}}\) computing ends here
16 while\(40^{\circ}\leq\theta\leq 60^{\circ}\)do
17 if\(\theta=60^{\circ}\)then
18\(C_{f}\gets 1\)
19 else
20\(C_{f}\leftarrow-0.002\theta^{2}+0.175\theta-2.3\)
21
22\(A_{B}\gets kR^{a}B_{\mathit{eff}}C_{f}\)
23 Compute \(A_{p}\) /* Equation (24) */ Return \(A_{p}\)
```  
**Algorithm 7**Breakpoint Model [29]  
```
1 Choose rainfall rate, \(R_{p}\)
2 Compute \(L_{S}\) /* Equation (28) */
3 Compute \(R_{\mathrm{eff}}\) /* Equation (29) */
4 Compute \(A_{p}\) /* Equation (27) */
5 Return \(A_{p}\)
```  
**Algorithm 8**Unified Model [89]  
Advantages  
The presented results show that the model outperforms ITU-R P.618-12 [108].  
Limitations  
The model has not been verified with measured attenuation data.
## 3 Feature Selection and Decision
### Hybrid Feature Decision
The total mean was computed as  
\[\bar{x}=\frac{1}{n}\sum_{k=1}^{m}\sum_{i=1}^{n_{k}}x_{ik}, \tag{4}\]  
The sum of the mean squared deviation (MSD) within groups was given as  
\[\mathcal{S}_{W}=\frac{1}{n}\sum_{k=1}^{m}\sum_{i=1}^{n_{k}}(x_{ik}-\bar{x}_{k})^ {2} \tag{5}\]  
The sum of the MSD between groups was computed by  
\[\mathcal{S}_{B}=\frac{1}{n}\sum_{k=1}^{m}\sum_{i=1}^{n_{k}}(\bar{x}_{k}-\bar{x })^{2} \tag{6}\]  
Hence, we obtained the F-score as  
\[\mathcal{F}_{S}=\frac{\mathcal{S}_{B}/(m-1)}{\mathcal{S}_{W}/(n-m)} \tag{7}\]  
```
1:Procedure F-TEST(X, Y): training data set
2:return \((\mathrm{w}_{f})\) that produces weighted feature vectors using F-test
3:select \((\mathrm{w}_{f})\geq\mathrm{threshold}\)
4:EndProcedure
5:Procedure RNCA(X, Y): divided data set into 5 folds
6:for\(i=1,n\)do
7:\(\lambda_{i,k}\): tuning using 5-fold cross-validation
8:for\(k=1,5\)do
9: call NCA(\(D\),\(\lambda_{i,k}\)): train NCA for \(\lambda\)
10: compute \(\mathcal{L}_{i,k}\): record loss values
11:endfor
12:endfor
13:\(\mathcal{C}_{\mu}=\mathrm{mean}(\mathcal{C}_{k,i})\): calculate average costvalue
14:\(\lambda_{b}=\mathrm{arg\,min}_{\mathcal{C}_{\mu}}(\mathrm{y}|\mathrm{x}, \lambda_{k,i},\mathcal{C}_{\mu})\): detect best \(\lambda_{b}\)
15: call RNCA(\(\mathcal{D}\),\(\lambda\), \(\bar{\mathcal{C}}\)): \(\bar{\mathcal{C}}=\bar{\mathcal{C}}(\mathrm{y}_{k},\mathrm{y}_{j})1-\mathrm{ exp}(-|\mathrm{y}_{k}-\mathrm{y}_{j}|)\)
16: return \((\mathrm{w}_{r})\) weighted features using RNCA
17:select \((\mathrm{w}_{r})\geq\mathrm{threshold}\)
18:EndProcedure
19:Procedure AIC
20: call \(\mathrm{GP}\ (\mathbf{X},\mathbf{Y})\): return \(\mathcal{M}\) a maximum likelihood
21: call AIC \((\mathcal{M},\mathcal{W}):\mathcal{W}\) is the number of \((\mathrm{w}_{f},\mathrm{w}_{r})\)
22: decide \((\mathrm{w}_{b})\): the best feature subset
23:EndProcedure
```  
**Algorithm 1** Hybrid F-test and RNCA using AIC based on GP algorithm. The F-test accepts an alternative hypothesis if \(p<0.05\). This indicates a significant difference in a feature between the two groups. When \(p\geq 0.05\), the null hypothesis is accepted, and the alternative hypothesis is rejected. This indicates that there is no significant difference in a feature between the two groups. The smaller the \(p\)-value, the more significant the difference in a feature between the two groups, making it more beneficial for estimating BPs. Therefore, a small \(p\)-value for the test statistic indicates the importance of a feature, as shown in Figure 4a,b. Table 4 lists the ranked features obtained using the F-test.
## 2 Materials and Methods
### Measurements
#### 2.2.2 Frequency-Domain  
Frequency-domain measures were analyzed using three primary frequency bands: very low-frequency (VLF: 0.0033-0.04 Hz), low-frequency (LF: 0.04-0.15 Hz), and high-frequency (HF: 0.15-0.40 Hz). The LF band represented both sympathetic and parasympathetic activity, while the HF band predominantly indicated parasympathetic activity. The LF/HF ratio was calculated to assess the balance between sympathetic and parasympathetic activity. This ratio provides a valuable measure of the sympathovagal balance, with higher values suggesting sympathetic dominance and lower values indicating parasympathetic dominance.
## 5 Discussion of Issues and Challenges  
With all the cases demonstrated above, issues and challenges of remote sensing from different sources can be identified as follows:  
1. Spatial resolution  
For satellite imagery, Landsat data, with its 30 m spatial resolutions, 250 m, are limited to delineate the boundary of the crop fields at the three locations. Sentinel-2 and Planet imagery, with higher spatial resolutions (10 m and 3 m, respectively), can delineate the boundary of the fields, and even be discriminate the features and variations within the fields. Therefore, satellite imagery may be used to complement when the UAVs miss some days or some part of the field area.  
2. Signal inconsistency  
Because of the atmospheric effect, the attenuation of reflectance signals to the remote sensors, and sensor factors, the remote sensing data from different sources may not be consistent in observing in order to characterize the same area of earth using NDVI or any other vegetation indices [29]. Landsat data are good for regional studies. At the field scale, 5-cm resolution UAV NDVI was more accurate than 10-m Sentinel-2 NDVI for measuring vineyard variability [43]. UAV-derived NDVI was found more effective in tracking forest canopy phenology than Landsat-derived NDVI [44].  
3. Cloud cover  
Cloud cover over study areas is an issue for the use of satellite imagery in the growth season, especially in the summer of the year in the humid study areas. Planet has high revisiting cycles, increasing the possibility to capture cloudless imagery. Sentinel-2 is acceptable in this aspect too, but Landsat may not guarantee provision of useable imagery from June to August in our study areas.  
4. Limits of UAV imaging sensors  
Although UAS imaging sensors can offer high-resolution products, the quality of the sensors varies greatly [45]. The satellite systems typically have been thoroughly calibrated into different levels of products. However, various commercially available UAV-based  
Figure 10: Satellite multispectral imagery of the research farm area at Pontotoc, Mississippi: Landsat 8 (25 July 2018), Sentinel 2A (22 July 2018), and PlanetScope 4-band (4 July 2018).  
multispectral NDVI capable sensors (cameras) may involve substantial geometric and radiometric errors, which may need further calibration and correction to improve. UAV imagery has high spatial resolutions. Therefore, even a small error in the viewing angle of a single lens could cause unacceptable geometric distortions between the NIR and red bands to produce biased NDVI.  
1. Limits of UAV imagery  
Even for a small field, a UAV imaging system needs to acquire multiple images during a period of time, then a mosaicking of the multiple images is needed to fully visualize the field, and the resulting image mosaic may contain unignorable geometric and radiometric mismatches due to unmatched features and projections between images and lapses of imaging time of different images.
## 4 Challenges and Future Research Areas
### Applicability of MIMO Antennas  
The applicability of multiple-input-multiple-output (MIMO) antennas that provide sufficient multiplexing gain and spectral efficiency in A2A and A2S links is limited, for many reasons. The lack of a scattering environment in the proximity of the airborne terminals is one reason why these antennas are only capable of offering minimal throughput, which makes them comparable with single-input-single-output (SISO) architectures. In addition, it is rather problematic to mount multiple antennas onboard small UAVs with sufficient element separation to improve the multiplexing gain. On the other hand, the exploitation of millimeter-wave carriers could be a solution to accommodate small-sized antenna arrays, albeit with the drawback of increased path loss. Furthermore, MIMO antennas require high power consumption, which is another limitation, especially for small-sized battery-powered UAVs, as well as the fact that the MIMO multiplexing gain can be further degraded because it is cumbersome to obtain precise channel-state information in time-variant airborne links, especially when terminals fly at lower altitudes.  
Both A2A and A2S links require clear LOS conditions to establish viable communication. In this regard, the application of beamforming techniques is very promising, especially when millimeter-wave frequencies are utilized. Therefore, convenient beam alignment would enable the Tx and Rx to select beams that maintain the maximum beamforming gain, thereby alleviating severe path loss and improving the channels' link budget. However, rapid and precise beam tracking is very intriguing in airborne links with extreme mobility. Instead of beamforming, the establishment of free-space optical (FSO) links is an alternative solution, which could provide reliable communication in both A2A and A2S scenarios, although their deployment requires further study, making it a very significant theme for future research.
## 4 Methods
### CWTS Generation  
In order to apply the continuous wavelet transform to obtain the more complete signal information scalogram as the input of CNN, we first used the sliding window strategy to maintain the real-time input in-vehicle sensor data, as is shown in Figure 2. The rectangular sliding window box with a fixed window size and sliding step was used to collect the data required for each anomaly detection. We set the window step to be shorter than the window size to ensure the detection sensitivity. As the sliding window moves toward the direction of time, new observations enter in and the previous exits. Every movement of the window represents an execution of detection.  
The in-vehicle sensor data in each detection window can be expressed as \(H(t)=\{h_{1},h_{2},...,h_{T}\}\), where \(T\) represents the length of the detection window. According to Equation (2), the real-time sensor data maintained in the detection windows can be decomposed using the continuous wavelet transform as:  
\[C(a,b)=\frac{1}{\sqrt{a}}\int_{-\infty}^{+\infty}H(t)\psi^{*}\left(\frac{t-b} {a}\right)dt. \tag{3}\]  
\(C(a,b)\) are the wavelet coefficients ranging from scale 1 to \(l\), where \(a=\{1,2,...,l\}\) and \(b=\{1,2,...,T\}\). Putting the wavelet coefficients into a \(T\times l\) matrix, the matrix can be regarded as a two-dimensional scalogram of in-vehicle sensor signals in the time-frequency domain.  
Figure 1: Visualization of anomaly scenarios. (**a**) is hijack, (**b**) is bias, (**c**) is injection, (**d**) is DOS, and (**e**) is replay.  
Figure 3 shows the time domain waveforms and CWTS of the normal and anomaly in-vehicle sensor signals. Each sensor signal has 512 data samples and is decomposed by a Morlet wavelet with the scale parameter set to 128. The red rectangles in the raw signal waveform represent the anomaly intervals. The horizontal axis of the CWTS represents the time resolution of signals, and the vertical axis is the scale parameter. Each element in the CWTS describes the magnitude of the wavelet coefficients via the brightness of color.  
In Figure 3, it can be observed that there are some obvious differences in the CWTS between the normal vehicle driving state and each scenario of anomalous vehicle behaviors. These distinctions indicate the possibility to identify normal or anomalous vehicle behaviors using CWTS. However, it is difficult to completely capture the explicit and precise information from the CWTS to make a distinction between the conditions of normal and anomalous vehicle behavior patterns. To overcome this problem, we utilized the CNN to automatically extract the features of the normal/anomalous behaviors from the CWTS  
Figure 2: The framework of the proposed anomaly detection model.  
for anomaly detection by taking advantage of its excellent capability of classification for two-dimensional data.